Recent studies monitoring severity of abdominal aortic aneurysm (AAA) suggested that reliance on only the maximum transverse diameter (Dmax) may be insufficient to predict AAA rupture risk. Moreover, geometric indices, biomechanical parameters, material properties, and patient-specific historical data affect AAA morphology, indicating the need for an integrative approach that incorporates all factors for more accurate estimation of AAA severity. We implemented a machine learning algorithm using 45 features extracted from 66 patients. The model was generated using the J48 decision tree algorithm with the aim of maximizing model accuracy. Three different feature sets were used to assess the prediction rate: i) using Dmax as a single-feature set, ii) using a set of all features, and, lastly iii) using a feature set selected via the BestFirst feature selection algorithm. Our results indicate that BestFirst feature selection yielded the highest prediction accuracy. These results indicate that a combination of several specific parameters that comprehensively capture AAA behavior may enable a suitable assessment of AAA severity, suggesting the potential benefit of machine learning for this application.
The overall geometry and different biomechanical parameters of an abdominal aortic aneurysm (AAA), contribute to its severity and risk of rupture, therefore they could be used to track its progression. Previous and ongoing research efforts have resorted to using uniform material properties to model the behavior of AAA. However, it has been recently illustrated that different regions of the AAA wall exhibit different behavior due to the effect of the biological activities in the metalloproteinase matrix that makes up the wall at the aneurysm site. In this work, we introduce a non-invasive patientspecific regional material property model to help us better understand and investigate the AAA wall stress distribution, peak wall stress (PWS) severity, and potential rupture risk. Our results indicate that the PWS and the overall wall stress distribution predicted using the proposed regional material property model, are higher than those predicted using the traditional homogeneous, hyper-elastic model (p <1.43E-07). Our results also show that to investigate AAA, the overall geometry, presence of intra-luminal thrombus (ILT), and loading condition in a patient specific manner may be critical for capturing the biomechanical complexity of AAAs.
KEYWORDS: Magnetic resonance imaging, Tissues, Process modeling, Data modeling, Finite element methods, 3D modeling, Image segmentation, 3D image processing, Arteries, Computed tomography
Abdominal aortic aneurysm (AAA) is known as a leading cause of death in the United States. AAA is an abnormal dilation of the aorta, which usually occurs below the renal arteries and causes an expansion at least 1.5 times its normal diameter. It has been shown that biomechanical parameters of the aortic tissue coupled with a set of specific geometric parameters characterizing the vessel expansion, affect the risk of aneurysm rupture. Here, we developed a numerical framework that incorporates both biomechanical and geometrical factors to study the behavior of abdominal aortic aneurysm. Our workflow enables the extraction of the aneurysm geometry from both clinical quality, as well as low-resolution MR images. We used a two-parameter, hyper-elastic, isotropic, incompressible material to model the vessel tissue. Our numerical model was tested using both synthetic and mouse data and we evaluated the effects of the geometrical and biomechanical properties on the developed peak wall stress. In addition, we performed several parameter sensitivity studies to investigate the effect of different factors affecting the AAA and its behavior and rupture. Lastly, relationships between different geometrical and biomechanical parameters and peak wall stress were determined. These studies help us better understand vessel tissue response to various loading, geometry and biomechanics conditions, and we plan to further correlate these findings with the pathophysiological conditions from a patient population diagnosed with abdominal aortic aneurysms.
Measurement of blood flow velocity for in vivo microscopic video is an invasive approach to study microcirculation systems, which has been applied in clinical analysis and physiological study. The video sequences investigated in this paper are recording the microcirculation in a rat brain using a CCD camera with a frame rate of 30 fps. To evaluate the accuracy and feasibility of applying motion estimation methods, we have compared both current optical flow and particle image velocimetry (PIV) techniques using cross-correlation by testing them with simulated vessel images and in vivo microscopic video sequences. The accuracy is evaluated by calculating the mean square root values of the results of these two methods based on ground truth. The limitations of applying both algorithms to our particular video sequences are discussed in terms of noise, the effect of large displacements, and vascular structures. The sources of erroneous motion vectors resulting from utilizing microscopic video with standard frame rate are addressed in this paper. Based on the above, a modified cross-correlation PIV technique called adaptive window cross-correlation (AWCC) is proposed to improve the performance of detecting motions in thinner and slightly complex vascular structures.
Frequency domain analysis of the photoacoustic (PA) radio frequency signals can potentially be used as a tool for characterizing microstructure of absorbers in tissue. This study investigates the feasibility of analyzing the spectrum of multiwavelength PA signals generated by excised human prostate tissue samples to differentiate between malignant and normal prostate regions. Photoacoustic imaging at five different wavelengths, corresponding to peak absorption coefficients of deoxyhemoglobin, whole blood, oxyhemoglobin, water and lipid in the near infrared (NIR) (700 nm – 1000 nm) region, was performed on freshly excised prostate specimens taken from patients undergoing prostatectomy for biopsy confirmed prostate cancer. The PA images were co-registered with the histopathology images of the prostate specimens to determine the region of interest (ROI) corresponding to malignant and normal tissue. The calibrated power spectrum of each PA signal from a selected ROI was fit to a linear model to extract the corresponding slope, midband fit and intercept parameters. The mean value of each parameter corresponding to malignant and adjacent normal prostate ROI was calculated for each of the five wavelengths. The results obtained for 9 different human prostate specimens, show that the mean values of midband fit and intercept are significantly different between malignant and normal regions. In addition, the average midband fit and intercept values show a decreasing trend with increasing wavelength. These preliminary results suggest that frequency analysis of multispectral PA signals can be used to differentiate malignant region from the adjacent normal region in human prostate tissue.
Three-dimensional textural and volumetric image analysis holds great potential in understanding the image data produced by multi-photon microscopy. In this paper, an algorithm that quantitatively analyzes the texture and the morphology of vasculature in engineered tissues is proposed. The investigated 3D artificial tissues consist of Human Umbilical Vein Endothelial Cells (HUVEC) embedded in collagen exposed to two regimes of ultrasound standing wave fields under different pressure conditions. Textural features were evaluated using the normalized Gray-Scale Cooccurrence Matrix (GLCM) combined with Gray-Level Run Length Matrix (GLRLM) analysis. To minimize error resulting from any possible volume rotation and to provide a comprehensive textural analysis, an averaged version of nine GLCM and GLRLM orientations is used. To evaluate volumetric features, an automatic threshold using the gray level mean value is utilized. Results show that our analysis is able to differentiate among the exposed samples, due to morphological changes induced by the standing wave fields. Furthermore, we demonstrate that providing more textural parameters than what is currently being reported in the literature, enhances the quantitative understanding of the heterogeneity of artificial tissues.
A currently available 2-D high-resolution, optical molecular imaging system was modified by the addition of a
structured illumination source, OptigridTM, to investigate the feasibility of providing depth resolution along the
optical axis. The modification involved the insertion of the OptigridTM and a lens in the path between the light source
and the image plane, as well as control and signal processing software. Projection of the OptigridTM onto the imaging
surface at an angle, was resolved applying the Scheimpflug principle. The illumination system implements
modulation of the light source and provides a framework for capturing depth resolved mages.
The system is capable of in-focus projection of the OptigridTM at different spatial frequencies, and supports the use
of different lenses. A calibration process was developed for the system to achieve consistent phase shifts of the
OptigridTM. Post-processing extracted depth information using depth modulation analysis using a phantom block
with fluorescent sheets at different depths.
An important aspect of this effort was that it was carried out by a multidisciplinary team of engineering and science
students as part of a capstone senior design program. The disciplines represented are mechanical engineering,
electrical engineering and imaging science. The project was sponsored by a financial grant from New York State
with equipment support from two industrial concerns. The students were provided with a basic imaging concept and
charged with developing, implementing, testing and validating a feasible proof-of-concept prototype system that was
returned to the originator of the concept for further evaluation and characterization.
KEYWORDS: Tissues, Breast, Blood, Skin, Signal attenuation, Monte Carlo methods, 3D modeling, Raster graphics, Positron emission tomography, Natural surfaces
The quality and realism of simulated images is currently limited by the quality of the digital phantoms used for the simulations. The transition from simple raster based phantoms to more detailed geometric (mesh) based phantoms has the potential to increase the usefulness of the simulated data. A preliminary breast phantom which contains 12 distinct tissue classes along with the tissue properties necessary for the simulation of dynamic positron emission tomography scans was created (activity and attenuation). The phantom contains multiple components which can be separately manipulated, utilizing geometric transformations, to represent populations or a single individual being imaged in multiple positions. A new relational descriptive language is presented which conveys the relationships between individual mesh components. This language, which defines how the individual mesh components are composed into the phantom, aids in phantom development by enabling the addition and removal of components without modification of the other components, and simplifying the definition of complex interfaces. Results obtained when testing the phantom using the SimSET PET/SPECT simulator are very encouraging.
A recently developed, freely available, application specifically designed for the visualization of multimodal data sets is
presented. The application allows multiple 3D data sets such as CT (x-ray computer tomography), MRI (magnetic
resonance imaging), PET (positron emission tomography), and SPECT (single photon emission tomography) of the same
subject to be viewed simultaneously. This is done by maintaining synchronization of the spatial location viewed within
all modalities, and by providing fused views of the data where multiple data sets are displayed as a single volume.
Different options for the fused views are provided by plug-ins. Plug-ins typically used include color-overlays and
interlacing, but more complex plug-ins such as those based on different color spaces, and component analysis techniques
are also supported.
Corrections for resolution differences and user preference of contrast and brightness are made. Pre-defined and custom
color tables can be used to enhance the viewing experience. In addition to these essential capabilities, multiple options
are provided for mapping 16-bit data sets onto an 8-bit display, including windowing, automatically and dynamically
defined tone transfer functions, and histogram based techniques.
The 3D data sets can be viewed not only as a stack of images, but also as the preferred three orthogonal cross sections
through the volume. More advanced volumetric displays of both individual data sets and fused views are also provided.
This includes the common MIP (maximum intensity projection) both with and without depth correction for both
individual data sets and multimodal data sets created using a fusion plug-in.
Ultrasound speckle carries information about the interrogated scattering microstructure. The complex signal is represented as a superposition of signals due to all scatterers within a resolution cell volume, VE. A crossbeam geometry with separate transmit and receive transducers is well suited for such studies. The crossbeam volume, VE is defined in terms of the overlapping diffraction beam patterns. Given the focused piston transducer's radius and focal distance, a Lommel diffraction formulation suitable for monochromatic excitation is used to calculate VE as a function of frequency and angle. This formulation amounts to a Fresnel approximation to the diffraction problem and is not limited to the focal zone or the far field. Such diffraction corrections as VE are needed to remove the system effects when trying to characterize material using moment analysis. Theoretically, VE is numerically integrated within the overlapping region of the product of the transmit-receive transfer functions. Experimentally, VE was calculated from the field pattern of a medium-focused transducer excited by a monochromatic signal detected by a 0.5mm diameter PVDF membrane hydrophone. We present theoretical and experimental evaluations of VE for the crossbeam geometry at frequencies within the transducers' bandwidth, and its application to tissue microstructure characterization.
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