Graduate Degree Type
School of Engineering
Preclinical in vivo imaging is a powerful tool used for a wide variety of biomedical research applications including oncology, cardiac disease, and neurological disease. Disease physiology can be imaged in vivo with molecular imaging such as PET and SPECT. Quality analysis of molecular in vivo images currently requires an expert technician. The feasibility of large preclinical molecular imaging studies is limited by the man hours required to process the overwhelming amount of data created from preclinical scans. Our proposed solution to the bottle neck of manual image analysis is to implement automation of preclinical molecular image analysis. The method described in this study automatically registers different bone regions of interest in fused molecular imaging/CT scans. Automated analysis can run without supervision from a user, allowing for an increase in image processing throughput compared to manual analysis. The results of this novel image analysis show that atlas based registration of CT data is possible with a moderate degree of accuracy. Using this registration method to generate radiotracer uptake values for different bone groups resulted in mixed success. Bones that are registered first; skull, spine, pelvis, had automated radiotracer uptake measurements that correlated highly with the manual radiotracer uptake measurements. Bones that were last to be registered; tibia, hindpaws, were susceptible to large amounts of variation from the manual radiotracer uptake measurements. Large improvements to the accuracy of the results could be made by ensuring the accuracy of the joint registration of the atlas to the CT dataset.
VanOss, Jeffrey Lee, "Automatic Atlas Based Analysis of Radiotracer Uptake in Bones from Fused Nuclear Imaging/CT Data Sets of Mice" (2012). Masters Theses. 19.