School of Physics and Astronomy, University of Leeds, Leeds, UK.
Division of Clinical Medicine, University of Sheffield, Sheffield, UK.
Magn Reson Med. 2024 Mar;91(3):1136-1148. doi: 10.1002/mrm.29906. Epub 2023 Nov 6.
In perfusion MRI, image voxels form a spatially organized network of systems, all exchanging indicator with their immediate neighbors. Yet the current paradigm for perfusion MRI analysis treats all voxels or regions-of-interest as isolated systems supplied by a single global source. This simplification not only leads to long-recognized systematic errors but also fails to leverage the embedded spatial structure within the data. Since the early 2000s, a variety of models and implementations have been proposed to analyze systems with between-voxel interactions. In general, this leads to large and connected numerical inverse problems that are intractible with conventional computational methods. With recent advances in machine learning, however, these approaches are becoming practically feasible, opening up the way for a paradigm shift in the approach to perfusion MRI. This paper seeks to review the work in spatiotemporal modelling of perfusion MRI using a coherent, harmonized nomenclature and notation, with clear physical definitions and assumptions. The aim is to introduce clarity in the state-of-the-art of this promising new approach to perfusion MRI, and help to identify gaps of knowledge and priorities for future research.
在灌注 MRI 中，图像体素形成了一个空间组织化的系统网络，所有体素都与其相邻体素交换指示剂。然而，目前的灌注 MRI 分析范式将所有体素或感兴趣区域视为由单个全局源供应的孤立系统。这种简化不仅导致了长期存在的系统误差，而且未能利用数据中嵌入的空间结构。自 21 世纪初以来，已经提出了多种模型和实现方法来分析具有体素间相互作用的系统。一般来说，这会导致大型的、连通的数值反问题，这是传统计算方法无法解决的。然而，随着机器学习的最新进展，这些方法变得切实可行，为灌注 MRI 方法的范式转变开辟了道路。本文旨在使用一致的、协调的命名法和符号，结合明确的物理定义和假设，来回顾灌注 MRI 的时空建模工作。目的是为这种有前途的灌注 MRI 新方法的最新进展提供清晰的认识，并有助于确定知识差距和未来研究的重点。
