Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, US.
Institute of Mathematical Sciences and Computation, University of São Paulo, São Carlos, SP, Brazil.
NMR Biomed. 2020 Dec;33(12):e4318. doi: 10.1002/nbm.4318. Epub 2020 May 2.
NMR relaxometry can provide information about the relaxation of the magnetization in different tissues, increasing our understanding of molecular dynamics and biochemical composition in biological systems. In general, tissues have complex and heterogeneous structures composed of multiple pools. As a result, bulk magnetization returns to its original state with different relaxation times, in a multicomponent relaxation. Recovering the distribution of relaxation times in each voxel is a difficult inverse problem; it is usually unstable and requires long acquisition time, especially on clinical scanners. MRI can also be viewed as an inverse problem, especially when compressed sensing (CS) is used. The solution of these two inverse problems, CS and relaxometry, can be obtained very efficiently in a synergistically combined manner, leading to a more stable multicomponent relaxometry obtained with short scan times. In this paper, we will discuss the details of this technique from the viewpoint of inverse problems.
NMR 弛豫谱学可以提供关于不同组织中磁化弛豫的信息,增加我们对生物系统中分子动力学和生化组成的理解。一般来说,组织具有由多个池组成的复杂和异质结构。因此,在多分量弛豫中,整体磁化以不同的弛豫时间返回到其原始状态。在每个体素中恢复弛豫时间的分布是一个困难的反问题;它通常不稳定,需要长的采集时间,特别是在临床扫描仪上。磁共振成像也可以看作是一个反问题,特别是在使用压缩感知 (CS) 时。这两个反问题,CS 和弛豫谱学,可以非常有效地以协同组合的方式获得,从而在短扫描时间内获得更稳定的多分量弛豫谱学。在本文中,我们将从反问题的角度讨论该技术的细节。