A Hidden Markov Model Based Segmentation Framework for MR Spectroscopy Imaging

DESCRIPTION (provided by applicant): The convergence of biomedicine and computation in the form of biomedical computing has demonstrated gains in the areas of genetic sequences, biomedical images, qualitative descriptors for health and social science and geospatial images and chemical formulae. This provides a unique opportunity to utilize novel image processing techniques to more accurately and objectively characterize anatomical and molecular structures and establish tractable measurements and quantification of normal and disease states. Accurate quantification of brain metabolites from Magnetic Resonance Spectroscopic Imaging (MRSI) is becoming increasingly important in the examination of long-term effects of disease and monitoring of the effects of treatment in cancer, neuro-degenerative diseases, and mental health. During Phase I, this proposal will develop an innovative image segmentation framework for the analysis of MRSI data, which is accurate, robust, and computationally efficient for eventual use as a tool in monitoring cancer treatment. The proposed framework is based on a novel utilization of Hidden Markov Models (HMM) that are traind to recognize different tissue parameters in the brain and is then used for the segmentation of MR data. The HMM-based segmentation is used for its attractive accuracy, robustness and computational efficiency (as tradeoff with accuracy) characteristics which are demonstrated from the mathematical foundation of the HMM as well as the preliminary results. The segmentation framework will then be used as part of a system to provide reproducible and tractable quantification of brain metabolites in MRSI analysis for cancer treatment analysis. Based on the success of Phase I, in Phase II, the tools developed from the framework will be integrated within a Picture Archiving and Communication Systems (PACS) for clinical use utilizing MRSI datasets for monitoring the effects of cancer treatment. Accurate in vivo quantification of brain metabolites is useful in examination of long-term effects of disease and monitoring the effects of treatment. This provides a unique opportunity to utilize novel image processing techniques to more accurately and objectively characterize anatomical and molecular structures and establish tractable measurements and quantification of normal and disease states. Co-analysis of segmented MR imaging data and "functional" MR data can improve the accuracy of assessing the burden of disease in patients with neurodegenerative, inflammatory/infectious, and neurovascular disorders.