We propose to design, develop and implement a computer-aided detection system for integrating multiple magnetic resonance imaging (MRI) modalities for EARLY DETECTION OF BREAST CANCER IN HIGH RISK PATIENTS, using structural, dynamic contrast enhanced (DCE), and diffusion-weighted (DW) MRI, and magnetic resonance spectroscopy (MRS). Incorporation of morphological and parametric information from structural and DCE MRI data leads to notable improvements in both sensitivity and specificity. However, sensitivity remains suboptimal, particularly for screening purposes. By incorporating additional contrast information derived from DW images and MRS data, further improvements in detection accuracy are expected. A hierarchical set of algorithms will be implemented, using a combination of static feature descriptors, neural networks, and decision tree analysis to integrate the multimodality data. ? ? Early detection of breast cancer is an extremely active area of research, driven by the mediocrity of current mammographic screening methodologies and the consequent expense and inconvenience of unnecessary biopsies of breast lesions ultimately identified as benign. Dr. Schabel will apply his strong background in computational simulation and modeling, spectroscopic data analysis, and image processing toward the detection system described above while developing MRI expertise. Concomitantly he will test and develop appropriate methodologies for application of our new Siemens 3T MRI system. With the significant benefits in signal-to-noise to be gained from using the 3T system, it is likely that this will develop into a fertile area for future work in screening and early detection. ? ? The University of Utah provides a unique constellation of resources for this project. UCAIR has faculty and staff with extensive practical experience with all proposed MRI modalities, and is equipped with cutting edge MRI instrumentation. A group of high-risk breast cancer patients willing to participate in clinical trials is already in place at the Huntsman Cancer Institute, forming a body of prospective participants for the clinical work proposed. Finally, the close participation of clinical radiologists with Dr. Schabel will significantly facilitate the algorithmic work by providing the knowledge and expertise in interpreting radiologic images against which algorithms will be tested. ? ? ?
