Multiscale Monte Carlo methods for multiparametric biomedical image processing and analysis

多参数成像的最新进展对改进的疾病诊断方案的发展做出了重大贡献。然而，考虑到这种多参数成像数据的数量和复杂性，以及与保持合理的采集时间相关的图像质量权衡，研究科学家和临床医生通常很难以有意义和有效的方式解释和分析采集的数据。提出的研究计划的主要目标是开发用于多参数生物医学图像处理和分析的多尺度蒙特卡罗方法，并将这些方法整合到临床可行的平台中，供放射科医生、临床医生和临床研究人员使用。将研究三个重要问题：1)图像建模，2)图像处理，3)图像分析。这些问题中的每一个都将在一个有效的多尺度马尔可夫链蒙特卡罗(MCMC)框架内进行研究。

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.