Volume 11 Issue 2 | Journal of Medical Imaging

Journal of Medical Imaging

VOL. 11 · NO. 2 | March 2024

CONTENTS

IN THIS ISSUE

Editorial (1)

Physics of Medical Imaging (1)

Image Processing (13)

Computer-Aided Diagnosis (4)

Image-Guided Procedures, Robotic Interventions, and Modeling (1)

Image Perception, Observer Performance, and Technology Assessment (2)

Biomedical Applications in Molecular, Structural, and Functional Imaging (2)

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Editorial

Welcome to the second issue of the Journal of Medical Imaging (JMI) for the 2024 year!

Journal of Medical Imaging, Vol. 11, Issue 02, 020101, (April 2024) https://doi.org/10.1117/1.JMI.11.2.020101

TOPICS: Medical imaging, Vacuum chambers, Transparency, Metrology, Liver, Image registration, Image processing, Data acquisition, Connectors, Clinical trials

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Physics of Medical Imaging

Dual-energy computed tomography imaging with megavoltage and kilovoltage X-ray spectra

Giavanna Jadick, Geneva Schlafly, Patrick La Rivière

Journal of Medical Imaging, Vol. 11, Issue 02, 023501, (March 2024) https://doi.org/10.1117/1.JMI.11.2.023501

TOPICS: Image quality, Signal to noise ratio, Bone, Computed tomography, X-ray computed tomography, Metals, Dual energy imaging, Lanthanum, Titanium, Radiotherapy

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Image Processing

Estimation of radiographic joint space of the trapeziometacarpal joint with computed tomographic validation

David Jordan, John Elfar, Chian Kwoh, Zong-Ming Li

Journal of Medical Imaging, Vol. 11, Issue 02, 024001, (March 2024) https://doi.org/10.1117/1.JMI.11.2.024001

TOPICS: Radiography, Computed tomography, 3D projection, Visualization, Tomography, Biological imaging, In vivo imaging, Image quality standards, Anatomy, 3D imaging standards

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Systematic evaluation of MRI-based characterization of tumor-associated vascular morphology and hemodynamics via a dynamic digital phantom

Chengyue Wu, David Hormuth II, Ty Easley, Federico Pineda, Gregory Karczmar, Thomas Yankeelov

Journal of Medical Imaging, Vol. 11, Issue 02, 024002, (March 2024) https://doi.org/10.1117/1.JMI.11.2.024002

TOPICS: Tumors, Signal to noise ratio, Spatial resolution, Hemodynamics, Image segmentation, Image resolution, Temporal resolution, Morphological analysis, Cancer, Biological imaging

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Automated segmentation of the left-ventricle from MRI with a fully convolutional network to investigate CTRCD in breast cancer patients

Julia Kar, Michael Cohen, Samuel McQuiston, Teja Poorsala, Christopher Malozzi

Journal of Medical Imaging, Vol. 11, Issue 02, 024003, (March 2024) https://doi.org/10.1117/1.JMI.11.2.024003

TOPICS: Image segmentation, Vacuum chambers, Myocardium, Education and training, Magnetic resonance imaging, Breast cancer, Tunable filters, Convolution, Avalanche photodetectors, Strain analysis

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CMNet: deep learning model for colon polyp segmentation based on dual-branch structure

Xuguang Cao, Kefeng Fan, Cun Xu, Huilin Ma, Kaijie Jiao

Journal of Medical Imaging, Vol. 11, Issue 02, 024004, (March 2024) https://doi.org/10.1117/1.JMI.11.2.024004

TOPICS: Polyps, Colon, Image segmentation, Education and training, Data modeling, Performance modeling, Deep learning, Transformers, Convolution, Feature extraction

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Purpose

Colon cancer is one of the top three diseases in gastrointestinal cancers, and colon polyps are an important trigger of colon cancer. Early diagnosis and removal of colon polyps can avoid the incidence of colon cancer. Currently, colon polyp removal surgery is mainly based on artificial-intelligence (AI) colonoscopy, supplemented by deep-learning technology to help doctors remove colon polyps. With the development of deep learning, the use of advanced AI technology to assist in medical diagnosis has become mainstream and can maximize the doctor’s diagnostic time and help doctors to better formulate medical plans.

Approach

We propose a deep-learning model for segmenting colon polyps. The model adopts a dual-branch structure, combines a convolutional neural network (CNN) with a transformer, and replaces ordinary convolution with deeply separable convolution based on ResNet; a stripe pooling module is introduced to obtain more effective information. The aggregated attention module (AAM) is proposed for high-dimensional semantic information, which effectively combines two different structures for the high-dimensional information fusion problem. Deep supervision and multi-scale training are added in the model training process to enhance the learning effect and generalization performance of the model.

Results

The experimental results show that the proposed dual-branch structure is significantly better than the single-branch structure, and the model using the AAM has a significant performance improvement over the model not using the AAM. Our model leads 1.1% and 1.5% in mIoU and mDice, respectively, when compared with state-of-the-art models in a fivefold cross-validation on the Kvasir-SEG dataset.

Conclusions

We propose and validate a deep learning model for segmenting colon polyps, using a dual-branch network structure. Our results demonstrate the feasibility of complementing traditional CNNs and transformer with each other. And we verified the feasibility of fusing different structures on high-dimensional semantics and successfully retained the high-dimensional information of different structures effectively.

AMS-U-Net: automatic mass segmentation in digital breast tomosynthesis via U-Net

Ahmad Qasem, Genggeng Qin, Zhiguo Zhou

Journal of Medical Imaging, Vol. 11, Issue 02, 024005, (March 2024) https://doi.org/10.1117/1.JMI.11.2.024005

TOPICS: Image segmentation, Digital breast tomosynthesis, Gaussian filters, Education and training, Image processing, Performance modeling, Visualization, Tumors, Breast cancer, Tunable filters

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Task-based transferable deep-learning scatter correction in cone beam computed tomography: a simulation study

Juan Cruz-Bastida, Fernando Moncada, Arnulfo Martínez-Dávalos, Mercedes Rodríguez-Villafuerte

Journal of Medical Imaging, Vol. 11, Issue 02, 024006, (March 2024) https://doi.org/10.1117/1.JMI.11.2.024006

TOPICS: Cone beam computed tomography, X-rays, Education and training, Data modeling, Head, X-ray imaging, Image quality, Monte Carlo methods, Image restoration, Computer simulations

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Prognostic value of different discretization parameters in ¹⁸fluorodeoxyglucose positron emission tomography radiomics of oropharyngeal squamous cell carcinoma

Breylon Riley, Jack Stevens, Xiang Li, Zhenyu Yang, Chunhao Wang, Yvonne Mowery, David Brizel, Fang-Fang Yin, Kyle Lafata

Journal of Medical Imaging, Vol. 11, Issue 02, 024007, (March 2024) https://doi.org/10.1117/1.JMI.11.2.024007

TOPICS: Radiomics, Positron emission tomography, Tumors, Feature extraction, Diseases and disorders, Tumor growth modeling, Image segmentation, Radiotherapy, Performance modeling, Matrices

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Purpose

We aim to interrogate the role of positron emission tomography (PET) image discretization parameters on the prognostic value of radiomic features in patients with oropharyngeal cancer.

Approach

A prospective clinical trial (NCT01908504) enrolled patients with oropharyngeal squamous cell carcinoma (N=69; mixed HPV status) undergoing definitive radiotherapy and evaluated intra-treatment ¹⁸fluorodeoxyglucose PET as a potential imaging biomarker of early metabolic response. The primary tumor volume was manually segmented by a radiation oncologist on PET/CT images acquired two weeks into treatment (20 Gy). From this, 54 radiomic texture features were extracted. Two image discretization techniques—fixed bin number (FBN) and fixed bin size (FBS)—were considered to evaluate systematic changes in the bin number ({32, 64, 128, 256} gray levels) and bin size ({0.10, 0.15, 0.22, 0.25} bin-widths). For each discretization-specific radiomic feature space, an LASSO-regularized logistic regression model was independently trained to predict residual and/or recurrent disease. The model training was based on Monte Carlo cross-validation with a 20% testing hold-out, 50 permutations, and minor-class up-sampling to account for imbalanced outcomes data. Performance differences among the discretization-specific models were quantified via receiver operating characteristic curve analysis. A final parameter-optimized logistic regression model was developed by incorporating different settings parameterizations into the same model.

Results

FBN outperformed FBS in predicting residual and/or recurrent disease. The four FBN models achieved AUC values of 0.63, 0.61, 0.65, and 0.62 for 32, 64, 128, and 256 gray levels, respectively. By contrast, the average AUC of the four FBS models was 0.53. The parameter-optimized model, comprising features joint entropy (FBN = 64) and information measure correlation 1 (FBN = 128), achieved an AUC of 0.70. Kaplan–Meier analyses identified these features to be associated with disease-free survival (p=0.0158 and p=0.0180, respectively; log-rank test).

Conclusions

Our findings suggest that the prognostic value of individual radiomic features may depend on feature-specific discretization parameter settings.

Deep conditional generative model for longitudinal single-slice abdominal computed tomography harmonization

Xin Yu, Qi Yang, Yucheng Tang, Riqiang Gao, Shunxing Bao, Leon Cai, Ho Hin Lee, Yuankai Huo, Ann Zenobia Moore, Luigi Ferrucci, Bennett Landman

Journal of Medical Imaging, Vol. 11, Issue 02, 024008, (April 2024) https://doi.org/10.1117/1.JMI.11.2.024008

TOPICS: Data modeling, Education and training, Computed tomography, Performance modeling, 3D modeling, Tissues, Gallium nitride, Body composition, 3D image processing, 3D acquisition

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Cascaded cross-attention transformers and convolutional neural networks for multi-organ segmentation in male pelvic computed tomography

Rahul Pemmaraju, Gayoung Kim, Lina Mekki, Daniel Song, Junghoon Lee

Journal of Medical Imaging, Vol. 11, Issue 02, 024009, (April 2024) https://doi.org/10.1117/1.JMI.11.2.024009

TOPICS: Image segmentation, Image enhancement, Transformers, Education and training, Head, Prostate, Computed tomography, Bladder, Rectum, Voxels

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Somatomotor-visual resting state functional connectivity increases after 2 years in the UK Biobank longitudinal cohort

Anton Orlichenko, Kuan-Jui Su, Hui Shen, Hong-Wen Deng, Yu-Ping Wang

Journal of Medical Imaging, Vol. 11, Issue 02, 024010, (April 2024) https://doi.org/10.1117/1.JMI.11.2.024010

TOPICS: Scanners, Functional magnetic resonance imaging, Education and training, Independent component analysis, Brain, Matrices, Fluorescence correlation spectroscopy, Alzheimer disease, Neuroimaging, Emotion

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Empirical assessment of the assumptions of ComBat with diffusion tensor imaging

Michael Kim, Chenyu Gao, Leon Cai, Qi Yang, Nancy Newlin, Karthik Ramadass, Angela Jefferson, Derek Archer, Niranjana Shashikumar, Kimberly Pechman, Katherine Gifford, Timothy Hohman, Lori Beason-Held, Susan Resnick, Stefan Winzeck, Kurt Schilling, Panpan Zhang, Daniel Moyer, Bennett Landman

Journal of Medical Imaging, Vol. 11, Issue 02, 024011, (April 2024) https://doi.org/10.1117/1.JMI.11.2.024011

TOPICS: Silver, Diffusion tensor imaging, Error analysis, Diffusion, Statistical analysis, Biological samples, Reliability, Data modeling, Magnetic resonance imaging, Voxels

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Purpose

Diffusion tensor imaging (DTI) is a magnetic resonance imaging technique that provides unique information about white matter microstructure in the brain but is susceptible to confounding effects introduced by scanner or acquisition differences. ComBat is a leading approach for addressing these site biases. However, despite its frequent use for harmonization, ComBat’s robustness toward site dissimilarities and overall cohort size have not yet been evaluated in terms of DTI.

Approach

As a baseline, we match N=358 participants from two sites to create a “silver standard” that simulates a cohort for multi-site harmonization. Across sites, we harmonize mean fractional anisotropy and mean diffusivity, calculated using participant DTI data, for the regions of interest defined by the JHU EVE-Type III atlas. We bootstrap 10 iterations at 19 levels of total sample size, 10 levels of sample size imbalance between sites, and 6 levels of mean age difference between sites to quantify (i) βAGE, the linear regression coefficient of the relationship between FA and age; (ii) γ^sf*, the ComBat-estimated site-shift; and (iii) δ^sf*, the ComBat-estimated site-scaling. We characterize the reliability of ComBat by evaluating the root mean squared error in these three metrics and examine if there is a correlation between the reliability of ComBat and a violation of assumptions.

Results

ComBat remains well behaved for βAGE when N>162 and when the mean age difference is less than 4 years. The assumptions of the ComBat model regarding the normality of residual distributions are not violated as the model becomes unstable.

Conclusion

Prior to harmonization of DTI data with ComBat, the input cohort should be examined for size and covariate distributions of each site. Direct assessment of residual distributions is less informative on stability than bootstrap analysis. We caution use ComBat of in situations that do not conform to the above thresholds.

SpecReFlow: an algorithm for specular reflection restoration using flow-guided video completion

Haoli Yin, Rachel Eimen, Daniel Moyer, Audrey Bowden

Journal of Medical Imaging, Vol. 11, Issue 02, 024012, (April 2024) https://doi.org/10.1117/1.JMI.11.2.024012

TOPICS: Video, Endoscopy, Specular reflections, RGB color model, Detection and tracking algorithms, Image restoration, Image enhancement, Education and training, Data modeling, Optical flow

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Simulation of acquisition shifts in T2 weighted fluid-attenuated inversion recovery magnetic resonance images to stress test artificial intelligence segmentation networks

Christiane Posselt, Mehmet Yigit Avci, Mehmet Yigitsoy, Patrick Schuenke, Christoph Kolbitsch, Tobias Schaeffter, Stefanie Remmele

Journal of Medical Imaging, Vol. 11, Issue 02, 024013, (April 2024) https://doi.org/10.1117/1.JMI.11.2.024013

TOPICS: Magnetic resonance imaging, Image segmentation, Data modeling, Tissues, Photovoltaics, Education and training, Artificial intelligence, Multiple sclerosis, Performance modeling, Data acquisition

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Computer-Aided Diagnosis

Automatic hepatic tumor segmentation in intra-operative ultrasound: a supervised deep-learning approach

Tiziano Natali, Andrey Zhylka, Karin Olthof, Jasper Smit, Tarik Baetens, Niels F. Kok, Koert F. Kuhlmann, Oleksandra Ivashchenko, Theo J. Ruers, Matteo Fusaglia

Journal of Medical Imaging, Vol. 11, Issue 02, 024501, (March 2024) https://doi.org/10.1117/1.JMI.11.2.024501

TOPICS: Tumors, Image segmentation, Education and training, Performance modeling, Liver, Surgery, Ultrasonography, Data modeling, Cancer detection, Resection

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Detecting bone lesions in X-ray under diverse acquisition conditions

Tal Zimbalist, Ronnie Rosen, Keren Peri-Hanania, Yaron Caspi, Bar Rinott, Carmel Zeltser-Dekel, Eyal Bercovich, Yonina Eldar, Shai Bagon

Journal of Medical Imaging, Vol. 11, Issue 02, 024502, (March 2024) https://doi.org/10.1117/1.JMI.11.2.024502

TOPICS: Bone, Tumors, Object detection, Cancer detection, Radiography, Education and training, X-rays, Tumor growth modeling, Histograms, Image processing

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Lightweight preprocessing and template matching facilitate streamlined ischemic myocardial scar classification

Michael Udin, Sara Armstrong, Alice Kai, Scott Doyle, Ciprian Ionita, Saraswati Pokharel, Umesh Sharma

Journal of Medical Imaging, Vol. 11, Issue 02, 024503, (March 2024) https://doi.org/10.1117/1.JMI.11.2.024503

TOPICS: Image classification, Cardiovascular magnetic resonance imaging, Visualization, Image processing, Photonic integrated circuits, Education and training, Machine learning, Transform theory, Image quality, Heart

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MIDRC-MetricTree: a decision tree-based tool for recommending performance metrics in artificial intelligence-assisted medical image analysis

Karen Drukker, Berkman Sahiner, Tingting Hu, Grace Hyun Kim, Heather Whitney, Natalie Baughan, Kyle Myers, Maryellen Giger, Michael McNitt-Gray

Journal of Medical Imaging, Vol. 11, Issue 02, 024504, (April 2024) https://doi.org/10.1117/1.JMI.11.2.024504

TOPICS: Medical imaging, Decision trees, Image segmentation, Algorithm development, Statistical analysis, Diseases and disorders, Binary data, Evolutionary algorithms, COVID 19, Biological research

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Image-Guided Procedures, Robotic Interventions, and Modeling

Comparison study of intraoperative surface acquisition methods on registration accuracy for soft-tissue surgical navigation

Bowen Xiang, Jon Heiselman, Winona Richey, Michael D’Angelica, Alice Wei, T. Peter Kingham, Frankangel Servin, Kyvia Pereira, Sunil Geevarghese, William Jarnagin, Michael Miga

Journal of Medical Imaging, Vol. 11, Issue 02, 025001, (March 2024) https://doi.org/10.1117/1.JMI.11.2.025001

TOPICS: Liver, Natural surfaces, Deformation, Image registration, Surgery, Data acquisition, Data modeling, Rigid registration, Optical tracking, Image segmentation

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Purpose

To study the difference between rigid registration and nonrigid registration using two forms of digitization (contact and noncontact) in human in vivo liver surgery.

Approach

A Conoprobe device attachment and sterilization process was developed to enable prospective noncontact intraoperative acquisition of organ surface data in the operating room (OR). The noncontact Conoprobe digitization method was compared against stylus-based acquisition in the context of image-to-physical registration for image-guided surgical navigation. Data from n=10 patients undergoing liver resection were analyzed under an Institutional Review Board-approved study at Memorial Sloan Kettering Cancer Center. Organ surface coverage of each surface acquisition method was compared. Registration accuracies resulting from the acquisition techniques were compared for (1) rigid registration method (RRM), (2) model-based nonrigid registration method (NRM) using surface data only, and (3) NRM with one subsurface feature (vena cava) from tracked intraoperative ultrasound (NRM-VC). Novel vessel centerline and tumor targets were segmented and compared to their registered preoperative counterparts for accuracy validation.

Results

Surface data coverage collected by stylus and Conoprobe were 24.6%±6.4% and 19.6%±5.0%, respectively. The average difference between stylus data and Conoprobe data using NRM was −1.05 mm and using NRM-VC was −1.42 mm, indicating the registrations to Conoprobe data performed worse than to stylus data with both NRM approaches. However, using the stylus and Conoprobe acquisition methods led to significant improvement of NRM-VC over RRM by average differences of 4.48 and 3.66 mm, respectively.

Conclusion

The first use of a sterile-field amenable Conoprobe surface acquisition strategy in the OR is reported for open liver surgery. Under clinical conditions, the nonrigid registration significantly outperformed standard-of-care rigid registration, and acquisition by contact-based stylus and noncontact-based Conoprobe produced similar registration results. The accuracy benefits of noncontact surface acquisition with a Conoprobe are likely obscured by inferior data coverage and intrinsic noise within acquisition systems.

Image Perception, Observer Performance, and Technology Assessment

Protocol selection formalism for minimizing detectable differences in morphological radiomics features of lung lesions in repeated CT acquisitions

Mojtaba Zarei, Ehsan Abadi, Liesbeth Vancoillie, Ehsan Samei

Journal of Medical Imaging, Vol. 11, Issue 02, 025501, (April 2024) https://doi.org/10.1117/1.JMI.11.2.025501

TOPICS: Magnetorheological finishing, Radiomics, Imaging systems, Scanners, Biological imaging, Modulation transfer functions, Mathematical optimization, Simulations, Lung, Image segmentation

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Background

The accuracy of morphological radiomic features (MRFs) can be affected by various acquisition settings and imaging conditions. To ensure that clinically irrelevant changes do not reduce sensitivity to capture the radiomics changes between successive acquisitions, it is essential to determine the optimal imaging systems and protocols to use.

Purpose

The main goal of our study was to optimize CT protocols and minimize the minimum detectable difference (MDD) in successive acquisitions of MRFs.

Method

MDDs were derived based on the previous research involving 15 realizations of nodule models at two different sizes. Our study involved simulations of two consecutive acquisitions using 297 different imaging conditions, representing variations in scanners’ reconstruction kernels, dose levels, and slice thicknesses. Parametric polynomial models were developed to establish correlations between imaging system characteristics, lesion size, and MDDs. Additionally, polynomial models were used to model the correlation of the imaging system parameters. Optimization problems were formulated for each MRF to minimize the approximated function. Feature importance was determined for each MRF through permutation feature analysis. The proposed method was compared to the recommended guidelines by the quantitative imaging biomarkers alliance (QIBA).

Results

The feature importance analysis showed that lesion size is the most influential parameter to estimate the MDDs in most of the MRFs. Our study revealed that thinner slices and higher doses had a measurable impact on reducing the MDDs. Higher spatial resolution and lower noise magnitude were identified as the most suitable or noninferior acquisition settings. Compared to QIBA, the proposed protocol selection guideline demonstrated a reduced coefficient of variation, with values decreasing from 1.49 to 1.11 for large lesions and from 1.68 to 1.12 for small lesions.

Conclusion

The protocol optimization framework provides means to assess and optimize protocols to minimize the MDD to increase the sensitivity of the measurements in lung cancer screening.

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

Kaiyan Li, Umberto Villa, Hua Li, Mark Anastasio

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

TOPICS: 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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Purpose

The 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.

Approach

Numerical 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.

Results

For 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.

Conclusion

This 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.

Biomedical Applications in Molecular, Structural, and Functional Imaging

Machine learning based prediction of image quality in prostate MRI using rapid localizer images

Abdullah Al-Hayali, Amin Komeili, Azar Azad, Paul Sathiadoss, Nicola Schieda, Eranga Ukwatta

Journal of Medical Imaging, Vol. 11, Issue 02, 026001, (March 2024) https://doi.org/10.1117/1.JMI.11.2.026001

TOPICS: Image quality, Magnetic resonance imaging, Image segmentation, Diffusion weighted imaging, Prostate, Analog to digital converters, Rectum, Radiomics, Machine learning, Education and training

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Mobile infrared slit-light scanner for rapid eye disease screening

Neelam Kaushik, Parmanand Sharma, Noriko Himori, Takuro Matsumoto, Takehiro Miya, Toru Nakazawa

Journal of Medical Imaging, Vol. 11, Issue 02, 026003, (April 2024) https://doi.org/10.1117/1.JMI.11.2.026003

TOPICS: Eye, Portability, Cataracts, Diseases and disorders, Microscopes, Vacuum chambers, Medicine, Infrared radiation, Scanners, Photography

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