We are proposing to purchase a distributed memory computation cluster to enhance our research in image analysis, image reconstruction, and technology assessment, The long-term objectives of our research is to advance our knowledge in computer-aided diagnosis (CAD), image reconstruction, MRI/MRS for the breast and prostate cancers, and ROC analysis. Specifically, the proposed system will allow for advances in four areas. First, we plan to conduct clinical evaluation of CAD where online calculations are needed. Because of the complexity of the calculations, these need to conducted on high performance computers to achieve 1 s response of the CAD system. Second, we will develop image analysis techniques for analyzing MRI and MRS datasets for improving the early detection and staging of breast and prostate cancers. The datasets for MRI and MRS are large - over a gigabyte in size. To maximize the extraction of information from these data, preprocesswill develop advanced image reconstruction techniques using a combination of computer simulation to model image acquisition systems and optimizing reconstruction methods using the simulations. Both the simulation and reconstruction techniques are compute intensive. Fourth, we will develop publicly available software for ROC analysis that incorporates recent advances in the field. These include statistical analyses of differences between ROC curves based on variations that due to case-sample variation and image-reader variation. The relevance of the research, which will employ the computation cluster, is four fold. First, we will demonstrate in prospective pre-clinical studies that computers can help radiologists determine whether a breast lesion or a lung nodule is benign or malignant. This can reduce the number of unnecessary biopsies and reduce the chances that a cancer is missed. Second, by extracting more information from MRI studies, we can improve the early detection, diagnosisctive. The impact of our research using the poposed system is large. For example, the old version of ROC software is used by more than 10,000 researchers worldwide. The updated software will undoubtedly benefit many researchers.

ology (ROC analysis) can lead to faster and cheaper implementation of the most promising of new medical advances, because evaluation studies can be designed that are more efficient and cost effe

S10RR021039

2007-04-01

2009-03-31