Pathak Arvind P, Ward B Douglas, Schmainda Kathleen M
JHU ICMIC Program, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Neuroimage. 2008 Apr 15;40(3):1130-43. doi: 10.1016/j.neuroimage.2008.01.022. Epub 2008 Jan 29.
Recently, we demonstrated that vessel geometry is a significant determinant of susceptibility-induced contrast in MRI. This is especially relevant for susceptibility-contrast enhanced MRI of tumors with their characteristically abnormal vessel morphology. In order to better understand the biophysics of this contrast mechanism, it is of interest to model how various factors, including microvessel morphology contribute to the measured MR signal, and was the primary motivation for developing a novel computer modeling approach called the Finite Perturber Method (FPM). The FPM circumvents the limitations of traditional fixed-geometry approaches, and enables us to study susceptibility-induced contrast arising from arbitrary microvascular morphologies in 3D, such as those typically observed with brain tumor angiogenesis. Here we describe this new modeling methodology and some of its applications. The excellent agreement of the FPM with theory and the extant susceptibility modeling data, coupled with its computational efficiency demonstrates its potential to transform our understanding of the factors that engender susceptibility contrast in MRI.
最近，我们证明血管几何形状是MRI中磁化率诱导对比度的一个重要决定因素。这对于具有特征性异常血管形态的肿瘤的磁化率对比增强MRI尤为重要。为了更好地理解这种对比机制的生物物理学，模拟包括微血管形态在内的各种因素如何对测量的MR信号产生影响是很有意义的，这也是开发一种名为有限微扰法（FPM）的新型计算机建模方法的主要动机。FPM规避了传统固定几何方法的局限性，使我们能够研究三维中任意微血管形态产生的磁化率诱导对比度，例如脑肿瘤血管生成中通常观察到的那些形态。在这里，我们描述这种新的建模方法及其一些应用。FPM与理论和现有磁化率建模数据的出色一致性，再加上其计算效率，证明了它有可能改变我们对MRI中产生磁化率对比度的因素的理解。
