Prof. Mark A. Anastasio Profile

Prof. Mark A. Anastasio

Head of Bioengineering at Univ of Illinois

SPIE Involvement:

Conference Program Committee | Author | Special Event Speaker

Publications (219)

SPIE Journal Paper | 15 October 2024

Automated assessment of task-based performance of digital mammography and tomosynthesis systems using an anthropomorphic breast phantom and deep learning-based scoring

Andrey Makeev, Kaiyan Li, Mark Anastasio, Arthur Emig, Paul Jahnke, Stephen Glick

JMI, Vol. 12, Issue S1, S13005, (October 2024) https://doi.org/10.1117/12.10.1117/1.JMI.12.S1.S13005

KEYWORDS: Education and training, Breast, Digital breast tomosynthesis, Mammography, Imaging systems, Image quality, Data modeling, Signal detection, X-rays, Tomosynthesis

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PurposeConventional metrics used for assessing digital mammography (DM) and digital breast tomosynthesis (DBT) image quality, including noise, spatial resolution, and detective quantum efficiency, do not necessarily predict how well the system will perform in a clinical task. A number of existing phantom-based methods have their own limitations, such as unrealistic uniform backgrounds, subjective scoring using humans, and regular signal patterns unrepresentative of common clinical findings. We attempted to address this problem with a realistic breast phantom with random hydroxyapatite microcalcifications and semi-automated deep learning-based image scoring. Our goal was to develop a methodology for objective task-based assessment of image quality for tomosynthesis and DM systems, which includes an anthropomorphic phantom, a detection task (microcalcification clusters), and automated performance evaluation using a convolutional neural network.ApproachExperimental 2D and pseudo-3D mammograms of an anthropomorphic inkjet-printed breast phantom with inserted microcalcification clusters were collected on clinical mammography systems to train a signal-present/signal-absent image classifier based on Resnet-18 architecture. In a separate validation study using simulations, this Resnet-18 classifier was shown to approach the performance of an ideal observer. Microcalcification detection performance was evaluated as a function of four dose levels using receiver operating characteristic (ROC) analysis [i.e., area under the ROC curve (AUC)]. To demonstrate the use of this evaluation approach for assessing different technologies, the method was applied to two different mammography systems, as well as to mammograms with re-binned pixels emulating a lower-resolution X-ray detector.ResultsMicrocalcification detectability, as assessed by the deep learning classifier, was observed to vary with the exposure incident on the breast phantom for both DM and tomosynthesis. At full dose, experimental AUC was 0.96 (for DM) and 0.95 (for DBT), whereas at half dose, it dropped to 0.85 and 0.71, respectively. AUC performance on DM was significantly decreased with an effective larger pixel size obtained with re-binning. The task-based assessment approach also showed the superiority of a newer mammography system compared with an older system.ConclusionsAn objective task-based methodology for assessing the image quality of mammography and tomosynthesis systems is proposed. Possible uses for this tool could be quality control, acceptance, and constancy testing, assessing the safety and effectiveness of new technology for regulatory submissions, and system optimization. The results from this study showed that the proposed evaluation method using a deep learning model observer can track differences in microcalcification signal detectability with varied exposure conditions.

SPIE Journal Paper | 5 September 2024 Open Access

Investigating the use of signal detection information in supervised learning-based image denoising with consideration of task-shift

Kaiyan Li, Hua Li, Mark Anastasio

JMI, Vol. 11, Issue 05, 055501, (September 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.5.055501

KEYWORDS: Education and training, Signal detection, Denoising, Image quality, Machine learning, Binary data, Lithium, Image denoising, Signal attenuation, Covariance matrices

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PurposeRecently, learning-based denoising methods that incorporate task-relevant information into the training procedure have been developed to enhance the utility of the denoised images. However, this line of research is relatively new and underdeveloped, and some fundamental issues remain unexplored. Our purpose is to yield insights into general issues related to these task-informed methods. This includes understanding the impact of denoising on objective measures of image quality (IQ) when the specified task at inference time is different from that employed for model training, a phenomenon we refer to as “task-shift.”ApproachA virtual imaging test bed comprising a stylized computational model of a chest X-ray computed tomography imaging system was employed to enable a controlled and tractable study design. A canonical, fully supervised, convolutional neural network–based denoising method was purposely adopted to understand the underlying issues that may be relevant to a variety of applications and more advanced denoising or image reconstruction methods. Signal detection and signal detection-localization tasks under signal-known-statistically with background-known-statistically conditions were considered, and several distinct types of numerical observers were employed to compute estimates of the task performance. Studies were designed to reveal how a task-informed transfer-learning approach can influence the tradeoff between conventional and task-based measures of image quality within the context of the considered tasks. In addition, the impact of task-shift on these image quality measures was assessed.ResultsThe results indicated that certain tradeoffs can be achieved such that the resulting AUC value was significantly improved and the degradation of physical IQ measures was statistically insignificant. It was also observed that introducing task-shift degrades the task performance as expected. The degradation was significant when a relatively simple task was considered for network training and observer performance on a more complex one was assessed at inference time.ConclusionsThe presented results indicate that the task-informed training method can improve the observer performance while providing control over the tradeoff between traditional and task-based measures of image quality. The behavior of a task-informed model fine-tuning procedure was demonstrated, and the impact of task-shift on task-based image quality measures was investigated.

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SPIE Journal Paper | 27 July 2024 Open Access

Quantitative phase imaging techniques for measuring scattering properties of cells and tissues: a review–part II

Neha Goswami, Mark A. Anastasio, Gabriel Popescu

JBO, Vol. 29, Issue S2, S22714, (July 2024) https://doi.org/10.1117/12.10.1117/1.JBO.29.S2.S22714

KEYWORDS: Biological samples, Light sources and illumination, Scattering, Phase imaging, Red blood cells, Optical coherence tomography, Tomography, 3D metrology, Double positive medium, Autocorrelation

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SPIE Journal Paper | 18 July 2024 Open Access

Quantitative phase imaging techniques for measuring scattering properties of cells and tissues: a review—part I

Neha Goswami, Mark A. Anastasio, Gabriel Popescu

JBO, Vol. 29, Issue S2, S22713, (July 2024) https://doi.org/10.1117/12.10.1117/1.JBO.29.S2.S22713

KEYWORDS: Scattering, Tissues, Phase measurement, Phase imaging, Refractive index, Light scattering, Scatter measurement, Microscopy, Biological samples, Reflection

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Proceedings Article | 18 June 2024 Presentation

Machine learning-enabled quantitative phase imaging: my collaborations with Prof. Gabi Popescu

Mark Anastasio

Proceedings Volume PC12996, PC1299602 (2024) https://doi.org/10.1117/12.3022992

KEYWORDS: Phase imaging, Machine learning, Image segmentation, Image resolution, Fluorescence, Confocal microscopy

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SPIE Journal Paper | 6 June 2024 Open Access

Introducing the Special Issue Honoring Lihong V. Wang, Pioneer in Biomedical Optics

Xueding Wang, Mark Anastasio, Hao Zhang, Sava Sakadzic, Song Hu, Liang Gao

JBO, Vol. 29, Issue S1, S11500, (June 2024) https://doi.org/10.1117/12.10.1117/1.JBO.29.S1.S11500

KEYWORDS: Biomedical optics, Tissues, Photoacoustic tomography, Monte Carlo methods, Wavefronts, Ultrafast imaging, Tissue optics, Photoacoustic spectroscopy, Photoacoustic imaging, Modeling

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SPIE Journal Paper | 4 April 2024 Open Access

Deep-learning-based image super-resolution of an end-expandable optical fiber probe for application in esophageal cancer diagnostics

Xiaohui Zhang, Mimi Tan, Mansour Nabil, Richa Shukla, Shaleen Vasavada, Sharmila Anandasabapathy, Mark Anastasio, Elena Petrova

JBO, Vol. 29, Issue 04, 046001, (April 2024) https://doi.org/10.1117/12.10.1117/1.JBO.29.4.046001

KEYWORDS: Optical fibers, Lawrencium, Image quality, Super resolution, Endoscopy, Image restoration, Education and training, Cancer, Image enhancement, Image acquisition

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Proceedings Article | 2 April 2024 Poster + Paper

Transformer-based classifier with feature aggregation for cancer subtype classification on histopathological images

Chaojie Zhang, Zong Fan, Zhimin Wang, Lulu Sun, Yao Hao, Zhongwei Zhang, Wade Thorstad, Hiram Gay, Xiaowei Wang, Mark Anastasio, Hua Li

Proceedings Volume 12926, 1292633 (2024) https://doi.org/10.1117/12.3007045

KEYWORDS: Education and training, Cancer, Transformers, Performance modeling, Tumor growth modeling, Histopathology, Image classification, Tumors, Data modeling, Tissues

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Proceedings Article | 1 April 2024 Poster + Paper

Bayesian Cramér-Rao bound optimization of the illumination pattern in quantitative photoacoustic computed tomography

Evan Scope Crafts, Mark Anastasio, Umberto Villa

Proceedings Volume 12925, 129254A (2024) https://doi.org/10.1117/12.3005856

KEYWORDS: Photoacoustic tomography, Imaging systems, Absorption, Design, Inverse optics, Error analysis

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Proceedings Article | 1 April 2024 Poster + Paper

A learning-based method for compensating 3D-2D model mismatch in ring-array ultrasound computed tomography

Fu Li, Umberto Villa, Mark Anastasio

Proceedings Volume 12932, 129321E (2024) https://doi.org/10.1117/12.3006968

KEYWORDS: 3D modeling, 3D image reconstruction, 3D image processing, Data modeling, Transducers, Wave propagation, Image restoration, Data acquisition

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Ultrasound computed tomography (USCT) is an emerging imaging technique that holds great promise for breast cancer diagnosis and screening. Full-waveform inversion (FWI)-based image reconstruction methods can produce high spatial resolution images that depict the acoustic properties of soft tissues. However, FWI is computationally demanding, especially when wave physics is modeled in three dimensions (3D). A common USCT design employs a circular ring-array comprised of elevation-focused ultrasonic transducers, where the array is translated orthogonally to achieve volumetric (3D) imaging. This design allows efficient two-dimensional (2D) slice-byslice reconstruction methods to estimate a 3D volume by stacking reconstructed cross-sectional images at each ring-array position. However, these 2D methods do not account for the 3D wave propagation physics and the focusing properties of the transducers and thus can result in out-of-plane scattering-based artifacts and inaccuracies. Previous work has investigated a learning-based method, in which a deep neural network was used to map the 3D ring-array USCT data to idealized 2D USCT measurements, from which acoustic properties can be estimated by use of 2D FWI. The presented work extends that previous study in two ways. First, sophisticated anatomically realistic 3D numerical breast phantoms are used to construct clinically relevant training and testing sets. Second, a high-fidelity 3D wave propagation forward imaging model incorporating elevation focusing effects is used to virtually image the phantoms. The results show promise in improving image accuracy compared to both 3D FWI and conventional 2D FWI, achieving both mitigation of out-of-plane scattering and 3D-2D model mismatches and significant reduction of computational cost compared to 3D FWI. Additionally, the use of multiple ring-array measurements from adjacent elevations are explored as concurrent neural network inputs, showing improved accuracy compared to only using single-ring data.

Proceedings Article | 30 March 2024 Poster

Exploring a method to evaluate image-conditioned deep generative models for their capacity to reproduce domain-relevant spatial context

Rucha Deshpande, Mark Anastasio, Frank Brooks

Proceedings Volume 12929, 1292912 (2024) https://doi.org/10.1117/12.3006469

KEYWORDS: Particle filters, Reproducibility, Education and training, Biomedical optics, Visualization, Visual process modeling, Stochastic processes, Network architectures, Diagnostics, Design and modelling

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Proceedings Article | 30 March 2024 Presentation

Evaluating the capacity of a diffusion generative model to reproduce spatial context relevant to diagnostic imaging

Rucha Deshpande, Muzaffer Ozbey, Hua Li, Mark Anastasio, Frank Brooks

Proceedings Volume 12929, 129290I (2024) https://doi.org/10.1117/12.3006845

KEYWORDS: Diffusion, Diagnostics, Reproducibility, Anatomy, Performance modeling, Image quality, Stochastic processes, Gallium nitride

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SPIE Journal Paper | 20 March 2024 Open Access

Application of learned ideal observers for estimating task-based performance bounds for computed imaging systems

Kaiyan Li, Umberto Villa, Hua Li, Mark Anastasio

JMI, Vol. 11, Issue 02, 026002, (March 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.2.026002

KEYWORDS: Information operations, Signal detection, Image restoration, Binary data, Imaging systems, Education and training, Biomedical applications, Stochastic processes, Medical image reconstruction, Tomography

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PurposeThe performance of the ideal observer (IO) acting on imaging measurements has long been advocated as a figure-of-merit (FOM) to guide the optimization of imaging systems. For computed imaging systems, the performance of the IO acting on imaging measurements also sets an upper bound on task-performance that no image reconstruction method can transcend. As such, estimation of IO performance can provide valuable guidance when designing data-acquisition techniques by enabling the identification of designs that will not permit the reconstruction of diagnostically useful images for a specified task – no matter how advanced the reconstruction method is or plausible the reconstructed images appear. While such data space IO analyses are known conceptually, they have generally remained infeasible to widely implement. In this work, convolutional neural network (CNN) approximated IOs (CNN-IOs) are investigated for estimating the performance of data space IOs for the purpose of guiding hardware and data-acquisition designs and establishing task-based performance bounds for image reconstruction.ApproachNumerical studies that utilized a stylized breast X-ray computed tomography test bed are conducted to validate and demonstrate the approach. Signal-known-statistically and background-known-statistically (SKS/BKS) binary signal detection and discrimination tasks are addressed and the impact of the number of views and beam intensities on IO performance is investigated as a case study. The image space CNN-IO performance is also computed by use of images reconstructed by both U-Net and FBP reconstruction methods and compared to the corresponding data space CNN-IO performance to assess task-related information loss.ResultsFor all considered cases, task-performance bounds were established by use of the data space CNN-IO performance. A comparison of the data space and image space CNN-IO performances quantified the task-relevant information loss induced by the considered image reconstruction methods. Moreover, the U-Net reconstructed images possessed improved traditional metrics compared to those produced by the FBP method but resulted in lower image space CNN-IO performance. This demonstrates that traditional IQ measures can be misleading if task-performance is of ultimate interest.ConclusionThis work confirms that recent developments in learning-based IO approximation methods can enable the ranking of data-acquisition designs based on optimal task-performance with consideration of object variability. The work also demonstrates that such methods can enable estimation of task-based performance bounds for image reconstruction.

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Proceedings Article | 13 March 2024 Presentation

Artificial confocal microscopy for deep label-free imaging

Xi Chen, Mikhail Kandel, Shenghua He, Chenfei Hu, Young Jae Lee, Kathryn Sullivan, Gregory Tracy, Hee Jung Chung, Hyun Joon Kong, Mark Anastasio, Gabriel Popescu

Proceedings Volume PC12852, PC128520V (2024) https://doi.org/10.1117/12.3001172

KEYWORDS: Confocal microscopy, Fluorescence, Laser scanners, Fluorescence microscopy, Education and training, Turbidity, Tomography, Superposition, Phase imaging, Neurons

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Proceedings Article | 12 March 2024 Presentation + Paper

A learning-based image reconstruction method for skull-induced aberration compensation in transcranial photoacoustic computed tomography

Hsuan-Kai Huang, Joseph Kuo, Seonyeong Park, Umberto Villa, Lihong Wang, Mark Anastasio

Proceedings Volume 12842, 128420C (2024) https://doi.org/10.1117/12.3008569

KEYWORDS: Image restoration, Skull, Photoacoustic tomography, Elasticity, 3D modeling, Model based design, Acoustics, 3D image reconstruction

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Proceedings Article | 12 March 2024 Presentation + Paper

High-throughput photoacoustic tomography by integrated robotics and automation

Nathanael Marshall, Hans-Peter Brecht, Weylan Thompson, Dylan Lawrence, Vanessa Marshall, Samuel Toler, Stanislav Emelianov, Anthony Yu, Mark Anastasio, Umberto Villa, Joshua Maxwell, Sergey Ermilov

Proceedings Volume 12842, 128420I (2024) https://doi.org/10.1117/12.3005569

KEYWORDS: Imaging systems, Animals, Photoacoustic spectroscopy, Control systems, Spatial resolution, Animal model studies, Robotics, Acquisition tracking and pointing, Automation, Laser safety

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Proceedings Article | 12 March 2024 Presentation + Paper

Investigation of a learned image reconstruction method for three-dimensional quantitative photoacoustic tomography of the breast

Refik Mert Cam, Seonyeong Park, Umberto Villa, Mark Anastasio

Proceedings Volume 12842, 128420M (2024) https://doi.org/10.1117/12.3008904

KEYWORDS: Breast, Tumors, Photoacoustic tomography, Arteries, Veins, Computer simulations, Deep learning, Inverse imaging problems, Blood oxygen saturation

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Proceedings Article | 12 March 2024 Poster + Paper

Learning a semi-analytic reconstruction method for photoacoustic computed tomography with hemispherical measurement geometries

Panpan Chen, Seonyeong Park, Refik Mert Cam, Hsuan-Kai Huang, Umberto Villa, Mark Anastasio

Proceedings Volume 12842, 128420U (2024) https://doi.org/10.1117/12.3008778

KEYWORDS: Image restoration, Photoacoustic tomography, Tunable filters, Data modeling, Education and training, Data acquisition, Signal filtering, Linear filtering, Image filtering, 3D modeling

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SPIE Journal Paper | 19 January 2024 Open Access

Spatiotemporal image reconstruction to enable high-frame-rate dynamic photoacoustic tomography with rotating-gantry volumetric imagers

Refik Mert Cam, Chao Wang, Weylan Thompson, Sergey A. Ermilov, Mark A. Anastasio, Umberto Villa

JBO, Vol. 29, Issue S1, S11516, (January 2024) https://doi.org/10.1117/12.10.1117/1.JBO.29.S1.S11516

KEYWORDS: Image restoration, Tomography, Photoacoustic tomography, Imaging systems, Transducers, Data acquisition, Biomedical optics, Matrices, Image processing, Video

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SignificanceDynamic photoacoustic computed tomography (PACT) is a valuable imaging technique for monitoring physiological processes. However, current dynamic PACT imaging techniques are often limited to two-dimensional spatial imaging. Although volumetric PACT imagers are commercially available, these systems typically employ a rotating measurement gantry in which the tomographic data are sequentially acquired as opposed to being acquired simultaneously at all views. Because the dynamic object varies during the data-acquisition process, the sequential data-acquisition process poses substantial challenges to image reconstruction associated with data incompleteness. The proposed image reconstruction method is highly significant in that it will address these challenges and enable volumetric dynamic PACT imaging with existing preclinical imagers.AimThe aim of this study is to develop a spatiotemporal image reconstruction (STIR) method for dynamic PACT that can be applied to commercially available volumetric PACT imagers that employ a sequential scanning strategy. The proposed reconstruction method aims to overcome the challenges caused by the limited number of tomographic measurements acquired per frame.ApproachA low-rank matrix estimation-based STIR (LRME-STIR) method is proposed to enable dynamic volumetric PACT. The LRME-STIR method leverages the spatiotemporal redundancies in the dynamic object to accurately reconstruct a four-dimensional (4D) spatiotemporal image.ResultsThe conducted numerical studies substantiate the LRME-STIR method’s efficacy in reconstructing 4D dynamic images from tomographic measurements acquired with a rotating measurement gantry. The experimental study demonstrates the method’s ability to faithfully recover the flow of a contrast agent with a frame rate of 10 frames per second, even when only a single tomographic measurement per frame is available.ConclusionsThe proposed LRME-STIR method offers a promising solution to the challenges faced by enabling 4D dynamic imaging using commercially available volumetric PACT imagers. By enabling accurate STIRs, this method has the potential to significantly advance preclinical research and facilitate the monitoring of critical physiological biomarkers.

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SPIE Journal Paper | 26 September 2023 Open Access

Approximating the Hotelling observer with autoencoder-learned efficient channels for binary signal detection tasks

Jason Granstedt, Weimin Zhou, Mark Anastasio

JMI, Vol. 10, Issue 05, 055501, (September 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.5.055501

KEYWORDS: Education and training, Signal detection, Imaging systems, Signal attenuation, Breast, Binary data, Information operations, Image restoration, Image processing, Signal processing

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PurposeThe objective assessment of image quality (IQ) has been advocated for the analysis and optimization of medical imaging systems. One method of computing such IQ metrics is through a numerical observer. The Hotelling observer (HO) is the optimal linear observer, but conventional methods for obtaining the HO can become intractable due to large image sizes or insufficient data. Channelized methods are sometimes employed in such circumstances to approximate the HO. The performance of such channelized methods varies, with different methods obtaining superior performance to others depending on the imaging conditions and detection task. A channelized HO method using an AE is presented and implemented across several tasks to characterize its performance.ApproachThe process for training an AE is demonstrated to be equivalent to developing a set of channels for approximating the HO. The efficiency of the learned AE-channels is increased by modifying the conventional AE loss function to incorporate task-relevant information. Multiple binary detection tasks involving lumpy and breast phantom backgrounds across varying dataset sizes are considered to evaluate the performance of the proposed method and compare to current state-of-the-art channelized methods. Additionally, the ability of the channelized methods to generalize to images outside of the training dataset is investigated.ResultsAE-learned channels are demonstrated to have comparable performance with other state-of-the-art channel methods in the detection studies and superior performance in the generalization studies. Incorporating a cleaner estimate of the signal for the detection task is also demonstrated to significantly improve the performance of the proposed method, particularly in datasets with fewer images.ConclusionsAEs are demonstrated to be capable of learning efficient channels for the HO. The resulting significant increase in detection performance for small dataset sizes when incorporating a signal prior holds promising implications for future assessments of imaging technologies.

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SPIE Journal Paper | 20 June 2023 Open Access

Stochastic three-dimensional numerical phantoms to enable computational studies in quantitative optoacoustic computed tomography of breast cancer

Seonyeong Park, Umberto Villa, Fu Li, Refik Mert Cam, Alexander A. Oraevsky, Mark A. Anastasio

JBO, Vol. 28, Issue 06, 066002, (June 2023) https://doi.org/10.1117/12.10.1117/1.JBO.28.6.066002

KEYWORDS: Breast, Tissues, Acoustics, Anatomy, Blood, Tissue optics, Blood vessels, Tumors, Stochastic processes, Imaging systems

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SignificanceWhen developing a new quantitative optoacoustic computed tomography (OAT) system for diagnostic imaging of breast cancer, objective assessments of various system designs through human trials are infeasible due to cost and ethical concerns. In prototype stages, however, different system designs can be cost-efficiently assessed via virtual imaging trials (VITs) employing ensembles of digital breast phantoms, i.e., numerical breast phantoms (NBPs), that convey clinically relevant variability in anatomy and optoacoustic tissue properties.AimThe aim is to develop a framework for generating ensembles of realistic three-dimensional (3D) anatomical, functional, optical, and acoustic NBPs and numerical lesion phantoms (NLPs) for use in VITs of OAT applications in the diagnostic imaging of breast cancer.ApproachThe generation of the anatomical NBPs was accomplished by extending existing NBPs developed by the U.S. Food and Drug Administration. As these were designed for use in mammography applications, substantial modifications were made to improve blood vasculature modeling for use in OAT. The NLPs were modeled to include viable tumor cells only or a combination of viable tumor cells, necrotic core, and peripheral angiogenesis region. Realistic optoacoustic tissue properties were stochastically assigned in the NBPs and NLPs.ResultsTo advance optoacoustic and optical imaging research, 84 datasets have been released; these consist of anatomical, functional, optical, and acoustic NBPs and the corresponding simulated multi-wavelength optical fluence, initial pressure, and OAT measurements. The generated NBPs were compared with clinical data with respect to the volume of breast blood vessels and spatially averaged effective optical attenuation. The usefulness of the proposed framework was demonstrated through a case study to investigate the impact of acoustic heterogeneity on OAT images of the breast.ConclusionsThe proposed framework will enhance the authenticity of virtual OAT studies and can be widely employed for the investigation and development of advanced image reconstruction and machine learning-based methods, as well as the objective evaluation and optimization of the OAT system designs.

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Proceedings Article | 10 April 2023 Presentation + Paper

3D full-waveform inversion in ultrasound computed tomography employing a ring-array

Fu Li, Umberto Villa, Nebojsa Duric, Mark Anastasio

Proceedings Volume 12470, 124700K (2023) https://doi.org/10.1117/12.2654406

KEYWORDS: Ultrasound tomography, Breast imaging, 3D image reconstruction, Data acquisition

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Proceedings Article | 10 April 2023 Presentation + Paper

A deep-learning-based image reconstruction method for USCT that employs multimodality inputs

Gangwon Jeong, Fu Li, Umberto Villa, Mark Anastasio

Proceedings Volume 12470, 124700M (2023) https://doi.org/10.1117/12.2654564

KEYWORDS: Image restoration, Tomography, Education and training, Medical image reconstruction, Image quality, Ultrasound reflection tomography, Deep learning, Machine learning, Acoustics, Tissues

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Proceedings Article | 7 April 2023 Presentation + Paper

Memory-efficient self-supervised learning of null space projection operators

Albert Zhai, Joseph Kuo, Mark Anastasio, Umberto Villa

Proceedings Volume 12463, 124631I (2023) https://doi.org/10.1117/12.2654260

KEYWORDS: Imaging systems, Matrices, Tomography, Singular value decomposition, Image processing, Radon transform, Mathematical optimization, Machine learning, Linear algebra, Medical image processing, Computational imaging, Medical imaging

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Proceedings Article | 7 April 2023 Presentation

Low-rank matrix estimation-based spatiotemporal image reconstruction from few tomographic measurements per frame for dynamic photoacoustic computed tomography

Refik Mert Cam, Chao Wang, Weylan Thompson, Sergey Ermilov, Mark Anastasio, Umberto Villa

Proceedings Volume 12463, 124630R (2023) https://doi.org/10.1117/12.2654199

KEYWORDS: Photoacoustic tomography, Tomography, Image restoration, Image analysis, Tissue optics, Biomedical optics, Tissues, Data acquisition, Biological research, Tumors

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Proceedings Article | 7 April 2023 Poster

Assessing the applicability of a learning-based method for quantitative phase retrieval from propagation-based x-ray phase contrast images under benchtop conditions

Rucha Deshpande, Ashish Avachat, Frank Brooks, Mark Anastasio

Proceedings Volume 12463, 124632T (2023) https://doi.org/10.1117/12.2653921

KEYWORDS: Phase retrieval, X-rays, X-ray imaging, Phase contrast, Systems modeling, Spatial coherence, Data modeling

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Proceedings Article | 7 April 2023 Presentation + Paper

Semi-supervised contrastive learning for white blood cell segmentation from label-free quantitative phase imaging

Sourya Sengupta, Michael Fanous, Hua Li, Mark Anastasio

Proceedings Volume 12471, 124710E (2023) https://doi.org/10.1117/12.2654584

KEYWORDS: Image segmentation, White blood cells, Phase imaging, Convolution, Biological samples, Blood, Binary data

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Proceedings Article | 3 April 2023 Poster + Paper

Evaluating generative stochastic image models using task-based image quality measures

Varun Kelkar, Dimitrios Gotsis, Rucha Deshpande, Frank Brooks, Prabhat K. C., Kyle Myers, Rongping Zeng, Mark Anastasio

Proceedings Volume 12467, 1246717 (2023) https://doi.org/10.1117/12.2654590

KEYWORDS: Signal detection, Image quality, Data modeling, Stochastic processes, Medical imaging, Medical statistics, Mammography, Image analysis, Realistic image synthesis

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Proceedings Article | 3 April 2023 Presentation + Paper

Estimating task-based performance bounds for image reconstruction methods by use of learned-ideal observers

Kaiyan Li, Weimin Zhou, Hua Li, Mark Anastasio

Proceedings Volume 12467, 124670I (2023) https://doi.org/10.1117/12.2655241

KEYWORDS: Image restoration, Information operations, Signal detection, Imaging systems, Tomography, Stochastic processes, Binary data, Image quality, Education and training, X-ray computed tomography

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Proceedings Article | 17 March 2023 Presentation

Template- and model-based decoding of movie identities with high-density diffuse optical tomography of neural hemodynamics

Zachary Markow, Kalyan Tripathy, Jason Trobaugh, Alexa Svoboda, Mariel Schroeder, Sean Rafferty, Edward Richter, Adam Eggebrecht, Mark Anastasio, Joseph Culver

Proceedings Volume PC12365, PC1236503 (2023) https://doi.org/10.1117/12.2649294

KEYWORDS: Model-based design, Diffuse optical tomography, Brain, Hemodynamics, Functional magnetic resonance imaging, Neuroimaging, Motion models, Electroencephalography, Near infrared spectroscopy, Magnetic resonance imaging

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Proceedings Article | 17 March 2023 Presentation

Evaluation of multivariate approaches to functional connectivity mapping with fNIRS

Wiete Fehner, Morgan Fogarty, Mark Anastasio, Joseph Culver

Proceedings Volume PC12365, PC123650C (2023) https://doi.org/10.1117/12.2649434

KEYWORDS: Brain mapping, Microsoft Foundation Class Library, Brain

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Proceedings Article | 15 March 2023 Poster + Paper

Attention-based CNN-BiLSTM for sleep state classification of spatiotemporal wide-field calcium imaging data

Xiaohui Zhang, Eric Landsness, Joseph Culver, Jin-Moo Lee, Mark Anastasio

Proceedings Volume 12365, 123650B (2023) https://doi.org/10.1117/12.2649375

KEYWORDS: Deep learning, Machine learning, Classification systems, Calcium, Data modeling, Brain, Network architectures, Feature extraction, Convolutional neural networks, Spatial learning, Reliability, Electroencephalography

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Proceedings Article | 9 March 2023 Open Access

Deep learning and photoacoustic image formation: promises and challenges

Mark Anastasio

Proceedings Volume PC12379, PC1237901 (2023) https://doi.org/10.1117/12.2674089

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Proceedings Article | 9 March 2023 Presentation

Numerical investigation of impact of skin phototype on three-dimensional optoacoustic tomography of the breast

Seonyeong Park, Umberto Villa, Alexander Oraevsky, Mark Anastasio

Proceedings Volume PC12379, PC123790E (2023) https://doi.org/10.1117/12.2650740

KEYWORDS: Skin, Breast, Tissue optics, Tomography, Optoacoustics, Signal to noise ratio, Photoacoustic tomography, Interfaces, Image filtering, Breast imaging

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Proceedings Article | 9 March 2023 Presentation + Paper

Dynamic image reconstruction to monitor tumor vascular perfusion in small animals using 3D photoacoustic computed-tomography imagers with rotating gantries

Refik Mert Cam, Chao Wang, Seonyeong Park, Weylan Thompson, Sergey Ermilov, Mark Anastasio, Umberto Villa

Proceedings Volume 12379, 123790F (2023) https://doi.org/10.1117/12.2647663

KEYWORDS: Photoacoustic tomography, Tumors, Imaging systems, Acoustics, Matrices, Tomography, 3D image reconstruction, Preclinical imaging, Iterative methods, Inverse imaging problems, Small animal whole body imaging

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Proceedings Article | 18 October 2022 Open Access

Image reconstruction in phase-contrast CT with shortened scans

Zheng Zhang, Buxin Chen, Dan Xia, Emil Sidky, Mark Anastasio, Xiaochuan Pan

Proceedings Volume 12304, 123042A (2022) https://doi.org/10.1117/12.2646432

KEYWORDS: Reconstruction algorithms, Data modeling, Algorithm development, Image restoration, X-ray computed tomography, X-rays, Refractive index, Mathematical modeling, Computed tomography, X-ray imaging

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Proceedings Article | 28 April 2022 Poster + Presentation

Identifying functional brain networks from spatial-temporal wide-field calcium imaging data via a recurrent autoencoder

Xiaohui Zhang, Eric Landsness, Joseph Culver, Mark Anastasio

Proceedings Volume PC11946, PC1194612 (2022) https://doi.org/10.1117/12.2626317

KEYWORDS: Calcium, Brain, Neuroimaging, Temporal resolution, Signal to noise ratio, Neural networks, Network architectures, Independent component analysis, Hemodynamics

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Proceedings Article | 28 April 2022 Presentation

Template-based and model-based decoding of movie clip identities from brain hemodynamics with high-density diffuse optical tomography

Zachary Markow, Kalyan Tripathy, Jason Trobaugh, Alexa Svoboda, Mariel Schroeder, Sean Rafferty, Edward Richter, Adam Eggebrecht, Mark Anastasio, Joseph Culver

Proceedings Volume PC11946, PC119460I (2022) https://doi.org/10.1117/12.2609038

KEYWORDS: Brain, Model-based design, Functional magnetic resonance imaging, Diffuse optical tomography, Hemodynamics, Neuroimaging, Motion models, Electroencephalography, Near infrared spectroscopy, Interfaces

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Proceedings Article | 4 April 2022 Presentation + Paper

Prior image-based medical image reconstruction using a style-based generative adversarial network

Varun Kelkar, Mark Anastasio

Proceedings Volume 12031, 120310Z (2022) https://doi.org/10.1117/12.2612287

KEYWORDS: Magnetic resonance imaging, Image restoration, Inverse problems, Neuroimaging, Medical imaging, Brain, Medical image reconstruction, Imaging systems, Compressed sensing

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Proceedings Article | 4 April 2022 Poster + Presentation + Paper

A learned filtered backprojection method for use with half-time circular radon transform data

Refik Mert Cam, Umberto Villa, Mark Anastasio

Proceedings Volume 12031, 1203134 (2022) https://doi.org/10.1117/12.2612941

KEYWORDS: Tomography, Radon transform, Photoacoustic tomography, Data modeling, Acoustics, Linear filtering, Image filtering, Reconstruction algorithms

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Proceedings Article | 4 April 2022 Presentation + Paper

Neural fields for dynamic imaging

Luke Lozenski, Mark Anastasio, Umberto Villa

Proceedings Volume 12031, 120310X (2022) https://doi.org/10.1117/12.2613148

KEYWORDS: Image restoration, Imaging systems, Sensors, Neural networks, Image quality, Photoacoustic tomography, Optimization (mathematics), Data acquisition

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Proceedings Article | 4 April 2022 Presentation + Paper

Analyzing neural networks applied to an anatomical simulation of the breast

Craig Abbey, Sourya Sengupta, Weimin Zhou, Andreu Badal, Rongping Zeng, Frank Samuelson, Miguel Eckstein, Kyle Myers, Mark Anastasio, Jovan Brankov

Proceedings Volume 12035, 1203505 (2022) https://doi.org/10.1117/12.2612614

KEYWORDS: Denoising, Neural networks, Breast, Data modeling, Digital breast tomosynthesis, Signal detection

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Proceedings Article | 4 April 2022 Presentation + Paper

Evaluating procedures for establishing generative adversarial network-based stochastic image models in medical imaging

Varun Kelkar, Dimitrios Gotsis, Frank Brooks, Kyle Myers, Prabhat K.C., Rongping Zeng, Mark Anastasio

Proceedings Volume 12035, 120350O (2022) https://doi.org/10.1117/12.2612893

KEYWORDS: Machine learning, Medical imaging, Aneurysms, Computer simulations, Data modeling, Stochastic processes, Angiography, Visualization, Visual process modeling, Systems modeling

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Proceedings Article | 4 April 2022 Paper

Investigation of adversarial robust training for establishing interpretable CNN-based numerical observers

Sourya Sengupta, Craig Abbey, Kaiyan Li, Mark Anastasio

Proceedings Volume 12035, 1203514 (2022) https://doi.org/10.1117/12.2613220

KEYWORDS: Image classification, Visual process modeling, Performance modeling, Visualization, Data modeling, Signal detection, Optimization (mathematics), Image visualization, Binary data, Machine learning

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Proceedings Article | 4 April 2022 Poster + Presentation + Paper

Learned Hotelling observers for use with multi-modal data

Jason Granstedt, Fu Li, Umberto Villa, Mark Anastasio

Proceedings Volume 12035, 1203512 (2022) https://doi.org/10.1117/12.2613138

KEYWORDS: Imaging systems, Signal detection, Signal to noise ratio, Data modeling, Stochastic processes, Information operations, Image quality, Image processing, Computed tomography, Targeting Task Performance metric, Ultrasound tomography, Machine learning, Artificial neural networks

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Proceedings Article | 4 April 2022 Poster + Paper

A task-informed model training method for deep neural network-based image denoising

Kaiyan Li, Hua Li, Mark Anastasio

Proceedings Volume 12035, 1203510 (2022) https://doi.org/10.1117/12.2613181

KEYWORDS: Denoising, Signal detection, Image quality, Binary data, Neural networks, Image denoising, Computed tomography

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Proceedings Article | 4 April 2022 Poster + Paper

Evaluating the capacity of deep generative models to reproduce measurable high-order spatial arrangements in diagnostic images

Rucha Deshpande, Mark Anastasio, Frank Brooks

Proceedings Volume 12032, 120321X (2022) https://doi.org/10.1117/12.2611807

KEYWORDS: Diagnostics, Network architectures, Medical imaging, Feature extraction, Error analysis, Stochastic processes, Statistical modeling, Visualization

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Proceedings Article | 4 April 2022 Presentation + Paper

Application of DatasetGAN in medical imaging: preliminary studies

Zong Fan, Varun Kelkar, Mark Anastasio, Hua Li

Proceedings Volume 12032, 120321O (2022) https://doi.org/10.1117/12.2611191

KEYWORDS: Image segmentation, Medical imaging, Data modeling, Feature extraction, Visualization, Performance modeling, Network architectures

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Proceedings Article | 4 April 2022 Presentation + Paper

Investigation of an elevation-focused transducer model for three-dimensional full-waveform inversion in ultrasound computed tomography

Fu Li, Umberto Villa, Nebojsa Duric, Mark Anastasio

Proceedings Volume 12038, 120380S (2022) https://doi.org/10.1117/12.2613207

KEYWORDS: Transducers, Acoustics, 3D image processing, Systems modeling, 3D image reconstruction, Wave propagation, Imaging systems, Ultrasound tomography

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SPIE Journal Paper | 16 March 2022 Open Access

Normalization of optical fluence distribution for three-dimensional functional optoacoustic tomography of the breast

Seonyeong Park, Frank Brooks, Umberto Villa, Richard Su, Mark Anastasio, Alexander Oraevsky

JBO, Vol. 27, Issue 03, 036001, (March 2022) https://doi.org/10.1117/12.10.1117/1.JBO.27.3.036001

KEYWORDS: Breast, Tissue optics, 3D image processing, Signal attenuation, Optoacoustics, Blood, 3D image reconstruction, Blood vessels, Imaging systems, Breast imaging

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SPIE Journal Paper | 23 February 2022 Open Access

Learning stochastic object models from medical imaging measurements by use of advanced ambient generative adversarial networks

Weimin Zhou, Sayantan Bhadra, Frank Brooks, Hua Li, Mark Anastasio

JMI, Vol. 9, Issue 01, 015503, (February 2022) https://doi.org/10.1117/12.10.1117/1.JMI.9.1.015503

KEYWORDS: Imaging systems, Medical imaging, Gallium nitride, Stochastic processes, Magnetic resonance imaging, Signal to noise ratio, Statistical modeling, Data acquisition, Systems modeling, Visualization

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SPIE Journal Paper | 16 November 2021 Open Access

Impact of deep learning-based image super-resolution on binary signal detection

Xiaohui Zhang, Varun Kelkar, Jason Granstedt, Hua Li, Mark Anastasio

JMI, Vol. 8, Issue 06, 065501, (November 2021) https://doi.org/10.1117/12.10.1117/1.JMI.8.6.065501

KEYWORDS: Signal detection, Lawrencium, Super resolution, Binary data, Information operations, Network architectures, Medical imaging, Surgery, Signal attenuation, Image quality

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SPIE Journal Paper | 10 May 2021 Open Access

Special Series Guest Editorial: Artificial Intelligence and Machine Learning in Biomedical Optics

Behrouz Shabestari, Mark Anastasio, Baowei Fei, Frédéric Leblond

JBO, Vol. 26, Issue 05, 052901, (May 2021) https://doi.org/10.1117/12.10.1117/1.JBO.26.5.052901

KEYWORDS: Artificial intelligence, Machine learning, Biomedical optics, Tissues, Raman spectroscopy, Image segmentation, Tissue optics, Imaging spectroscopy, Optical coherence tomography, Integrated optics

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Proceedings Article | 8 March 2021 Presentation + Paper

An optical, endoscopic brush for high-yield diagnostics in esophageal cancer

S. Anandasabapathy, X. Zhang, M. Anastasio, R. Richards-Kortum, E. Petrova

Proceedings Volume 11620, 116200B (2021) https://doi.org/10.1117/12.2583301

KEYWORDS: Endoscopy, Diagnostics, Cancer, Super resolution, Algorithm development, Optical fibers, Motion models, Laser imaging, Image segmentation, Image processing

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Proceedings Article | 6 March 2021 Presentation

Three-dimensional quantitative functional optoacoustic tomography to estimate vascular blood oxygenation of the breast

Seonyeong Park, Umberto Villa, Frank Brooks, Richard Su, Alexander Oraevsky, Mark Anastasio

Proceedings Volume 11642, 116421N (2021) https://doi.org/10.1117/12.2585341

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The goal of quantitative optoacoustic tomography (qOAT) is to reconstruct a distribution of absolute chromophore concentrations and/or functional properties from measurements of the optically induced pressure (ultrasound signals) acquired at multiple excitation wavelengths. Estimating the distribution of hemoglobin, an endogenous OAT chromophore, is important because the oxygen saturation distribution of the blood vessels is a well-known indicator of aggressive growth of a cancerous tumor. In a number of studies, a spectral linear unmixing method has been applied to two-dimensional slices of tissue acquired with OAT at multiple wavelengths, leading to promising results at moderate penetration depths of ≤ 2 cm. In the three-dimensional (3D) OAT of the breast, such functional images cannot be accurately reconstructed via the spectral linear unmixing method due to unknown spatial distribution of the optical fluence in a relatively large size of the volume of interest (≥ 4 cm). Optical attenuation in biological tissue depends on the optical wavelength, and the optical fluence is exponentially attenuated with increasing imaging depth. Thus, the accuracy of the estimated distribution decreases with depth. To overcome this challenge, we investigated a spectral linear unmixing method with a simplified optical fluence normalization based on measurements of background absorbed optical energy in the breast. We compare estimates of blood oxygen saturations from two-wavelength clinical OAT breast images and demonstrate acceptable accuracy of ~10% while lack of compensation for the optical fluence distribution can lead to values outside the physiological range. We also quantitatively compare the accuracy of oxygen saturation estimates using numerical simulation of photon transport in realistic 3D OAT breast phantoms at dual wavelengths of 757 and 850 nm with inverse ratio of the optical absorption by deoxy- (Hb) and oxy-hemoglobin (HbO2) and three wavelengths of 757, 800, and 850 nm with inclusion of isosbestic point of the optical absorption in Hb/HbO₂.

Proceedings Article | 6 March 2021 Presentation

Dynamic reconstruction of three-dimensional photoacoustic tomography from few projections

Chao Wang, Umberto Villa, Weylan Thompson, Seonyeong Park, Sergey Ermilov, Mark Anastasio

Proceedings Volume 11642, 116421A (2021) https://doi.org/10.1117/12.2585344

KEYWORDS: Acquisition tracking and pointing, Photoacoustic tomography, 3D metrology, Reconstruction algorithms, 3D image reconstruction, Tomography, Tissues, Tissue optics, Temporal resolution, Numerical simulations

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Proceedings Article | 5 March 2021 Open Access

Cell cycle detection using phase imaging with computational specificity (PICS)

Yuchen He, Shenghua He, Mikhail Kandel, Young Jae Lee, Nahil Sobh, Mark Anastasio, Gabriel Popescu

Proceedings Volume 11653, 116531R (2021) https://doi.org/10.1117/12.2584432

KEYWORDS: Luminescence, Photonic integrated circuits, Phase imaging, Neural networks, Microscopy, Performance modeling, Network architectures, Medical research, Machine learning, Image segmentation

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Proceedings Article | 5 March 2021 Open Access

Laser scanning GLIM (LS-GLIM) for label-free imaging of turbid samples

Xi Chen, Mikhail Kandel, Shenghua He, Young Jae Lee, Kathryn Sullivan, Hyun Joon Kong, Mark Anastasio, Gabriel Popescu

Proceedings Volume 11653, 1165304 (2021) https://doi.org/10.1117/12.2584286

KEYWORDS: Laser scanners, Microscopes, Luminescence, Prisms, Phase shifts, Light, Laser sources, Confocal microscopy, Wave plates, Signal to noise ratio

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Proceedings Article | 5 March 2021 Open Access

Label-free cell viability assay using phase imaging with computational specificity (PICS)

Chenfei Hu, Shenghua He, Young Jae Lee, Yuchen He, Mark Anastasio, Gabriel Popescu

Proceedings Volume 11653, 116531D (2021) https://doi.org/10.1117/12.2584228

KEYWORDS: Phase imaging, Photonic integrated circuits, Microscopy, Luminescence, Molecules, Image segmentation

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Proceedings Article | 5 March 2021 Presentation

Decoding naturalistic movie clip identities from diffuse optical tomography measures of human brain activity

Zachary Markow, Kalyan Tripathy, Jason Trobaugh, Alexa Svoboda, Mariel Schroeder, Sean Rafferty, Edward Richter, Adam Eggebrecht, Mark Anastasio, Joseph Culver

Proceedings Volume 11629, 116291K (2021) https://doi.org/10.1117/12.2578609

KEYWORDS: Brain, Functional magnetic resonance imaging, Neuroimaging, Electroencephalography, Near infrared spectroscopy, Interfaces, Diffuse optical tomography, Blood

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Proceedings Article | 15 February 2021 Presentation + Paper

Advancing the AmbientGAN for learning stochastic object models

Weimin Zhou, Sayantan Bhadra, Frank Brooks, Jason Granstedt, Hua Li, Mark Anastasio

Proceedings Volume 11599, 115990A (2021) https://doi.org/10.1117/12.2581687

KEYWORDS: Systems modeling, Stochastic processes, Imaging systems, 3D image processing, Gallium nitride, 3D metrology, Stereoscopy, Network architectures, Medical imaging, Magnetic resonance imaging

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Proceedings Article | 15 February 2021 Presentation + Paper

Supervised learning-based ideal observer approximation for joint detection and estimation tasks

Kaiyan Li, Weimin Zhou, Hua Li, Mark Anastasio

Proceedings Volume 11599, 115990F (2021) https://doi.org/10.1117/12.2582327

KEYWORDS: Information operations, Imaging systems, Monte Carlo methods, Machine learning, Convolutional neural networks, Computer simulations

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Proceedings Article | 15 February 2021 Presentation

A framework for ultrasound computed tomography virtual imaging trials that employs anatomically realistic numerical breast phantoms

Fu Li, Umberto Villa, Seonyeong Park, Shenghua He, Mark Anastasio

Proceedings Volume 11602, 116020V (2021) https://doi.org/10.1117/12.2582260

KEYWORDS: Breast, Ultrasonography, Computed tomography, 3D modeling, Tissues, Reconstruction algorithms, Algorithm development, Acoustics, Wave propagation, Tumors

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Proceedings Article | 15 February 2021 Poster + Presentation + Paper

Learning a projection operator onto the null space of a linear imaging operator

Joseph Kuo, Jason Granstedt, Umberto Villa, Mark Anastasio

Proceedings Volume 11595, 115953X (2021) https://doi.org/10.1117/12.2582263

KEYWORDS: Space operations, Reconstruction algorithms, Imaging systems, Magnetic resonance imaging, Linear algebra, Image restoration, Image resolution, Artificial neural networks

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Proceedings Article | 15 February 2021 Poster + Presentation + Paper

Task-based evaluation of deep image super-resolution in medical imaging

Varun Kelkar, Xiaohui Zhang, Jason Granstedt, Hua Li, Mark Anastasio

Proceedings Volume 11599, 115990X (2021) https://doi.org/10.1117/12.2582011

KEYWORDS: Medical imaging, Super resolution, Image analysis, Image quality, Signal to noise ratio, Quality measurement, Neural networks, Diagnostics

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Proceedings Article | 15 February 2021 Presentation + Paper

Task-based performance evaluation of deep neural network-based image denoising

Kaiyan Li, Weimin Zhou, Hua Li, Mark Anastasio

Proceedings Volume 11599, 115990L (2021) https://doi.org/10.1117/12.2582324

KEYWORDS: Neural networks, Image denoising, Denoising, Signal detection, Surgery, Signal attenuation, Quality measurement, Medical imaging applications, Medical imaging, Information operations

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Proceedings Article | 15 February 2021 Presentation + Paper

A hybrid channelized Hotelling observer for estimating the ideal linear observer for total-variation-based image reconstruction

John Paul Phillips, Emil Sidky, Greg Ongie, Weimin Zhou, Juan Cruz-Bastida, Ingrid Reiser, Mark Anastasio, Xiaochuan Pan

Proceedings Volume 11599, 115990D (2021) https://doi.org/10.1117/12.2581248

KEYWORDS: Image restoration, Signal detection, Reconstruction algorithms, Nonlinear optics, X-ray computed tomography, Optimization (mathematics), Medical imaging, Image quality, Detection and tracking algorithms

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Proceedings Article | 15 February 2021 Presentation + Paper

Medical image reconstruction using compressible latent space invertible networks

Varun Kelkar, Sayantan Bhadra, Mark Anastasio

Proceedings Volume 11595, 115951S (2021) https://doi.org/10.1117/12.2581295

KEYWORDS: Medical image reconstruction, Image compression, Gallium nitride, Image restoration, Medical imaging, Neural networks, Magnetic resonance imaging, Indium nitride, Imaging systems, Image quality

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Proceedings Article | 15 February 2021 Presentation + Paper

Assessing regularization in tomographic imaging via hallucinations in the null space

Sayantan Bhadra, Varun Kelkar, Frank Brooks, Mark Anastasio

Proceedings Volume 11599, 115990N (2021) https://doi.org/10.1117/12.2581316

KEYWORDS: Tomography, Medical imaging, Magnetic resonance imaging, Inverse problems, Image restoration, Neural networks, Image analysis

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Proceedings Article | 15 February 2021 Presentation + Paper

SlabGAN: a method for generating efficient 3D anisotropic medical volumes using generative adversarial networks

Jason Granstedt, Varun Kelkar, Weimin Zhou, Mark Anastasio

Proceedings Volume 11596, 1159617 (2021) https://doi.org/10.1117/12.2581380

KEYWORDS: Gallium nitride, 3D image processing, Medical imaging, Magnetic resonance imaging, Super resolution, Stochastic processes, Neuroimaging, Medical image reconstruction, Imaging systems, Image restoration

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Proceedings Article | 16 March 2020 Presentation + Paper

Medical image reconstruction with image-adaptive priors learned by use of generative adversarial networks

Sayantan Bhadra, Weimin Zhou, Mark Anastasio

Proceedings Volume 11312, 113120V (2020) https://doi.org/10.1117/12.2549750

KEYWORDS: Magnetic resonance imaging, Data modeling, Inverse problems, Medical image reconstruction, Reconstruction algorithms, Image restoration, Medical diagnostics, Imaging systems

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Medical image reconstruction is often an ill-posed inverse problem. In order to address such ill-posed inverse problems, prior knowledge of the sought after object property is usually incorporated by means of regularization. For example, sparsity-promoting regularization in a suitable transform domain is widely used to reconstruct images with diagnostic quality from noisy and/or incomplete medical data. However, sparsity-promoting regularization may not be able to comprehensively describe the actual prior information of the objects being imaged. Deep generative models, such as generative adversarial networks (GANs) have shown great promise in learning the underlying distribution of images. Prior distributions for images estimated using GANs have been employed as a means of regularization with impressive results in several linear inverse problems in computer vision that are also relevant to medical imaging. However, in practice, it can be difficult for a GAN to comprehensively describe prior distributions, which can potentially lead to a lack of fidelity between the reconstructed image and the observed data. Recently, an image-adaptive GAN-based reconstruction method (IAGAN) was proposed to guarantee stronger data consistency by adapting the trained generative model parameters to the observed measurements. In this work, for the first time, we apply the IAGAN method to reconstruct images from undersampled magnetic resonance imaging (MRI) measurements. A state-of-the-art GAN model called Progressive Growing of GANs (ProGAN) was trained on a large number of ground truth images from the NYU fastMRI dataset, and the learned generator was subsequently employed in the IAGAN framework to reconstruct high fidelity images from retrospectively undersampled experimental k-space data in the validation dataset. It is demonstrated that by use of the GAN-based reconstruction method with noisy and/or incomplete measurements, we can potentially recover fine structures in the object that are relevant for medical diagnosis that may be difficult to achieve using traditional reconstruction methods relying on sparsity-promoting penalties.

Proceedings Article | 16 March 2020 Presentation + Paper

Learning efficient channels with a dual loss autoencoder

Jason Granstedt, Weimin Zhou, Mark Anastasio

Proceedings Volume 11316, 113160C (2020) https://doi.org/10.1117/12.2549363

KEYWORDS: Neural networks, Signal detection, Imaging systems, Signal to noise ratio, Breast, Performance modeling, Image quality

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Proceedings Article | 16 March 2020 Presentation + Paper

Markov-Chain Monte Carlo approximation of the Ideal Observer using generative adversarial networks

Weimin Zhou, Mark Anastasio

Proceedings Volume 11316, 113160D (2020) https://doi.org/10.1117/12.2549732

KEYWORDS: Signal detection, Monte Carlo methods, Imaging systems, Statistical analysis, Systems modeling, Stochastic processes, Performance modeling

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Proceedings Article | 16 March 2020 Presentation + Paper

Progressively-Growing AmbientGANs for learning stochastic object models from imaging measurements

Weimin Zhou, Sayantan Bhadra, Frank Brooks, Hua Li, Mark Anastasio

Proceedings Volume 11316, 113160Q (2020) https://doi.org/10.1117/12.2549610

KEYWORDS: Magnetic resonance imaging, Imaging systems, Signal detection, Stochastic processes, Neuroimaging, Brain, Data modeling, Systems modeling

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Proceedings Article | 16 March 2020 Presentation + Paper

Learning numerical observers using unsupervised domain adaptation

Shenghua He, Weimin Zhou, Hua Li, Mark Anastasio

Proceedings Volume 11316, 113160W (2020) https://doi.org/10.1117/12.2549812

KEYWORDS: Signal detection, Imaging systems, Image quality, Neural networks, Medical imaging

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Proceedings Article | 6 March 2020 Presentation

Realistic three-dimensional optoacoustic tomography imaging trials using the VICTRE breast phantom of FDA (Conference Presentation)

Seonyeong Park, Umberto Villa, Richard Su, Alexander Oraevsky, Frank Brooks, Mark Anastasio

Proceedings Volume 11240, 112401H (2020) https://doi.org/10.1117/12.2552380

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Proceedings Article | 17 February 2020 Presentation + Paper

A preclinical small animal imaging platform combining multi-angle photoacoustic and fluorescence projections into co-registered 3D maps

Weylan Thompson, Anthony Yu, Diego Dumani, Jason Cook, Mark Anastasio, Stanislav Emelianov, Sergey Ermilov

Proceedings Volume 11240, 112400L (2020) https://doi.org/10.1117/12.2549088

KEYWORDS: Photoacoustic imaging, Fluorescence tomography, Contrast sensitivity, Photoacoustic tomography, Optical imaging, In vivo imaging

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SPIE Journal Paper | 18 December 2019 Open Access

Special Section Guest Editorial: Celebrating the Exponential Growth of Optoacoustic/Photoacoustic Imaging

Paul Beard, Mark Anastasio, Alexander Oraevsky, Lihong Wang

JBO, Vol. 24, Issue 12, 121901, (December 2019) https://doi.org/10.1117/12.10.1117/1.JBO.24.12.121901

KEYWORDS: Photoacoustic spectroscopy, Biomedical optics, Optoacoustics, Ultrasonography, Tumors, Monte Carlo methods, Data modeling, Spectroscopy, Sensors, In vivo imaging

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SPIE Journal Paper | 26 April 2019 Open Access

All-reflective ring illumination system for photoacoustic tomography

Suhail Salem Alshahrani, Yan Yan, Naser Alijabbari, Alexander Pattyn, Ivan Avrutsky, Eugene Malyarenko, Joemini Poudel, Mark Anastasio, Mohammad Mehrmohammadi

JBO, Vol. 24, Issue 04, 046004, (April 2019) https://doi.org/10.1117/12.10.1117/1.JBO.24.4.046004

KEYWORDS: Imaging systems, Reflectors, Transducers, Acquisition tracking and pointing, Photoacoustic tomography, Mirrors, Breast, Tissues, Breast cancer, Tissue optics

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Proceedings Article | 19 March 2019 Paper

Preclinical small animal imaging platform providing co-registered 3D maps of photoacoustic response and fluorescence

Diego Dumani, Anthony Yu, Weylan Thompson, Hans-Peter Brecht, Vassili Ivanov, Mark Anastasio, Jason Cook, Sergey Ermilov, Stanislav Emelianov

Proceedings Volume 10878, 108784Y (2019) https://doi.org/10.1117/12.2514489

KEYWORDS: Skin, Luminescence, Photoacoustic spectroscopy, Tissues, 3D image processing, Animal model studies, Preclinical imaging, Spatial resolution, In vivo imaging, Acquisition tracking and pointing

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Proceedings Article | 18 March 2019 Presentation + Paper

Automatic microscopic cell counting by use of deeply-supervised density regression model

Shenghua He, Kyaw Thu Minn, Lilianna Solnica-Krezel, Mark Anastasio, Hua Li

Proceedings Volume 10956, 109560L (2019) https://doi.org/10.1117/12.2513045

KEYWORDS: Error analysis, Network architectures, Convolutional neural networks, Feature extraction, Stem cells, Image processing, Machine vision, Computer vision technology, Image segmentation, Lithium

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Proceedings Article | 18 March 2019 Presentation + Paper

Automatic microscopic cell counting by use of unsupervised adversarial domain adaptation and supervised density regression

Shenghua He, Kyaw Thu Minn, Lilianna Solnica-Krezel, Hua Li, Mark Anastasio

Proceedings Volume 10956, 1095604 (2019) https://doi.org/10.1117/12.2513058

KEYWORDS: Image analysis, Machine learning, Diagnostics, Stem cells, Machine vision, Computer vision technology

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Proceedings Article | 15 March 2019 Presentation

Compensation of 3D-2D model mismatch in ultrasound computed tomography with the aid of convolutional neural networks (Conference Presentation)

Joemini Poudel, Luca Forte, Mark Anastasio

Proceedings Volume 10955, 1095507 (2019) https://doi.org/10.1117/12.2512966

KEYWORDS: 3D modeling, Ultrasonography, Computed tomography, Convolutional neural networks, Wave propagation, 3D image reconstruction, Data modeling, Breast cancer, Image restoration, CT reconstruction

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Proceedings Article | 8 March 2019 Paper

Compensation for non-uniform illumination and optical fluence attenuation in three-dimensional optoacoustic tomography of the breast

Seonyeong Park, Alexander Oraevsky, Richard Su, Mark Anastasio

Proceedings Volume 10878, 108784X (2019) https://doi.org/10.1117/12.2514750

KEYWORDS: Breast, Tissue optics, Signal attenuation, Blood vessels, 3D image processing, Natural surfaces, Tissues, Optoacoustics, Tomography, Nipple

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Proceedings Article | 4 March 2019 Presentation + Paper

Autoencoder embedding of task-specific information

Jason Granstedt, Weimin Zhou, Mark Anastasio

Proceedings Volume 10952, 1095207 (2019) https://doi.org/10.1117/12.2513120

KEYWORDS: Computer programming, Signal detection, Statistical analysis, Imaging systems, Targeting Task Performance metric, Machine learning, Image processing algorithms and systems

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Proceedings Article | 4 March 2019 Presentation + Paper

Learning the ideal observer for joint detection and localization tasks by use of convolutional neural networks

Weimin Zhou, Mark Anastasio

Proceedings Volume 10952, 1095209 (2019) https://doi.org/10.1117/12.2513016

KEYWORDS: Signal detection, Convolutional neural networks, Binary data, Medical imaging, Imaging systems, Quality measurement, Image quality

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Proceedings Article | 4 March 2019 Presentation + Paper

Learning stochastic object model from noisy imaging measurements using AmbientGANs

Weimin Zhou, Sayantan Bhadra, Frank Brooks, Mark Anastasio

Proceedings Volume 10952, 109520M (2019) https://doi.org/10.1117/12.2512633

KEYWORDS: Signal detection, Stochastic processes, Imaging systems, Radon transform, Image processing, Tomography, Statistical modeling

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Proceedings Article | 4 March 2019 Presentation + Paper

Learning the Hotelling observer for SKE detection tasks by use of supervised learning methods

Weimin Zhou, Hua Li, Mark Anastasio

Proceedings Volume 10952, 1095208 (2019) https://doi.org/10.1117/12.2512607

KEYWORDS: Signal detection, Statistical analysis, Binary data, Machine learning, Signal to noise ratio, Information operations, Matrices, Quality measurement, Image quality, Medical imaging

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Proceedings Article | 4 March 2019 Presentation

A diffuse optical tomography system for whole-brain functional imaging in mice using multiple camera views (Conference Presentation)

Zachary Markow, Matthew Reisman, Adam Bauer, Adam Eggebrecht, Mark Anastasio, Joseph Culver

Proceedings Volume 10874, 108741G (2019) https://doi.org/10.1117/12.2508997

KEYWORDS: Imaging systems, Diffuse optical tomography, Cameras, Functional imaging, Head, Digital micromirror devices, 3D metrology, Profiling, Natural surfaces, Neuroimaging

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Proceedings Article | 4 March 2019 Presentation

Advancements in image reconstruction for photoacoustic computed tomography (Conference Presentation)

Mark Anastasio

Proceedings Volume 10878, 108780V (2019) https://doi.org/10.1117/12.2516264

KEYWORDS: Photoacoustic tomography, Image restoration, Optoacoustics, Tomography, Biomedical optics, Spatial resolution, Ultrasonography, X-ray imaging, X-rays, X-ray computed tomography

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Proceedings Article | 1 March 2019 Paper

Joint-reconstruction-enabled partial-dithering strategy for edge-illumination x-ray phase-contrast tomography

Yujia Chen, Mark Anastasio

Proceedings Volume 10948, 109481P (2019) https://doi.org/10.1117/12.2512568

KEYWORDS: Data acquisition, Tomography, X-rays, Image resolution, Sensors, Refractive index, Spatial resolution, Image quality, Imaging systems, X-ray technology

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SPIE Journal Paper | 28 June 2018

Subspace-based resolution-enhancing image reconstruction method for few-view differential phase-contrast tomography

Huifeng Guan, Charlotte Klara Hagen, Alessandro Olivo, Mark Anastasio

JMI, Vol. 5, Issue 02, 023501, (June 2018) https://doi.org/10.1117/12.10.1117/1.JMI.5.2.023501

KEYWORDS: Image restoration, Tomography, Reconstruction algorithms, Radon transform, X-rays, Data acquisition, Refractive index, Sensors, Imaging systems, Iterative methods

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Proceedings Article | 14 May 2018 Presentation

Development of computed phase-sensitive x-ray imaging technologies for pre-clinical science (Conference Presentation)

Mark Anastasio, Frank Brooks, Sergey Komarov, Yujia Chen, Weimin Zhou

Proceedings Volume 10632, 106320I (2018) https://doi.org/10.1117/12.2309429

KEYWORDS: X-ray imaging, X-ray technology, X-rays, Tissues, 3D image processing, Refractive index, Coherence imaging, Image acquisition, Tomography, In vivo imaging

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Proceedings Article | 11 April 2018 Paper

Full-view 3D imaging system for functional and anatomical screening of the breast

Alexander Oraevsky, Richard Su, Ha Nguyen, James Moore, Yang Lou, Sayantan Bhadra, Luca Forte, Mark Anastasio, Wei Yang

Proceedings Volume 10494, 104942Y (2018) https://doi.org/10.1117/12.2318802

KEYWORDS: Breast, Optoacoustics, Imaging systems, Tumors, Transducers, Ultrasonography, Ultrasonics, 3D image processing, Diagnostics, Tomography

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Laser Optoacoustic Ultrasonic Imaging System Assembly (LOUISA-3D) was developed in response to demand of diagnostic radiologists for an advanced screening system for the breast to improve on low sensitivity of x-ray based modalities of mammography and tomosynthesis in the dense and heterogeneous breast and low specificity magnetic resonance imaging. It is our working hypothesis that co-registration of quantitatively accurate functional images of the breast vasculature and microvasculature, and anatomical images of breast morphological structures will provide a clinically viable solution for the breast cancer care. Functional imaging is LOUISA-3D is enabled by the full view 3D optoacoustic images acquired at two rapidly toggling laser wavelengths in the near-infrared spectral range. 3D images of the breast anatomical background is enabled in LOUISA-3D by a sequence of B-mode ultrasound slices acquired with a transducer array rotating around the breast. This creates the possibility to visualize distributions of the total hemoglobin and blood oxygen saturation within specific morphological structures such as tumor angiogenesis microvasculature and larger vasculature in proximity of the tumor. The system has four major components: (i) a pulsed dual wavelength laser with fiberoptic light delivery system, (ii) an imaging module with two arc shaped probes (optoacoustic and ultrasonic) placed in a transparent bowl that rotates around the breast, (iii) a multichannel electronic system with analog preamplifiers and digital data acquisition boards, and (iv) computer for the system control, data processing and image reconstruction. The most important advancement of this latest system design compared with previously reported systems is the full breast illumination accomplished for each rotational step of the optoacoustic transducer array using fiberoptic illuminator rotating around the breast independently from rotation of the detector probe. We report here a pilot case studies on one healthy volunteer and on patient with a suspicious small lesion in the breast. LOUISA3D visualized deoxygenated veins and oxygenated arteries of a healthy volunteer, indicative of its capability to visualize hypoxic microvasculature in cancerous tumors. A small lesion detected on optoacoustic image of a patient was not visible on ultrasound, potentially indicating high system sensitivity of the optoacoustic subsystem to small but aggressively growing cancerous lesions with high density angiogenesis microvasculature. The main breast vasculature (0.5-1 mm) was visible at depth of up to 40-mm with 0.3-mm resolution. The results of LOUISA-3D pilot clinical validation demonstrated the system readiness for statistically significant clinical feasibility study.

Proceedings Article | 16 March 2018 Presentation + Paper

A 3D imaging system integrating photoacoustic and fluorescence orthogonal projections for anatomical, functional and molecular assessment of rodent models

Hans P. Brecht, Vassili Ivanov, Diego Dumani, Stanislav Emelianov, Mark Anastasio, Sergey Ermilov

Proceedings Volume 10494, 1049411 (2018) https://doi.org/10.1117/12.2290880

KEYWORDS: Imaging systems, Tissue optics, 3D image processing, In vivo imaging, Image resolution, Data acquisition

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Proceedings Article | 15 March 2018 Presentation

Parameterized joint reconstruction of the initial pressure and sound speed distributions in photoacoustic computed tomography (Conference Presentation)

Thomas Paul Matthews, Joemini Poudel, Lei Li, Lihong Wang, Mark Anastasio

Proceedings Volume 10494, 104942V (2018) https://doi.org/10.1117/12.2291014

KEYWORDS: Photoacoustic tomography, Animal model studies, CT reconstruction, Image restoration, Preclinical imaging, In vivo imaging, Computed tomography

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Proceedings Article | 14 March 2018 Presentation

Joint reconstruction of initial pressure distribution and acoustic skull parameters in transcranial photoacoustic computed tomography (Conference Presentation)

Joemini Poudel, Thomas Matthews, Mark Anastasio, Lihong Wang

Proceedings Volume 10494, 104942U (2018) https://doi.org/10.1117/12.2290890

KEYWORDS: Photoacoustic tomography, Skull, Acoustics, Image restoration, CT reconstruction, 3D modeling, Data modeling, Neuroimaging, Photoacoustic spectroscopy, 3D metrology

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Proceedings Article | 14 March 2018 Presentation

Joint image reconstruction of initial pressure distribution and acoustic parameters in elastic media with application to transcranial photoacoustic tomography (Conference Presentation)

Joemini Poudel, Thomas Matthews, Mark Anastasio

Proceedings Volume 10580, 1058004 (2018) https://doi.org/10.1117/12.2293672

KEYWORDS: Photoacoustic tomography, Skull, Acoustics, Image restoration, Reconstruction algorithms, Neuroimaging, Brain imaging, Wave propagation, Photoacoustic spectroscopy, 3D image processing

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Proceedings Article | 9 March 2018 Presentation + Paper

Joint-reconstruction-enabled data acquisition design for single-shot edge-illumination x-ray phase-contrast tomography

Yujia Chen, Weimin Zhou, Mark Anastasio

Proceedings Volume 10573, 1057322 (2018) https://doi.org/10.1117/12.2293621

KEYWORDS: Tomography, Data acquisition, X-rays, Reconstruction algorithms, Image restoration, Refractive index, Image quality, X-ray technology, X-ray imaging

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Proceedings Article | 7 March 2018 Paper

Learning the ideal observer for SKE detection tasks by use of convolutional neural networks (Cum Laude Poster Award)

Weimin Zhou, Mark Anastasio

Proceedings Volume 10577, 1057719 (2018) https://doi.org/10.1117/12.2293772

KEYWORDS: Statistical analysis, Data modeling, Binary data, Signal detection, Convolutional neural networks, Machine learning, Monte Carlo methods, Medical imaging, Image processing

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Proceedings Article | 6 March 2018 Presentation + Paper

An advanced photoacoustic tomography system based on a ring geometry design

Suhail Alshahrani, Yan Yan, Ivan Avrutsky, Eugene Malyarenko, Mark Anastasio, Mohammad Mehrmohammadi

Proceedings Volume 10580, 1058005 (2018) https://doi.org/10.1117/12.2296595

KEYWORDS: Breast, Acquisition tracking and pointing, Breast cancer, Tissue optics, Transducers, Mirrors, Tissues, Signal detection, Mammography, Photoacoustic tomography

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Proceedings Article | 2 March 2018 Paper

Convolutional neural network based automatic plaque characterization for intracoronary optical coherence tomography images

Shenghua He, Jie Zheng, Akiko Maehara, Gary Mintz, Dalin Tang, Mark Anastasio, Hua Li

Proceedings Volume 10574, 1057432 (2018) https://doi.org/10.1117/12.2293957

KEYWORDS: Optical coherence tomography, Tissues, Fourier transforms, Image classification, Network architectures, Tissue optics, Image processing, Molybdenum, Convolutional neural networks, Lithium

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SPIE Journal Paper | 16 October 2017 Open Access

Recent advances in edge illumination x-ray phase-contrast tomography

Anna Zamir, Charlotte Hagen, Paul Diemoz, Marco Endrizzi, Fabio Vittoria, Yujia Chen, Mark Anastasio, Alessandro Olivo

JMI, Vol. 4, Issue 04, 040901, (October 2017) https://doi.org/10.1117/12.10.1117/1.JMI.4.4.040901

KEYWORDS: X-rays, Refraction, Signal attenuation, Tomography, X-ray computed tomography, Sensors, Synchrotron radiation, X-ray imaging, Computed tomography, Synchrotrons

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Proceedings Article | 13 March 2017 Presentation + Paper

Joint reconstruction of the sound speed and initial pressure distributions for ultrasound computed tomography and photoacoustic computed tomography

Thomas Matthews, Mark Anastasio

Proceedings Volume 10139, 101390B (2017) https://doi.org/10.1117/12.2254171

KEYWORDS: Photoacoustic tomography, CT reconstruction, Ultrasonography, Computed tomography, Ultrasound tomography, Acoustics, Photoacoustic imaging, Medical image reconstruction, Transducers, Imaging systems, Stochastic processes, Photoacoustic spectroscopy, Wave propagation

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Proceedings Article | 13 March 2017 Presentation + Paper

Image reconstruction for ultrasound computed tomography by use of the regularized dual averaging method

Thomas Matthews, Kun Wang, Cuiping Li, Neb Duric, Mark Anastasio

Proceedings Volume 10139, 101390P (2017) https://doi.org/10.1117/12.2254170

KEYWORDS: Stochastic processes, Ultrasonography, Computed tomography, Ultrasound tomography, CT reconstruction, Computer programming, Optimization (mathematics), Medical image reconstruction, Transducers, Acoustics, Medical imaging

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Proceedings Article | 13 March 2017 Paper

Iterative image reconstruction in elastic inhomogenous media with application to transcranial photoacoustic tomography

Joemini Poudel, Thomas Matthews, Kenji Mitsuhashi, Alejandro Garcia-Uribe, Lihong Wang, Mark Anastasio

Proceedings Volume 10139, 101390C (2017) https://doi.org/10.1117/12.2254141

KEYWORDS: Photoacoustic tomography, Image restoration, Tissue optics, 3D modeling, Ultrasonics, Brain imaging, Wave propagation, Skull, Reconstruction algorithms, Data modeling, Algorithm development, Absorption

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Proceedings Article | 10 March 2017 Presentation + Paper

Task-based image quality assessment in radiation therapy: initial characterization and demonstration with CT simulation images

Steven Dolly, Mark Anastasio, Lifeng Yu, Hua Li

Proceedings Volume 10136, 101360Y (2017) https://doi.org/10.1117/12.2254063

KEYWORDS: Radiotherapy, Image quality, Process modeling, Image segmentation, Computed tomography, Computer simulations, Stochastic processes, X-ray computed tomography, Imaging systems, Image processing

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Proceedings Article | 10 March 2017 Paper

Task-based data-acquisition optimization for sparse image reconstruction systems

Yujia Chen, Yang Lou, Matthew Kupinski, Mark Anastasio

Proceedings Volume 10136, 101360Z (2017) https://doi.org/10.1117/12.2255536

KEYWORDS: Image restoration, Systems modeling, Imaging systems, Magnetic resonance imaging, Reconstruction algorithms, Information operations, Statistical modeling, Compressed sensing, Stochastic processes, Bayesian inference, Signal detection, Data acquisition, Statistical analysis

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Proceedings Article | 9 March 2017 Paper

Estimating posterior image variance with sparsity-based object priors for MRI

Yujia Chen, Yang Lou, Cihat Eldeniz, Hongyu An, Mark Anastasio

Proceedings Volume 10132, 101321J (2017) https://doi.org/10.1117/12.2255555

KEYWORDS: Magnetic resonance imaging, Bayesian inference, Image analysis, Reconstruction algorithms, Image restoration, Probability theory, Medical image reconstruction, Monte Carlo methods, Reliability, Optical imaging, Brain mapping, Imaging systems, Medical imaging, Brain, Image segmentation, Data modeling

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Proceedings Article | 9 March 2017 Presentation + Paper

Learning-based stochastic object models for use in optimizing imaging systems

Steven Dolly, Mark Anastasio, Lifeng Yu, Hua Li

Proceedings Volume 10132, 101321T (2017) https://doi.org/10.1117/12.2254055

KEYWORDS: Data modeling, Systems modeling, Stochastic processes, Imaging systems, Optimization (mathematics), Image quality, Quality measurement, Statistical modeling, X-ray imaging, X-rays, X-ray computed tomography, Natural surfaces, Prostate, Image segmentation

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Proceedings Article | 9 March 2017 Paper

A joint-reconstruction approach for single-shot edge illumination x-ray phase-contrast tomography

Yujia Chen, Huifeng Guan, Charlotte Hagen, Alessandro Olivo, Mark Anastasio

Proceedings Volume 10132, 1013217 (2017) https://doi.org/10.1117/12.2255545

KEYWORDS: Tomography, X-rays, X-ray imaging, Refractive index, Absorption, Image restoration, Reconstruction algorithms, Image analysis, In vivo imaging, Optimization (mathematics), Sensors, Algorithm development, Imaging systems

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Proceedings Article | 9 March 2017 Presentation + Paper

A resolution-enhancing image reconstruction method for few-view differential phase-contrast tomography

Huifeng Guan, Mark Anastasio

Proceedings Volume 10132, 1013216 (2017) https://doi.org/10.1117/12.2255543

KEYWORDS: Image restoration, Tomography, X-rays, Biomedical optics, Reconstruction algorithms, X-ray imaging, Image resolution, Spatial frequencies, Imaging systems, X-ray computed tomography, Radon transform, Data modeling, Medical imaging

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SPIE Journal Paper | 7 March 2017 Open Access

Mitigation of artifacts due to isolated acoustic heterogeneities in photoacoustic computed tomography using a variable data truncation-based reconstruction method

Joemini Poudel, Thomas Matthews, Lei Li, Mark Anastasio, Lihong Wang

JBO, Vol. 22, Issue 04, 041018, (March 2017) https://doi.org/10.1117/12.10.1117/1.JBO.22.4.041018

KEYWORDS: Acoustics, Photoacoustic tomography, Reconstruction algorithms, Transducers, Tissue optics, Image restoration, Bone, Data modeling, CT reconstruction, Image segmentation

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SPIE Journal Paper | 31 January 2017 Open Access

Generation of anatomically realistic numerical phantoms for photoacoustic and ultrasonic breast imaging

Yang Lou, Weimin Zhou, Thomas Matthews, Catherine Appleton, Mark Anastasio

JBO, Vol. 22, Issue 04, 041015, (January 2017) https://doi.org/10.1117/12.10.1117/1.JBO.22.4.041015

KEYWORDS: Breast, Skin, Photoacoustic tomography, Tissues, Tissue optics, Acoustics, Breast imaging, Binary data, Magnetic resonance imaging, Ultrasonics

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SPIE Journal Paper | 26 July 2016 Open Access

Label-free photoacoustic tomography of whole mouse brain structures ex vivo

Lei Li, Jun Xia, Guo Li, Alejandro Garcia-Uribe, Qiwei Sheng, Mark Anastasio, Lihong Wang

NPh, Vol. 3, Issue 03, 035001, (July 2016) https://doi.org/10.1117/12.10.1117/1.NPh.3.3.035001

KEYWORDS: Brain, Neuroimaging, Photoacoustic tomography, Skull, Absorption, Image segmentation, Acoustics, Video, Acquisition tracking and pointing, Brain imaging

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Proceedings Article | 4 April 2016 Paper

Quantification of signal detection performance degradation induced by phase-retrieval in propagation-based x-ray phase-contrast imaging

Cheng-Ying Chou, Mark Anastasio

Proceedings Volume 9783, 97835J (2016) https://doi.org/10.1117/12.2217338

KEYWORDS: Signal detection, Signal to noise ratio, Phase retrieval, X-ray imaging, Absorption, Image quality, Detection theory, Image retrieval, Quality measurement, Sensors

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Proceedings Article | 1 April 2016 Paper

In vivo small animal lung speckle imaging with a benchtop in-line XPC system

A. Garson, S. Gunsten, S. Vasireddi, S. Brody, M. Anastasio

Proceedings Volume 9783, 97834Z (2016) https://doi.org/10.1117/12.2217267

KEYWORDS: Lung, In vivo imaging, Imaging systems, X-rays, Phase contrast, Lung imaging, Speckle imaging, Preclinical imaging, X-ray imaging, Radiography, Speckle, Animal model studies, X-ray sources, Sensors

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Proceedings Article | 29 March 2016 Paper

Joint reconstruction of absorption and refractive properties in propagation-based x-ray phase-contrast tomography via a non-linear image reconstruction algorithm

Yujia Chen, Kun Wang, Doga Gursoy, Carmen Soriano, Francesco De Carlo, Mark Anastasio

Proceedings Volume 9783, 97835H (2016) https://doi.org/10.1117/12.2217443

KEYWORDS: Image restoration, Reconstruction algorithms, Absorption, Tomography, X-ray imaging, X-rays, Phase retrieval, Data modeling, Refractive index, Image retrieval, Sensors

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Proceedings Article | 29 March 2016 Paper

Advanced laser system for 3D optoacoustic tomography of the breast

Marc Klosner, Gary Chan, Chunbai Wu, Donald Heller, Richard Su, Sergey Ermilov, Hans Peter Brecht, Vassili Ivanov, Pratik Talole, Yang Lou, Mark Anastasio, Alexander Oraevsky

Proceedings Volume 9708, 97085B (2016) https://doi.org/10.1117/12.2209398

KEYWORDS: Breast, Laser systems engineering, Absorption, Imaging systems, Pulsed laser operation, Alexandrite lasers, Tissues, Blood, Breast imaging, Optical parametric oscillators

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Proceedings Article | 15 March 2016 Paper

Compensation for acoustic heterogeneities in photoacoustic computed tomography using a variable temporal data truncation reconstruction method

Joemini Poudel, Thomas Matthews, Mark Anastasio, Lihong Wang

Proceedings Volume 9708, 97083W (2016) https://doi.org/10.1117/12.2213416

KEYWORDS: Photoacoustic tomography, Acoustics, Reconstruction algorithms, Tissue optics, Image analysis, Bone, Computed tomography, Ultrasonics, Transducers, Data modeling, Computer simulations

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Proceedings Article | 15 March 2016 Paper

Compensation for air voids in photoacoustic computed tomography image reconstruction

Thomas Matthews, Lei Li, Lihong Wang, Mark Anastasio

Proceedings Volume 9708, 970841 (2016) https://doi.org/10.1117/12.2213307

KEYWORDS: Photoacoustic tomography, Acoustics, CT reconstruction, Imaging systems, Image quality, Water, Preclinical imaging, Lung, Stomach, Transducers, Photography, Numerical stability

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Proceedings Article | 15 March 2016 Paper

Generation of anatomically realistic numerical phantoms for optoacoustic breast imaging

Yang Lou, Kenji Mitsuhashi, Catherine Appleton, Alexander Oraevsky, Mark Anastasio

Proceedings Volume 9708, 97084O (2016) https://doi.org/10.1117/12.2217609

KEYWORDS: Breast, Image segmentation, Breast imaging, Imaging systems, Photoacoustic tomography, Breast cancer, Computer simulations, Computing systems, Tissues, Tissue optics, Skin, Acoustics, Binary data, Magnetic resonance imaging

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Proceedings Article | 15 March 2016 Paper

Application of signal detection theory to assess optoacoustic imaging systems

Yang Lou, Alexander Oraevsky, Mark Anastasio

Proceedings Volume 9708, 97083Z (2016) https://doi.org/10.1117/12.2217928

KEYWORDS: Imaging systems, Detection theory, Tissues, Photoacoustic tomography, Breast, Monte Carlo methods, Signal detection, Tomography, Breast cancer, Mammography, Tumors, Signal to noise ratio, Computing systems, Tissue optics, Transducers, Computer simulations

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Proceedings Article | 18 March 2015 Paper

Small animal lung imaging with an in-line X-ray phase contrast benchtop system

A. Garson, S. Gunsten, H. Guan, S. Vasireddi, S. Brody, M. Anastasio

Proceedings Volume 9412, 94120L (2015) https://doi.org/10.1117/12.2082898

KEYWORDS: Lung, Speckle, X-rays, X-ray imaging, Lung imaging, Imaging systems, Radiography, Optical spheres, Silica, Sensors

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Proceedings Article | 17 March 2015 Paper

Breast ultrasound computed tomography using waveform inversion with source encoding

Kun Wang, Thomas Matthews, Fatima Anis, Cuiping Li, Neb Duric, Mark Anastasio

Proceedings Volume 9419, 94190C (2015) https://doi.org/10.1117/12.2082286

KEYWORDS: Computer programming, Breast, Ultrasonography, Acoustics, Stochastic processes, Reconstruction algorithms, Computed tomography, Spatial resolution, Transducers, Data acquisition

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Proceedings Article | 17 March 2015 Paper

Sparsity-driven ideal observer for computed medical imaging systems

Kun Wang, Yang Lou, Matthew Kupinski, Mark Anastasio

Proceedings Volume 9416, 94161C (2015) https://doi.org/10.1117/12.2082316

KEYWORDS: Information operations, Imaging systems, Statistical analysis, Computing systems, Medical imaging, Statistical modeling, Data acquisition, Bayesian inference, Interference (communication), Magnetic resonance imaging

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Proceedings Article | 11 March 2015 Paper

Three-dimensional laser optoacoustic and laser ultrasound imaging system for biomedical research

Sergey Ermilov, Richard Su, Andre Conjusteau, Tanmayi Oruganti, Kun Wang, Fatima Anis, Mark Anastasio, Alexander Oraevsky

Proceedings Volume 9323, 93230N (2015) https://doi.org/10.1117/12.2085028

KEYWORDS: Imaging systems, Ultrasonography, 3D image processing, Absorption, Acoustics, Transducers, Laser systems engineering, Reconstruction algorithms, Kidney, Tomography

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Proceedings Article | 11 March 2015 Paper

Effect of rotating partial illumination on image reconstruction for optoacoustic breast tomography

Yang Lou, Vyacheslav Nadvoretskiy, Kun Wang, Sergey Emilov, Alexander Oraevsky, Mark Anastasio

Proceedings Volume 9323, 93233L (2015) https://doi.org/10.1117/12.2084909

KEYWORDS: Breast, Transducers, Monte Carlo methods, Image restoration, Absorption, Tomography, Acoustics, Mammography, Imaging systems, Data modeling

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Proceedings Article | 11 March 2015 Paper

Accelerated iterative image reconstruction in three-dimensional optoacoustic tomography

Fatima Anis, Kun Wang, Richard Su, Sergey Ermilov, Alexander Oraevsky, Mark Anastasio

Proceedings Volume 9323, 932315 (2015) https://doi.org/10.1117/12.2084087

KEYWORDS: Image restoration, Reconstruction algorithms, Transducers, 3D image processing, Tomography, Imaging systems, Image quality, Algorithm development, 3D modeling, Image analysis

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Proceedings Article | 11 March 2015 Paper

Photoacoustic computed tomography without accurate ultrasonic transducer responses

Qiwei Sheng, Kun Wang, Jun Xia, Liren Zhu, Lihong Wang, Mark Anastasio

Proceedings Volume 9323, 932313 (2015) https://doi.org/10.1117/12.2081056

KEYWORDS: Photoacoustic tomography, Reconstruction algorithms, Transducers, Image restoration, Data modeling, CT reconstruction, Iterative methods, Ultrasonics, Imaging systems, Photoacoustic spectroscopy

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Proceedings Article | 11 March 2015 Paper

Image reconstruction in transcranial photoacoustic computed tomography of the brain

Kenji Mitsuhashi, Lihong Wang, Mark Anastasio

Proceedings Volume 9323, 93233B (2015) https://doi.org/10.1117/12.2081050

KEYWORDS: Photoacoustic tomography, Reconstruction algorithms, Skull, Image restoration, Neuroimaging, CT reconstruction, Brain imaging, 3D image reconstruction, Brain, Photoacoustic spectroscopy

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Proceedings Article | 11 March 2015 Paper

Synergistic image reconstruction for hybrid ultrasound and photoacoustic computed tomography

Thomas Matthews, Kun Wang, Lihong Wang, Mark Anastasio

Proceedings Volume 9323, 93233A (2015) https://doi.org/10.1117/12.2081048

KEYWORDS: Photoacoustic tomography, Image restoration, Transducers, Ultrasonography, Imaging systems, Image quality, Data modeling, Photoacoustic spectroscopy, Computed tomography, Acoustics

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SPIE Journal Paper | 9 May 2014 Open Access

Fast spatiotemporal image reconstruction based on low-rank matrix estimation for dynamic photoacoustic computed tomography

Kun Wang, Jun Xia, Changhui Li, Lihong Wang, Mark Anastasio

JBO, Vol. 19, Issue 05, 056007, (May 2014) https://doi.org/10.1117/12.10.1117/1.JBO.19.5.056007

KEYWORDS: Photoacoustic tomography, Image restoration, Reconstruction algorithms, Principal component analysis, Image filtering, Transducers, Biological research, Imaging systems, Video, Linear filtering

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Proceedings Article | 19 March 2014 Paper

Depth resolution properties of in-line X-ray phase-contrast tomosynthesis

Huifeng Guan, Qiaofeng Xu, Alfred Garson, Mark Anastasio

Proceedings Volume 9033, 90330H (2014) https://doi.org/10.1117/12.2044009

KEYWORDS: Optical spheres, X-rays, Reconstruction algorithms, Sensors, Tomography, Algorithm development, X-ray imaging, Tissues, Image resolution, Spatial resolution

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Proceedings Article | 19 March 2014 Paper

A multi-channel image reconstruction method for grating-based X-ray phase-contrast computed tomography

Qiaofeng Xu, Alex Sawatzky, Mark Anastasio

Proceedings Volume 9033, 90330D (2014) https://doi.org/10.1117/12.2043732

KEYWORDS: Absorption, Reconstruction algorithms, Algorithm development, Tomography, Image restoration, X-ray imaging, X-rays, Sensors, Scattering, Statistical analysis

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Proceedings Article | 19 March 2014 Paper

In-line X-ray phase-contrast lung imaging In situ with a benchtop system

A. Garson, E. Izaguirre, S. Price, H. Guan, S. Vasireddi, M. Anastasio

Proceedings Volume 9033, 90330A (2014) https://doi.org/10.1117/12.2043936

KEYWORDS: Lung, Imaging systems, X-rays, Lung imaging, Speckle, X-ray imaging, Visibility, Tissues, Bone, Synchrotrons

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Proceedings Article | 3 March 2014 Paper

Acoustic-speed correction of photoacoustic tomography by ultrasonic computed tomography based on optical excitation of elements of a full-ring transducer array

Jun Xia, Chao Huang, Konstantin Maslov, Mark Anastasio, Lihong Wang

Proceedings Volume 8943, 89432G (2014) https://doi.org/10.1117/12.2036615

KEYWORDS: Photoacoustic tomography, Ultrasonics, Transducers, Acoustics, Computed tomography, Photoacoustic spectroscopy, Brain, Brain mapping, Optical components, Tissues

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Proceedings Article | 3 March 2014 Paper

3D laser optoacoustic ultrasonic imaging system for research in mice (LOUIS-3DM)

Sergey Ermilov, Richard Su, Andre Conjusteau, Vassili Ivanov, Vyacheslav Nadvoretskiy, Tanmayi Oruganti, Pratik Talole, Fatima Anis, Mark Anastasio, Alexander Oraevsky

Proceedings Volume 8943, 89430J (2014) https://doi.org/10.1117/12.2044817

KEYWORDS: Ultrasonography, Imaging systems, Acoustics, 3D image processing, Transducers, Receivers, Tomography, Visualization, Reconstruction algorithms, Prototyping

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Proceedings Article | 3 March 2014 Paper

Investigation of effective system designs for transcranial photoacoustic tomography of the brain

Kenji Mitsuhashi, Robert Schoonover, Chao Huang, Lihong Wang, Mark Anastasio

Proceedings Volume 8943, 894369 (2014) https://doi.org/10.1117/12.2041998

KEYWORDS: Skull, Photoacoustic tomography, Photoacoustic spectroscopy, Imaging systems, Image resolution, Transducers, Wave propagation, Electroluminescent displays, Bone, Absorption

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Proceedings Article | 3 March 2014 Paper

Investigation of the adjoint-state method for ultrasound computed tomography: a numerical and experimental study

Fatima Anis, Yang Lou, André Conjusteau, Richard Su, Tanmayi Oruganti, Sergey Ermilov, Alexander Oraevsky, Mark Anastasio

Proceedings Volume 8943, 894337 (2014) https://doi.org/10.1117/12.2042636

KEYWORDS: Ultrasonography, Reconstruction algorithms, Computed tomography, Imaging systems, Breast imaging, Biomedical optics, Transducers, CT reconstruction, Image restoration, Tomography

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Proceedings Article | 3 March 2014 Paper

Low-rank matrix estimation-based spatio-temporal image reconstruction for dynamic photoacoustic computed tomography

Kun Wang, Jun Xia, Changhui Li, Lihong Wang, Mark Anastasio

Proceedings Volume 8943, 89432I (2014) https://doi.org/10.1117/12.2041850

KEYWORDS: Photoacoustic tomography, Image restoration, Reconstruction algorithms, Transducers, Image analysis, Imaging systems, Algorithm development, Data acquisition, CT reconstruction, Tomography

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Proceedings Article | 3 March 2014 Paper

Simultaneous reconstruction of absorbed optical energy density and speed of sound distributions in photoacoustic computed tomography

Chao Huang, Kun Wang, Robert Schoonover, Lihong Wang, Mark Anastasio

Proceedings Volume 8943, 894360 (2014) https://doi.org/10.1117/12.2041825

KEYWORDS: Photoacoustic tomography, Reconstruction algorithms, Transducers, Optimization (mathematics), Algorithm development, Computer simulations, Photoacoustic spectroscopy, Image restoration, Biomedical optics, Tomography

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SPIE Journal Paper | 6 January 2014 Open Access

Retrospective respiration-gated whole-body photoacoustic computed tomography of mice

Jun Xia, Wanyi Chen, Konstantin Maslov, Mark Anastasio, Lihong Wang

JBO, Vol. 19, Issue 01, 016003, (January 2014) https://doi.org/10.1117/12.10.1117/1.JBO.19.1.016003

KEYWORDS: Photoacoustic spectroscopy, Video, Data acquisition, Photoacoustic tomography, Acquisition tracking and pointing, Kidney, Skin, Sensors, CT reconstruction, Image segmentation

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Proceedings Article | 4 March 2013 Paper

Iterative image reconstruction in photoacoustic tomography using Kaiser-Bessel windows

Robert Schoonover, Kun Wang, Mark Anastasio

Proceedings Volume 8581, 85814X (2013) https://doi.org/10.1117/12.2009027

KEYWORDS: Reconstruction algorithms, Photoacoustic tomography, Image restoration, Transducers, Data modeling, Fourier transforms, Chemical elements, Solids, Spherical lenses, Optical spheres

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Proceedings Article | 4 March 2013 Paper

Image reconstruction in photoacoustic tomography with heterogeneous media using an iterative method

Chao Huang, Kun Wang, Lihong Wang, Mark Anastasio

Proceedings Volume 8581, 858106 (2013) https://doi.org/10.1117/12.2007072

KEYWORDS: Photoacoustic tomography, Image restoration, Acoustics, Iterative methods, Reconstruction algorithms, Transducers, Computer simulations, Imaging systems, CT reconstruction, Data modeling

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Proceedings Article | 4 March 2013 Paper

A simple Fourier transform-based reconstruction formula for photoacoustic computed tomography with a circular or spherical measurement geometry

Kun Wang, Mark Anastasio

Proceedings Volume 8581, 85814K (2013) https://doi.org/10.1117/12.2007340

KEYWORDS: Photoacoustic tomography, Spherical lenses, 3D metrology, Image restoration, Tomography, Reconstruction algorithms, 3D image processing, Biomedical optics, Image analysis, Spatial frequencies

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Proceedings Article | 4 March 2013 Paper

3D laser optoacoustic ultrasonic imaging system for preclinical research

Sergey Ermilov, André Conjusteau, Travis Hernandez, Richard Su, Vyacheslav Nadvoretskiy, Dmitri Tsyboulski, Fatima Anis, Mark Anastasio, Alexander Oraevsky

Proceedings Volume 8581, 85810N (2013) https://doi.org/10.1117/12.2008811

KEYWORDS: Ultrasonography, Imaging systems, Acoustics, 3D image processing, Tomography, Visualization, Signal attenuation, 3D modeling, Ultrasound tomography, Pre-clinical research

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Proceedings Article | 4 March 2013 Paper

Image reconstruction and system optimization for three-dimensional speed of sound tomography using laser-induced ultrasound

Fatima Anis, Richard Su, Vyacheslav Nadvoretsky, André Conjusteau, Sergey Ermilov, Alexander Oraevsky, Mark Anastasio

Proceedings Volume 8581, 85814T (2013) https://doi.org/10.1117/12.2008813

KEYWORDS: Imaging systems, Ultrasonography, Transducers, 3D image processing, Tomography, Image restoration, Receivers, Reconstruction algorithms, 3D modeling, Biomedical optics

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SPIE Journal Paper | 2 November 2012 Open Access

Photoacoustic tomography through a whole adult human skull with a photon recycler

Liming Nie, Xin Cai, Konstantin Maslov, Alejandro Garcia-Uribe, Mark Anastasio, Lihong Wang

JBO, Vol. 17, Issue 11, 110506, (November 2012) https://doi.org/10.1117/12.10.1117/1.JBO.17.11.110506

KEYWORDS: Skull, Acquisition tracking and pointing, Brain, Photoacoustic tomography, Ultrasonography, Neuroimaging, Signal to noise ratio, Brain imaging, Light scattering, Photoacoustic spectroscopy

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SPIE Journal Paper | 6 July 2012 Open Access

Tumor glucose metabolism imaged in vivo in small animals with whole-body photoacoustic computed tomography

Muhammad Chatni, Jun Xia, Konstantin Maslov, Zijian Guo, Kun Wang, Mark Anastasio, Lihong Wang, Rebecca Sohn, Jeffrey Arbeit, Yu Zhang, Younan Xia

JBO, Vol. 17, Issue 7, 076012, (July 2012) https://doi.org/10.1117/12.10.1117/1.JBO.17.7.076012

KEYWORDS: Tumors, Glucose, Mode conditioning cables, Imaging systems, In vivo imaging, Kidney, Absorption, Photoacoustic tomography, Reconstruction algorithms, Photoacoustic spectroscopy

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SPIE Journal Paper | 5 June 2012 Open Access

Aberration correction for transcranial photoacoustic tomography of primates employing adjunct image data

Chao Huang, Liming Nie, Robert Schoonover, Zijian Guo, Carsten Schirra, Mark Anastasio, Lihong Wang

JBO, Vol. 17, Issue 06, 066016, (June 2012) https://doi.org/10.1117/12.10.1117/1.JBO.17.6.066016

KEYWORDS: Skull, Reconstruction algorithms, Acquisition tracking and pointing, Image restoration, Acoustics, X-ray computed tomography, X-rays, Brain, Photoacoustic tomography, Neuroimaging

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SPIE Journal Paper | 4 June 2012 Open Access

Special Section Guest Editorial: Photoacoustic Imaging and Sensing

Mark Anastasio, Paul Beard

JBO, Vol. 17, Issue 6, 061201, (June 2012) https://doi.org/10.1117/12.10.1117/1.JBO.17.6.061201

KEYWORDS: Photoacoustic imaging, Tissue optics, Imaging systems, Photoacoustic tomography, Biomedical optics, Ultrasonics, Photoacoustic spectroscopy, Soft tissue optics, Transducers, Spatial resolution

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SPIE Journal Paper | 10 May 2012 Open Access

Numerical investigation of the effects of shear waves in transcranial photoacoustic tomography with a planar geometry

Robert Schoonover, Lihong Wang, Mark Anastasio

JBO, Vol. 17, Issue 6, 061215, (May 2012) https://doi.org/10.1117/12.10.1117/1.JBO.17.6.061215

KEYWORDS: Solids, Acquisition tracking and pointing, Photoacoustic tomography, Absorption, Acoustics, Bone, Reconstruction algorithms, Interfaces, Optical spheres, Wave propagation

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SPIE Journal Paper | 7 May 2012 Open Access

Photoacoustic computed tomography correcting for heterogeneity and attenuation

Chao Huang, Liming Nie, Robert Schoonover, Lihong Wang, Mark Anastasio

JBO, Vol. 17, Issue 6, 061211, (May 2012) https://doi.org/10.1117/12.10.1117/1.JBO.17.6.061211

KEYWORDS: Signal attenuation, Acoustics, Reconstruction algorithms, Photoacoustic tomography, Solids, Image restoration, Absorption, Bone, Transducers, Biomedical optics

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SPIE Journal Paper | 4 May 2012 Open Access

Whole-body ring-shaped confocal photoacoustic computed tomography of small animals in vivo

Jun Xia, Muhammad Chatni, Konstantin Maslov, Zijian Guo, Kun Wang, Mark Anastasio, Lihong Wang

JBO, Vol. 17, Issue 5, 050506, (May 2012) https://doi.org/10.1117/12.10.1117/1.JBO.17.5.050506

KEYWORDS: Imaging systems, Photoacoustic tomography, Signal detection, Confocal microscopy, In vivo imaging, Video, Image restoration, Computing systems, Kidney, Bladder

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Proceedings Article | 23 February 2012 Paper

Investigation of iterative image reconstruction in optoacoustic tomography

Kun Wang, Richard Su, Alexander Oraevsky, Mark Anastasio

Proceedings Volume 8223, 82231Y (2012) https://doi.org/10.1117/12.909610

KEYWORDS: Reconstruction algorithms, Image restoration, 3D image processing, 3D image reconstruction, Transducers, Algorithm development, Tomography, Data modeling, 3D modeling, Statistical modeling

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Proceedings Article | 23 February 2012 Paper

Analysis of the role of shear waves in transcranial photoacoustic tomography with a planar geometry

Robert Schoonover, Lihong Wang, Mark Anastasio

Proceedings Volume 8223, 822320 (2012) https://doi.org/10.1117/12.907389

KEYWORDS: Solids, Acquisition tracking and pointing, Reconstruction algorithms, Thallium, Acoustics, Absorption, Photoacoustic tomography, Wave propagation, Bone, Photoacoustic spectroscopy

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Proceedings Article | 23 February 2012 Paper

Transcranial photoacoustic tomography of the monkey brain

Liming Nie, Chao Huang, Zijian Guo, Mark Anastasio, Lihong Wang

Proceedings Volume 8223, 82230L (2012) https://doi.org/10.1117/12.907060

KEYWORDS: Skull, Acquisition tracking and pointing, Brain, Transducers, Reconstruction algorithms, Neuroimaging, Sensors, Blood vessels, Ultrasonics, Acoustics

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Proceedings Article | 23 February 2012 Paper

Ring-shaped confocal photoacoustic computed tomography for small-animal whole-body imaging

Jun Xia, Muhammad Chatni, Konstantin Maslov, Zijian Guo, Mark Anastasio, Lihong Wang

Proceedings Volume 8223, 82230G (2012) https://doi.org/10.1117/12.906056

KEYWORDS: Transducers, Photoacoustic tomography, Whole body imaging, Imaging systems, Data acquisition, Ultrasonics, In vivo imaging, Photoacoustic spectroscopy, 3D image reconstruction, Confocal microscopy

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Proceedings Article | 23 February 2012 Paper

Sparsity regularized data-space restoration in optoacoustic tomography

Kun Wang, Richard Su, Alexander Oraevsky, Mark Anastasio

Proceedings Volume 8223, 822322 (2012) https://doi.org/10.1117/12.909690

KEYWORDS: Reconstruction algorithms, Image restoration, Transducers, Deconvolution, Photoacoustic tomography, Tomography, 3D image processing, Data acquisition, Data modeling, Wavelets

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Proceedings Article | 23 February 2012 Paper

Compensation of shear waves in photoacoustic tomography with layered acoustic media

Robert Schoonover, Mark Anastasio

Proceedings Volume 8223, 82232P (2012) https://doi.org/10.1117/12.907391

KEYWORDS: Solids, Acoustics, Reconstruction algorithms, Image restoration, Fourier transforms, Photoacoustic tomography, Algorithm development, Optical spheres, Wave propagation, Computer simulations

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Proceedings Article | 28 February 2011 Paper

Limited data image reconstruction in optoacoustic tomography by constrained total variation minimization

Kun Wang, Emil Sidky, Mark Anastasio, Alexander Oraevsky, Xiaochuan Pan

Proceedings Volume 7899, 78993U (2011) https://doi.org/10.1117/12.875664

KEYWORDS: Reconstruction algorithms, Transducers, Image restoration, Tomography, Algorithm development, Ultrasonics, Ultrasonography, Spherical lenses, 3D modeling, Biomedical optics

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Proceedings Article | 28 February 2011 Paper

Estimate of effective singular values of a photoacoustic imaging system by noise characterization

Michael Roumeliotis, Mark Anastasio, Jeffrey J. Carson

Proceedings Volume 7899, 78991U (2011) https://doi.org/10.1117/12.873570

KEYWORDS: Imaging systems, Photoacoustic spectroscopy, Transducers, Photoacoustic imaging, Interference (communication), Ultrasonography, Pulsed laser operation, Acoustics, Optical properties, Optical imaging

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Proceedings Article | 23 February 2011 Paper

A layered media approach to photoacoustic tomography

Robert Schoonover, Mark Anastasio

Proceedings Volume 7899, 78993P (2011) https://doi.org/10.1117/12.875562

KEYWORDS: Acoustics, Photoacoustic tomography, Reconstruction algorithms, Acquisition tracking and pointing, Biomedical optics, Absorption, Tissue optics, Solids, Algorithm development, Image restoration

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Proceedings Article | 16 September 2010 Paper

3D photoacoustic imaging

Jeffrey J. L. Carson, Michael Roumeliotis, Govind Chaudhary, Robert Stodilka, Mark Anastasio

Proceedings Volume 7750, 775009 (2010) https://doi.org/10.1117/12.872720

KEYWORDS: Photoacoustic imaging, 3D image processing, Imaging systems, Reconstruction algorithms, Transducers, 3D acquisition, Photoacoustic spectroscopy, Image restoration, 3D applications, Pulsed laser operation

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Our group has concentrated on development of a 3D photoacoustic imaging system for biomedical imaging research. The technology employs a sparse parallel detection scheme and specialized reconstruction software to obtain 3D optical images using a single laser pulse. With the technology we have been able to capture 3D movies of translating point targets and rotating line targets. The current limitation of our 3D photoacoustic imaging approach is its inability ability to reconstruct complex objects in the field of view. This is primarily due to the relatively small number of projections used to reconstruct objects. However, in many photoacoustic imaging situations, only a few objects may be present in the field of view and these objects may have very high contrast compared to background. That is, the objects have sparse properties. Therefore, our work had two objectives: (i) to utilize mathematical tools to evaluate 3D photoacoustic imaging performance, and (ii) to test image reconstruction algorithms that prefer sparseness in the reconstructed images. Our approach was to utilize singular value decomposition techniques to study the imaging operator of the system and evaluate the complexity of objects that could potentially be reconstructed. We also compared the performance of two image reconstruction algorithms (algebraic reconstruction and l1-norm techniques) at reconstructing objects of increasing sparseness. We observed that for a 15-element detection scheme, the number of measureable singular vectors representative of the imaging operator was consistent with the demonstrated ability to reconstruct point and line targets in the field of view. We also observed that the l1-norm reconstruction technique, which is known to prefer sparseness in reconstructed images, was superior to the algebraic reconstruction technique. Based on these findings, we concluded (i) that singular value decomposition of the imaging operator provides valuable insight into the capabilities of a 3D photoacoustic imaging system, and (ii) that reconstruction algorithms which favor sparseness can significantly improve imaging performance. These methodologies should provide a means to optimize detector count and geometry for a multitude of 3D photoacoustic imaging applications.

Proceedings Article | 27 August 2010 Paper

A layered media approach to photoacoustic tomography

Robert Schoonover, Mark Anastasio

Proceedings Volume 7800, 780003 (2010) https://doi.org/10.1117/12.860887

KEYWORDS: Acoustics, Photoacoustic tomography, Acquisition tracking and pointing, Image restoration, Reconstruction algorithms, Absorption, Biomedical optics, Inverse optics, Data acquisition, Algorithm development

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Proceedings Article | 27 August 2010 Paper

Investigation of limited-view image reconstruction in optoacoustic tomography employing a priori structural information

Chao Huang, Alexander Oraevsky, Mark Anastasio

Proceedings Volume 7800, 780004 (2010) https://doi.org/10.1117/12.861005

KEYWORDS: Reconstruction algorithms, Image restoration, Tomography, Algorithm development, Transducers, Image quality, Ultrasonography, Detection and tracking algorithms, Ultrasonics, Data modeling

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Proceedings Article | 19 March 2010 Paper

Contributions to ideal observer SNRs in propagation-based x-ray phase-contrast imaging

Mark Anastasio, Cheng-Ying Chou, Adam Zysk, Jovan Brankov

Proceedings Volume 7622, 76220S (2010) https://doi.org/10.1117/12.844489

KEYWORDS: X-rays, Signal detection, Signal to noise ratio, X-ray imaging, Imaging systems, Tissues, Absorption, Detection theory, Radio propagation, Signal attenuation

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SPIE Journal Paper | 1 March 2010 Open Access

Image reconstruction in photoacoustic tomography with variable speed of sound using a higher-order geometrical acoustics approximation

Dimple Modgil, Mark Anastasio, Patrick La Rivière

JBO, Vol. 15, Issue 02, 021308, (March 2010) https://doi.org/10.1117/12.10.1117/1.3333550

KEYWORDS: Acoustics, Image restoration, Transducers, Photoacoustic tomography, Lanthanum, Absorption, Image quality, Reconstruction algorithms, 3D modeling, Acquisition tracking and pointing

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Proceedings Article | 24 February 2010 Paper

Analysis of a photoacoustic imaging system by singular value decomposition

Michael Roumeliotis, Govind Chaudhary, Mark Anastasio, Robert Stodilka, Andrea Immucci, Eldon Ng, Jeffrey J. Carson

Proceedings Volume 7564, 756432 (2010) https://doi.org/10.1117/12.842306

KEYWORDS: Imaging systems, Transducers, Photoacoustic imaging, Calibration, Data acquisition, Acoustics, Photoacoustic spectroscopy, Pulsed laser operation, Optical imaging, Computer programming

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Proceedings Article | 24 February 2010 Paper

Comparison of reconstruction algorithms for sparse-array detection photoacoustic tomography

G. Chaudhary, M. Roumeliotis, J. J. Carson, M. Anastasio

Proceedings Volume 7564, 756434 (2010) https://doi.org/10.1117/12.842607

KEYWORDS: Reconstruction algorithms, Acquisition tracking and pointing, Transducers, Image restoration, Photoacoustic tomography, Imaging systems, 3D image processing, Tissue optics, Acoustics, 3D metrology

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Proceedings Article | 24 February 2010 Paper

Characterization of sparse-array detection photoacoustic tomography using the singular value decomposition

G. Chaudhary, M. Roumeliotis, P. Ephrat, R. Stodilka, J. J. Carson, M. Anastasio

Proceedings Volume 7564, 756438 (2010) https://doi.org/10.1117/12.842663

KEYWORDS: Transducers, Acquisition tracking and pointing, Imaging systems, Photoacoustic tomography, Computing systems, Matrices, Sensors, Chemical elements, Ultrasonics, Tissue optics

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Proceedings Article | 24 February 2010 Paper

Compensation for transducer effects in optoacoustic tomography

Kun Wang, Mark Anastasio, Sergey Ermilov, Hans-Peter Brecht, Richard Su, Alexander Oraevsky

Proceedings Volume 7564, 75640P (2010) https://doi.org/10.1117/12.842459

KEYWORDS: Transducers, Reconstruction algorithms, Tomography, Image restoration, 3D modeling, Ultrasonography, Image resolution, Data modeling, Optical tomography, Optical imaging

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Proceedings Article | 14 March 2009 Paper

Analysis of the spectrum in phase-contrast mammography

Adam Zysk, Qiaofeng Xu, Luis de Sisternes, Jovan Brankov, Miles Wernick, Mark Anastasio

Proceedings Volume 7258, 72584E (2009) https://doi.org/10.1117/12.812146

KEYWORDS: Mammography, Breast, X-rays, Molybdenum, Absorption, Sensors, Tissues, X-ray imaging, Optical filters, Radiography

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Proceedings Article | 14 March 2009 Paper

Investigation of quantitative polychromatic x-ray phase-contrast tomography for tissue characterization

Cheng-Ying Chou, Adam Zysk, Mark Anastasio

Proceedings Volume 7258, 72584B (2009) https://doi.org/10.1117/12.812335

KEYWORDS: X-rays, Tomography, Tissues, X-ray imaging, Sensors, Refractive index, Reconstruction algorithms, X-ray characterization, Image restoration, Diagnostics

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Proceedings Article | 13 March 2009 Paper

Boundary reconstruction in limited-angle x-ray phase-contrast tomography

Mark Anastasio, Emil Sidky, Xiaochuan Pan, Cheng-Ying Chou

Proceedings Volume 7258, 725827 (2009) https://doi.org/10.1117/12.811918

KEYWORDS: Reconstruction algorithms, X-rays, Tomography, X-ray imaging, Image restoration, Algorithm development, Data modeling, Tissues, 3D image processing, Refractive index

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Proceedings Article | 25 February 2009 Paper

Image reconstruction in photoacoustic tomography with variable speed of sound using a higher order geometrical acoustics approximation

Dimple Modgil, Mark Anastasio, Kun Wang, Patrick LaRivière

Proceedings Volume 7177, 71771A (2009) https://doi.org/10.1117/12.809001

KEYWORDS: Acoustics, Image restoration, Photoacoustic tomography, Absorption, Image quality, Transducers, Acquisition tracking and pointing, Radon transform, Distortion, Algorithm development

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Proceedings Article | 25 February 2009 Paper

Photoacoustic image reconstruction in an attenuating medium using singular value decomposition

Dimple Modgil, Mark Anastasio, Patrick La Riviere

Proceedings Volume 7177, 71771B (2009) https://doi.org/10.1117/12.809030

KEYWORDS: Image restoration, Signal attenuation, Absorption, Fourier transforms, Reconstruction algorithms, Acquisition tracking and pointing, Photoacoustic tomography, Photoacoustic spectroscopy, Tissue optics, Acoustics

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Proceedings Article | 17 September 2008 Paper

Validity of a fully coherent field model for in-line x-ray phase imaging

Adam Zysk, Miles Wernick, Mark Anastasio

Proceedings Volume 7078, 70781A (2008) https://doi.org/10.1117/12.793872

KEYWORDS: Wave propagation, X-ray imaging, X-rays, Geometrical optics, Systems modeling, Refractive index, Phase imaging, Statistical modeling, Imaging systems, X-ray microscopy

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Proceedings Article | 16 September 2008 Paper

Noise properties of in-line x-ray imaging and tomography

Cheng-Ying Chou, Mark Anastasio

Proceedings Volume 7078, 707813 (2008) https://doi.org/10.1117/12.795525

KEYWORDS: Tomography, Sensors, X-ray imaging, X-rays, Refractive index, Absorption, Statistical analysis, Reconstruction algorithms, Imaging systems, Signal attenuation

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Proceedings Article | 16 September 2008 Paper

X-ray phase-contrast local tomography image reconstruction on pi-lines

Mark Anastasio, Daxin Shi, Xiaochuan Pan

Proceedings Volume 7078, 707812 (2008) https://doi.org/10.1117/12.795517

KEYWORDS: Tomography, Reconstruction algorithms, X-rays, X-ray imaging, Image restoration, Refractive index, Algorithm development, Imaging systems, CT reconstruction, Sensors

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Proceedings Article | 5 September 2008 Paper

Multi-spectral intensity diffraction tomography

Mark Anastasio, Qiaofeng Xu, Daxin Shi

Proceedings Volume 7076, 707605 (2008) https://doi.org/10.1117/12.795503

KEYWORDS: Tomography, Sensors, Diffraction, Refractive index, Scattering, Reconstruction algorithms, Imaging systems, Image restoration, Data acquisition, Spatial frequencies

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Proceedings Article | 18 March 2008 Paper

Region-of-interest image reconstruction in x-ray differential phase-contrast tomography

Mark Anastasio, Dan Xia, Xiaochuan Pan

Proceedings Volume 6913, 691327 (2008) https://doi.org/10.1117/12.771431

KEYWORDS: Reconstruction algorithms, X-rays, Tomography, Algorithm development, Image restoration, X-ray imaging, Refractive index, Sensors, Geometrical optics, Wave propagation

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Proceedings Article | 18 March 2008 Paper

Influence of imaging geometry on noise texture in x-ray in-line phase-contrast imaging

Cheng-Ying Chou, Mark Anastasio

Proceedings Volume 6913, 69131Z (2008) https://doi.org/10.1117/12.771756

KEYWORDS: X-rays, X-ray imaging, Sensors, Signal detection, Absorption, Refractive index, Statistical analysis, Imaging systems, Reconstruction algorithms, Image analysis

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Proceedings Article | 28 February 2008 Paper

Improving limited-view reconstruction in photoacoustic tomography by incorporating a priori boundary information

Mark Anastasio, Kun Wang, Jin Zhang, Gabe Kruger, Daniel Reinecke, Robert Kruger

Proceedings Volume 6856, 68561B (2008) https://doi.org/10.1117/12.764178

KEYWORDS: Reconstruction algorithms, Transducers, Acquisition tracking and pointing, Photoacoustic spectroscopy, Absorption, Photoacoustic tomography, Biomedical optics, Ultrasonography, Tomography, Tissue optics

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Proceedings Article | 28 February 2008 Paper

Elucidation of 2D and 3D photoacoustic tomography

Kun Wang, Jin Zhang, Mark Anastasio

Proceedings Volume 6856, 68561E (2008) https://doi.org/10.1117/12.764157

KEYWORDS: Transducers, Acquisition tracking and pointing, 3D metrology, Reconstruction algorithms, Ultrasonography, 3D image processing, Radon transform, Spherical lenses, Photoacoustic tomography, Photoacoustic spectroscopy

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Proceedings Article | 28 February 2008 Paper

Simultaneous reconstruction of speed-of-sound and optical absorption properties in photoacoustic tomography via a time-domain iterative algorithm

Jin Zhang, Kun Wang, Yongyi Yang, Mark Anastasio

Proceedings Volume 6856, 68561F (2008) https://doi.org/10.1117/12.764171

KEYWORDS: Acquisition tracking and pointing, Reconstruction algorithms, Absorption, Photoacoustic tomography, Data modeling, Acoustics, Spherical lenses, Ultrasonography, Radon transform, Transducers

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Proceedings Article | 28 February 2008 Paper

Multi-bandwidth image reconstruction in photoacoustic tomography

Cheng-Ying Chou, Mark Anastasio, Jin Zhang, Geng Ku, Xinmai Yang, Lihong Wang

Proceedings Volume 6856, 68561G (2008) https://doi.org/10.1117/12.764127

KEYWORDS: Transducers, Photoacoustic spectroscopy, Acquisition tracking and pointing, Signal to noise ratio, Ultrasonics, Photoacoustic tomography, Absorption, 3D image reconstruction, Statistical analysis, Image restoration

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SPIE Journal Paper | 1 October 2007

Intensity diffraction tomography with fixed detector plane

Daxin Shi, Mark Anastasio

OE, Vol. 46, Issue 10, 107003, (October 2007) https://doi.org/10.1117/12.10.1117/1.2799088

KEYWORDS: Sensors, Tomography, Diffraction, Reconstruction algorithms, Data acquisition, Spherical lenses, Optical engineering, Optical tomography, Refractive index, Computer simulations

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Proceedings Article | 27 February 2007 Paper

Comparison of iterative reconstruction approaches for photoacoustic tomography

Jin Zhang, Mark Anastasio, Patrick La Rivière

Proceedings Volume 6437, 64370Y (2007) https://doi.org/10.1117/12.700930

KEYWORDS: Reconstruction algorithms, Data modeling, Acquisition tracking and pointing, Expectation maximization algorithms, Data integration, Integration, Photoacoustic tomography, Spherical lenses, Radon transform, Absorption

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Proceedings Article | 13 February 2007 Paper

Perspectives in Fourier-based image reconstruction in photoacoustic tomography

Mark Anastasio, Jin Zhang, Daxin Shi, Xiaochuan Pan

Proceedings Volume 6437, 64371D (2007) https://doi.org/10.1117/12.701260

KEYWORDS: Acquisition tracking and pointing, Fourier transforms, Tissue optics, Photoacoustic tomography, Absorption, Photoacoustic spectroscopy, Optical spheres, 3D metrology, Image restoration, Biomedical optics

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Proceedings Article | 7 September 2006 Paper

Statistically optimal image reconstruction in propagation-based phase-contrast tomography

Mark Anastasio, Cheng-Ying Chou, Yin Huang, Daxin Shi

Proceedings Volume 6318, 63180U (2006) https://doi.org/10.1117/12.681301

KEYWORDS: Sensors, Tomography, 3D image reconstruction, X-rays, Wave propagation, Refractive index, Image restoration, Phase retrieval, Computed tomography, Holography

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Proceedings Article | 7 September 2006 Paper

A comparison of a generalized DEI method with multiple-image radiography

Cheng-Ying Chou, Mark Anastasio, Jovan Brankov, Miles Wernick

Proceedings Volume 6318, 631819 (2006) https://doi.org/10.1117/12.681203

KEYWORDS: X-rays, Crystals, Absorption, Computed tomography, Sensors, X-ray imaging, Radiography, X-ray computed tomography, Tissues, Refraction

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Proceedings Article | 5 September 2006 Paper

Statistically optimal image reconstruction for multi-detector intensity diffraction tomography

Yin Huang, Mark Anastasio

Proceedings Volume 6316, 63160K (2006) https://doi.org/10.1117/12.682987

KEYWORDS: Sensors, Tomography, Diffraction, Image restoration, 3D image reconstruction, Scattering, 3D image processing, Reconstruction algorithms, Spatial frequencies, Refractive index

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Proceedings Article | 5 September 2006 Paper

Intensity diffraction tomography with a novel scanning protocol

Daxin Shi, Mark Anastasio

Proceedings Volume 6316, 631607 (2006) https://doi.org/10.1117/12.682850

KEYWORDS: Tomography, Sensors, Spherical lenses, Diffraction, Reconstruction algorithms, Refractive index, Scattering, Algorithm development, Image restoration, Phase retrieval

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Proceedings Article | 31 August 2006 Paper

Three-dimensional image reconstruction in variable density acoustic tomography

Mark Anastasio, Daxin Shi

Proceedings Volume 6316, 631604 (2006) https://doi.org/10.1117/12.682898

KEYWORDS: Tomography, Acoustics, Reconstruction algorithms, Scattering, Image restoration, 3D image processing, Image compression, Sensors, Diffraction, Data acquisition

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Proceedings Article | 7 March 2006 Paper

Ultrasonic attenuation correction in optoacoustic tomography

Patrick La Rivière, Jin Zhang, Mark Anastasio

Proceedings Volume 6086, 60861I (2006) https://doi.org/10.1117/12.647673

KEYWORDS: Signal attenuation, Transducers, Tomography, Ultrasonics, Acoustics, Absorption, Distortion, Fourier transforms, Optical spheres, Signal detection

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Proceedings Article | 6 March 2006 Paper

Reconstruction of speed-of-sound and electromagnetic absorption distributions in photoacoustic tomography

Jin Zhang, Mark Anastasio

Proceedings Volume 6086, 608619 (2006) https://doi.org/10.1117/12.647665

KEYWORDS: Acoustics, Acquisition tracking and pointing, Electromagnetism, Absorption, Reconstruction algorithms, Photoacoustic tomography, Expectation maximization algorithms, Transducers, Spherical lenses, Ultrasonography

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Proceedings Article | 6 March 2006 Paper

Image reconstruction in photoacoustic tomography with truncated cylindrical measurement apertures

Mark Anastasio, Jin Zhang

Proceedings Volume 6086, 608610 (2006) https://doi.org/10.1117/12.647623

KEYWORDS: Acquisition tracking and pointing, Reconstruction algorithms, Tissue optics, Transducers, Expectation maximization algorithms, Image restoration, Photoacoustic tomography, 3D image processing, Absorption, Biomedical optics

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Proceedings Article | 2 March 2006 Paper

Cone-beam local tomography image reconstruction on chords

Mark Anastasio, E. Sidky, Y. Zou, X. Pan

Proceedings Volume 6142, 614226 (2006) https://doi.org/10.1117/12.651736

KEYWORDS: Reconstruction algorithms, Tomography, Image restoration, Image segmentation, Algorithm development, Sensors, Detection and tracking algorithms, CT reconstruction, X-ray computed tomography, 3D modeling

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Proceedings Article | 23 February 2006 Paper

Investigation of 3D microscopy using intensity diffraction tomography

Mark Anastasio, Yin Huang, Greg Gbur, P. Scott Carney

Proceedings Volume 6090, 60900G (2006) https://doi.org/10.1117/12.648169

KEYWORDS: Sensors, Tomography, Microscopy, Diffraction, 3D image processing, Reconstruction algorithms, 3D metrology, Microscopes, Refractive index, Phase measurement

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Proceedings Article | 2 February 2006 Paper

Progress in multiple-image radiography

Miles Wernick, Jovan Brankov, Dean Chapman, Yongyi Yang, Gocha Khelashvili, Mark Anastasio, Zhong Zhong, Christopher Parham, Jun Li, Carol Muehleman

Proceedings Volume 6065, 60650W (2006) https://doi.org/10.1117/12.658076

KEYWORDS: Radiography, Absorption, X-rays, X-ray imaging, Refraction, Tissues, Mammography, Scattering, Imaging systems, Crystals

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Proceedings Article | 25 April 2005 Paper

Mitigating artifacts via half-time reconstruction in thermoacoustic tomography

Mark Anastasio, Jin Zhang, Xiaochuan Pan, Geng Ku, Lihong Wang

Proceedings Volume 5697, (2005) https://doi.org/10.1117/12.589190

KEYWORDS: Acoustics, Expectation maximization algorithms, Reconstruction algorithms, Tomography, Biomedical optics, Image restoration, Electromagnetism, Photoacoustic tomography, Ultrasonography, Spherical lenses

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Proceedings Article | 20 April 2005 Paper

Phase-contrast tomography using tilted detector planes

Mark Anastasio, Daxin Shi

Proceedings Volume 5745, (2005) https://doi.org/10.1117/12.595882

KEYWORDS: Sensors, Tomography, Reconstruction algorithms, Diffraction, Wave propagation, Biomedical optics, X-ray optics, Imaging systems, X-rays, Detector development

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Proceedings Article | 12 April 2005 Paper

Image reconstruction in thermoacoustic tomography with compensation for acoustic heterogeneities

Mark Anastasio, Jin Zhang, Xiaochuan Pan

Proceedings Volume 5750, (2005) https://doi.org/10.1117/12.595866

KEYWORDS: Acoustics, Data modeling, Radon transform, Reconstruction algorithms, Expectation maximization algorithms, Tomography, Electromagnetism, Image restoration, Biomedical optics, Photoacoustic tomography

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Proceedings Article | 12 April 2005 Paper

Image reconstruction in variable density acoustic tomography

Mark Anastasio, Daxin Shi, Thomas Deffieux

Proceedings Volume 5750, (2005) https://doi.org/10.1117/12.596131

KEYWORDS: Reconstruction algorithms, Acoustics, Tomography, Scattering, Image compression, Diffraction, Image restoration, Algorithm development, Ultrasound tomography, Composites

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Proceedings Article | 26 October 2004 Paper

A preliminary study of multiple-image computed tomography

Miles Wernick, Jovan Brankov, Dean Chapman, Yongyi Yang, Carol Muehleman, Zhong Zhong, Mark Anastasio

Proceedings Volume 5535, (2004) https://doi.org/10.1117/12.560211

KEYWORDS: Scattering, X-rays, Refraction, Signal attenuation, Tomography, Reconstruction algorithms, X-ray imaging, Absorption, 3D image reconstruction, Computed tomography

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Proceedings Article | 26 October 2004 Paper

Phase-contrast tomography using incident spherical waves

Mark Anastasio, Daxin Shi, Yin Huang, Greg Gbur

Proceedings Volume 5535, (2004) https://doi.org/10.1117/12.560164

KEYWORDS: Tomography, Reconstruction algorithms, Sensors, Scattering, Refractive index, Algorithm development, Diffraction, Image restoration, Spherical lenses, Fourier transforms

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Proceedings Article | 26 October 2004 Paper

On the relationship between intensity diffraction tomography and phase-contrast tomography

Mark Anastasio, Daxin Shi

Proceedings Volume 5535, (2004) https://doi.org/10.1117/12.560143

KEYWORDS: Tomography, Diffraction, Sensors, Refractive index, Scattering, Reconstruction algorithms, X-rays, Fourier transforms, Phase measurement, X-ray imaging

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Proceedings Article | 26 October 2004 Paper

Quantitative boundary reconstruction from truncated phase-contrast tomographic projections

Daxin Shi, Mark Anastasio, Xiaochuan Pan

Proceedings Volume 5535, (2004) https://doi.org/10.1117/12.560183

KEYWORDS: Tomography, Refractive index, CT reconstruction, Reconstruction algorithms, Absorption, X-ray computed tomography, Imaging systems, Computed tomography, Algorithm development, X-rays

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Proceedings Article | 6 May 2004 Paper

Intensity diffraction tomography for phase objects

Daxin Shi, Mark Anastasio, Yin Huang, Greg Gbur

Proceedings Volume 5368, (2004) https://doi.org/10.1117/12.534461

KEYWORDS: Reconstruction algorithms, Tomography, Sensors, Zirconium, Refractive index, Diffraction, Scattering, Algorithm development, 3D image processing, 3D image reconstruction

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Proceedings Article | 6 May 2004 Paper

Noise properties of intensity diffraction tomography

Mark Anastasio, Yin Huang, Daxin Shi, Greg Gbur

Proceedings Volume 5368, (2004) https://doi.org/10.1117/12.534462

KEYWORDS: Sensors, Tomography, Statistical analysis, Diffraction, Scattering, Reconstruction algorithms, Image restoration, Stochastic processes, Phase measurement, Fourier transforms

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Proceedings Article | 28 April 2004 Paper

Half-time-based reflectivity tomography and its application to thermoacoustic tomography

Mark Anastasio, Xiaochuan Pan, Jin Zhang, Yu Zou, Minghua Xu, Lihong Wang

Proceedings Volume 5373, (2004) https://doi.org/10.1117/12.536710

KEYWORDS: Tomography, Reflectivity, Reconstruction algorithms, Expectation maximization algorithms, Spherical lenses, Data acquisition, Tissues, Radon transform, Microwave radiation, Biomedical optics

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Proceedings Article | 5 June 2003 Paper

Phase-contrast tomography and the local tomography problem

Mark Anastasio, Francesco De Carlo, Xiaochuan Pan

Proceedings Volume 5030, (2003) https://doi.org/10.1117/12.480385

KEYWORDS: Tomography, Reconstruction algorithms, Absorption, Refractive index, Sensors, X-ray imaging, X-rays, Radon transform, Optical spheres, Wave propagation

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Proceedings Article | 30 May 2003 Paper

Investigation of megavoltage local tomography for detecting setup errors in radiation therapy

Daxin Shi, Mark Anastasio, Xiaochuan Pan, Charles Pelizzari, Peter Munro

Proceedings Volume 5029, (2003) https://doi.org/10.1117/12.480348

KEYWORDS: Reconstruction algorithms, Tomography, Tumors, Radiotherapy, 3D image reconstruction, Sensors, Computed tomography, X-ray computed tomography, X-rays, Computer simulations

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Proceedings Article | 23 May 2003 Paper

Image reconstruction from reduced-scan data in ultrasonic reflectivity tomography

Xiaochuan Pan, Yu Zou, Mark Anastasio, Emil Sidky

Proceedings Volume 5035, (2003) https://doi.org/10.1117/12.479960

KEYWORDS: Reflectivity, Tomography, Expectation maximization algorithms, Reconstruction algorithms, Ultrasonics, Data acquisition, Radon transform, Image restoration, Algorithm development, Wavefronts

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Proceedings Article | 3 July 2001 Paper

Development and evaluation of minimal-scan reconstruction algorithms for diffraction tomography

Mark Anastasio, Xiaochuan Pan

Proceedings Volume 4322, (2001) https://doi.org/10.1117/12.431165

KEYWORDS: Reconstruction algorithms, Scattering, Algorithm development, Tomography, Diffraction, X-rays, Refractive index, X-ray computed tomography, Data acquisition, Fourier transforms

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Proceedings Article | 6 June 2000 Paper

Multiobjective genetic optimization of diagnostic classifiers used in the computerized detection of mass lesions in mammography

Matthew Kupinski, Mark Anastasio, Maryellen Giger

Proceedings Volume 3979, (2000) https://doi.org/10.1117/12.387596

KEYWORDS: Mammography, Genetics, Genetic algorithms, Diagnostics, Classification systems, Receivers, Databases, Image classification, Image processing, Detection and tracking algorithms

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SPIE Journal Paper | 1 December 1999

Fourier-based optimal recovery method for anti-aliasing interpolation

Chien-Min Kao, Xiaochuan Pan, Patrick La Riviere, Mark Anastasio

OE, Vol. 38, Issue 12, (December 1999) https://doi.org/10.1117/12.10.1117/1.602308

KEYWORDS: Fourier transforms, Image restoration, Signal processing, Linear filtering, Algorithm development, Image processing, Filtering (signal processing), Optical engineering, Magnetic resonance imaging, Lanthanum

Showing 5 of 219 publications

Conference Committee Involvement (55)

Ultrasonic Imaging and Tomography

18 February 2025 | San Diego, California, United States

Image Perception, Observer Performance, and Technology Assessment

17 February 2025 | San Diego, California, United States

Photons Plus Ultrasound: Imaging and Sensing 2025

26 January 2025 | San Francisco, California, United States

Anomaly Detection and Imaging with X-Rays (ADIX) IX

24 April 2024 | National Harbor, Maryland, United States

Image Perception, Observer Performance, and Technology Assessment

20 February 2024 | San Diego, California, United States

Ultrasonic Imaging and Tomography

19 February 2024 | San Diego, California, United States

Photons Plus Ultrasound: Imaging and Sensing 2024

28 January 2024 | San Francisco, California, United States

Anomaly Detection and Imaging with X-Rays (ADIX) VIII

3 May 2023 | Orlando, Florida, United States

Ultrasonic Imaging and Tomography

22 February 2023 | San Diego, California, United States

Image Perception, Observer Performance, and Technology Assessment

21 February 2023 | San Diego, California, United States

Photons Plus Ultrasound: Imaging and Sensing 2023

29 January 2023 | San Francisco, California, United States

Anomaly Detection and Imaging with X-Rays (ADIX) VII

4 April 2022 | Orlando, Florida, United States

Ultrasonic Imaging and Tomography

23 February 2022 | San Diego, California, United States

Image Perception, Observer Performance, and Technology Assessment

20 February 2022 | San Diego, California, United States

Photons Plus Ultrasound: Imaging and Sensing 2022

23 January 2022 | San Francisco, California, United States

Anomaly Detection and Imaging with X-Rays (ADIX) VI

12 April 2021 | Online Only, Florida, United States

Photons Plus Ultrasound: Imaging and Sensing 2021

6 March 2021 | Online Only, California, United States

Image Perception, Observer Performance, and Technology Assessment

15 February 2021 | Online Only, California, United States

Ultrasonic Imaging and Tomography

15 February 2021 | Online Only, California, United States

Anomaly Detection and Imaging with X-Rays (ADIX) V

27 April 2020 | Online Only, California, United States

Image Perception, Observer Performance, and Technology Assessment

19 February 2020 | Houston, Texas, United States

Ultrasonic Imaging and Tomography

16 February 2020 | Houston, Texas, United States

Photons Plus Ultrasound: Imaging and Sensing 2020

2 February 2020 | San Francisco, California, United States

Unconventional and Indirect Imaging, Image Reconstruction, and Wavefront Sensing 2019

14 August 2019 | San Diego, California, United States

Anomaly Detection and Imaging with X-Rays (ADIX) IV

15 April 2019 | Baltimore, MD, United States

Ultrasonic Imaging and Tomography

17 February 2019 | San Diego, California, United States

Photons Plus Ultrasound: Imaging and Sensing 2019

3 February 2019 | San Francisco, California, United States

Unconventional and Indirect Imaging, Image Reconstruction, and Wavefront Sensing 2018

22 August 2018 | San Diego, California, United States

Anomaly Detection and Imaging with X-Rays (ADIX) III

17 April 2018 | Orlando, FL, United States

Ultrasonic Imaging and Tomography

14 February 2018 | Houston, Texas, United States

Photons Plus Ultrasound: Imaging and Sensing 2018

28 January 2018 | San Francisco, California, United States

Unconventional and Indirect Imaging, Image Reconstruction, and Wavefront Sensing 2017

9 August 2017 | San Diego, California, United States

Advances in Laboratory-based X-Ray Sources, Optics, and Applications VI

7 August 2017 | San Diego, California, United States

Anomaly Detection and Imaging with X-Rays (ADIX) II

12 April 2017 | Anaheim, CA, United States

Ultrasonic Imaging and Tomography

15 February 2017 | Orlando, Florida, United States

Photons Plus Ultrasound: Imaging and Sensing 2017

29 January 2017 | San Francisco, California, United States

Advances in Laboratory-based X-Ray Sources, Optics, and Applications V

30 August 2016 | San Diego, California, United States

Anomaly Detection and Imaging with X-Rays (ADIX)

19 April 2016 | Baltimore, MD, United States

Computational Imaging

17 April 2016 | Baltimore, MD, United States

Ultrasonic Imaging and Tomography

28 February 2016 | San Diego, California, United States

Photons Plus Ultrasound: Imaging and Sensing 2016

14 February 2016 | San Francisco, California, United States

Image Reconstruction from Incomplete Data VIII

11 August 2015 | San Diego, California, United States

Advances in Laboratory-based X-Ray Sources, Optics, and Applications IV

10 August 2015 | San Diego, California, United States

Photons Plus Ultrasound: Imaging and Sensing 2015

8 February 2015 | San Francisco, California, United States

Photons Plus Ultrasound: Imaging and Sensing 2014

2 February 2014 | San Francisco, California, United States

Photons Plus Ultrasound: Imaging and Sensing 2013

3 February 2013 | San Francisco, California, United States

Image Reconstruction from Incomplete Data VII

14 August 2012 | San Diego, California, United States

Photons Plus Ultrasound: Imaging and Sensing 2012

22 January 2012 | San Francisco, California, United States

Photons Plus Ultrasound: Imaging and Sensing 2011

23 January 2011 | San Francisco, California, United States

Image Reconstruction from Incomplete Data VI

2 August 2010 | San Diego, California, United States

Photons Plus Ultrasound: Imaging and Sensing 2010

24 January 2010 | San Francisco, California, United States

Photons Plus Ultrasound: Imaging and Sensing 2009

25 January 2009 | San Jose, California, United States

Image Reconstruction from Incomplete Data V

11 August 2008 | San Diego, California, United States

Photons Plus Ultrasound: Imaging and Sensing 2008: The Ninth Conference on Biomedical Thermoacoustics, Optoacoustics, and Acousto-optics

20 January 2008 | San Jose, California, United States

Image Reconstruction from Incomplete Data IV

14 August 2006 | San Diego, California, United States

Showing 5 of 55 Conference Committees

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