School of Computer Science & Center for Intelligent Machines, McGill University, 3480 University Street, Montréal, QC, Canada H3A2A7.
Med Image Anal. 2011 Aug;15(4):369-96. doi: 10.1016/j.media.2011.02.002. Epub 2011 Feb 16.
Recent advances in diffusion magnetic resonance image (dMRI) modeling have led to the development of several state of the art methods for reconstructing the diffusion signal. These methods allow for distinct features to be computed, which in turn reflect properties of fibrous tissue in the brain and in other organs. A practical consideration is that to choose among these approaches requires very specialized knowledge. In order to bridge the gap between theory and practice in dMRI reconstruction and analysis we present a detailed review of the dMRI modeling literature. We place an emphasis on the mathematical and algorithmic underpinnings of the subject, categorizing existing methods according to how they treat the angular and radial sampling of the diffusion signal. We describe the features that can be computed with each method and discuss its advantages and limitations. We also provide a detailed bibliography to guide the reader.
近年来,扩散磁共振成像(dMRI)建模的进展导致了几种用于重建扩散信号的最先进方法的发展。这些方法允许计算出不同的特征,这些特征反过来反映了大脑和其他器官中纤维组织的特性。一个实际的考虑因素是,要在这些方法中进行选择,需要非常专业的知识。为了弥合 dMRI 重建和分析理论与实践之间的差距,我们对 dMRI 建模文献进行了详细回顾。我们强调了该主题的数学和算法基础,根据它们如何处理扩散信号的角度和径向采样对现有方法进行分类。我们描述了可以用每种方法计算的特征,并讨论了其优点和局限性。我们还提供了详细的参考书目,以指导读者。