Spatially-Constrained Probability Distribution Model of Incoherent Motion (SPIM) for Abdominal Diffusion-Weighted MRI: Quantitative diffusion-weighted MR imaging (DW-MRI) of the body enables characterization of the tissue microenvironment by measuring variations in the mobility of water molecules. The diffusion signal decay model parameters are increasingly used to evaluate various diseases of abdominal organs such as the liver and spleen. However, previous signal decay models (i.e., mono-exponential, bi-exponential intra-voxel incoherent motion (IVIM) and stretched exponential models) only provide insight into the average of the distribution of the signal decay rather than explicitly describe the entire range of diffusion scales. In this work, we propose a probability distribution model of incoherent motion that uses a mixture of Gamma distributions to fully characterize the multi-scale nature of diffusion within a voxel. Further, we improve the robustness of the distribution parameter estimates by integrating spatial homogeneity prior into the probability distribution model of incoherent motion (SPIM) and by using the fusion bootstrap solver (FBM) to estimate the model parameters. We evaluated the improvement in quantitative DW-MRI analysis achieved with the SPIM model in terms of accuracy, precision and reproducibility of parameter estimation in both simulated data and in 68 abdominal in-vivo DW-MRIs. Our results show that the SPIM model not only substantially reduced parameter estimation errors by up to 26%; it also significantly improved the robustness of the parameter estimates (paired Students t-test, p < 0:0001) by reducing the coefficient of variation (CV) of estimated parameters compared to those produced by previous models. In addition, the SPIM model improves the parameter estimates reproducibility for both intra- (up to 47%) and inter-session (up to 30%) estimates compared to those generated by previous models. Thus, the SPIM model has the potential to improve accuracy, precision and robustness of quantitative abdominal DW-MRI analysis for clinical applications.
Motion Compensated Abdominal Diffusion Weighted MRI by Simultaneous Image Registration and Model Estimation (SIR-ME): Non-invasive characterization of water molecule's mobility variations by quantitative analysis of diffusion-weighted MRI (DW-MRI) signal decay in the abdomen has the potential to serve as a biomarker in gastrointestinal and oncological applications. Accurate and reproducible estimation of the signal decay model parameters is challenging due to the presence of respiratory, cardiac, and peristalsis motion. Independent registration of each b-value image to the b-value=0 s/mm2 image prior to parameter estimation might be sub-optimal because of the low SNR and contrast difference between images of varying b-value. In this work, we introduce a motion-compensated parameter estimation framework that simultaneously solves image registration and model estimation (SIR-ME) problems by utilizing the interdependence of acquired volumes along the diffusion weighting dimension. We evaluated the improvement in model parameters estimation accuracy using 16 in-vivo DW-MRI data sets of Crohn's disease patients by comparing parameter estimates obtained using the SIR-ME model to the parameter estimates obtained by fitting the signal decay model to the acquired DW-MRI images. The proposed SIR-ME model reduced the average root-mean-square error between the observed signal and the fitted model by more than 50%. Moreover, the SIR-ME model estimates discriminate between normal and abnormal bowel loops better than the standard parameter estimates.
Modeling Spatial Distribution of Emphysema Patterns in Volumetric CTs of COPD Patients:
- Classification of Lung Regions into Patterns of Emphysema Progression Levels
Automated Quantification of Cardiovascular Disease Phenotypes in Volumetric CTs of Chronic Obstructive Pulmonary Disease (COPD) Patients: This project involved development of algorithms for segmentation of large vessels and extraction of morphological and calcification measures as imaging markers of cardiovascular disease. Specifically, I developed an automated aorta segmentation and aortic calcification detection algorithm. I also developed and algorithm to extract aorta morphology measures (e.g. Arch width, radius and cross sectional area, tortuosity, curvature)
3-D/4-D Segmentation of Structures in CT Images for Radiotherapy Planning: This project involved development of segmentation algorithms for radiotherapy planning. Specifically, I developed a knowledge based esophagus segmentation algorithm that uses a novel global-local shape model and a spatial dependency model that I introduced to solve this difficult problem.
Towards Automated Analysis of Confocal Microscopy Images of Human Skin for Early Cancer Detection: Confocal Microscopy allows early skin cancer detection without the need for biopsy followed by histopathology. In this work, I developed automated image analysis algorithms for confocal microscopy images: 1) A locally smooth SVM classification algorithm for detection of the skin layers; 2) Hybrid Sequential Segmentation and Locally Smooth Classification Approach to Detect Dermal Epidermal Junction.