Prostate cancer (PCa) is a prevalent disease which affects 1 in 6 men in the United States and has overtaken lung cancer as the leading cause of cancer related deaths in American men and number two worldwide. Among several diagnostic imaging tests that are available for detection of PCa in the market today, Magnetic Resonance Imaging (MRI) occupies a unique position in the detection of PCa due to its excellent soft tissue contrast and its ability to generate tissue property dependent multi-parametric data. While MRI has become an increasingly valuable tool in the management of men with PCa, its use to identify aggressive disease and characterize extent have yet to be developed. Multi-parametric MRI (MP-MRI) studies have been shown to increase sensitivity and specificity towards PCa detection compared to any single MRI dataset. The ability to develop and evaluate MP-MRI to prospectively detect disease, assess aggressiveness and delineate extent, first requires the retrospective validation against post-surgical pathology sections. Despite the large effort made by many groups in this area of research, the correlation of in vivo MP-MRI with pathology is still a challenge and to date is insufficient to develop highly accurate models of disease. To address this problem this thesis showcases (1) a novel registration approach called LATIS (Local Affine Transformation assisted by Internal Structures) for co-registering post prostatectomy pseudo-whole mount (PWM) pathological sections with in vivo MRI images and (2) MP-MRI based predictive model for disease detection using a composite biomarker score based on a unique database of pathology co-registered MR data sets. Also showcased in this thesis is a study where r1 and r2* relaxivities of a common paramagnetic contrast agent were measured in blood and saline at both 3T and 7T. This is important information to have when attempting to perform DCE-MRI studies as part of a MP-MRI protocol at ultra-high magnetic field strengths.
University of Minnesota Ph.D. dissertation. June 2014. Major: Biophysical Sciences and Medical Physics. Advisor: Gregory J. Metzger. 1 computer file (PDF); xix, 116 pages.
Development of multiparametric MRI models for prostate cancer detection based on improved correlative pathology.
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