Multiscale Monte Carlo methods for multiparametric biomedical image processing and analysis

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Recent advancements in multi-parametric imaging have contributed significantly to the development of improved disease diagnosis protocols. However, given the quantity and complexities of such multiparametric imaging data, along with image quality tradeoffs associated with maintaining reasonable acquisition times, it is often difficult for research scientists and clinicians to interpret and analyze the acquired data in a meaningful and efficient fashion. The main objectives of the proposed research program is to develop multiscale Monte Carlo methods for multiparametric biomedical image processing and analysis, and to incorporate such methods into a clinically-viable platform for use by radiologists, clinicians, and clinical researchers. Three important issues will be investigated: 1) Image modeling, 2) Image processing, and 3) Image analysis. Each of these issues will be investigated within an efficient multiscale Markov-chain Monte Carlo (MCMC) framework.