通过磁共振物理参数估计实现磁共振图像的分解(英文)

Decomposition of Magnetic Resonance Images by Estimating MR Physical Parameters

近年来,单谱与多谱磁共振图像的分割方法研究已经取得了很大进展,并应用于正常年龄的脑发育和脑疾病的诊断研究,例如老年痴呆综合征、脑损伤、脑肿瘤等的临床研究等。据此可以通过多谱MR图像获得多种对比度信息,更加准确表达人体组织及其病理情况。现已提出的大多数方法,把组织分割问题考虑成统计决策、模式识别和聚类、图像处理和分析等问题。在这些方法中用于组织分割的特征主要是单谱/多谱图像的灰度值/矢量,它们不能直接反映组织的物理特征。而且,这些方法将组织分割问题表述成组织磁共振图像中像素不同成分的有限集合。所以,这些组织分割的方法所取得的结果在某种意义上是不够合理的。这篇论文提出了一种基于磁共振图像谱分解的新的组织分割方法,该方法将组织分割问题考虑成组织的磁共振物理参数的估计问题。这个方法不仅用于磁共振成像中脂肪信号的抑止,也用于核磁共振谱中的水信号抑止。因此,这个方法可以称为空间和谱MR成像。

Tissue segmentation of single and multi-spectral magnetic resonance （MR） images has been widely studied for the applications on normal aging brain, as well as on the diagnosis studies of Alzheimer＇s disease （AD）, brain trauma and tumor in the recent years. But, the most of proposed methods in the published papers, the tissue segmentation was considered as problems of statistical decision, pattern classification, cluttering, image processing and analysis. The parameters used for tissue segmentation in those methods were the gray scalar/vector in single/multi-spectral images, which indirectly reflected the physical characteristics of the tissue. And those methods addressed the problems of tissue segmentation as the partitioning concourse of the components in a pixel in finite sets. So the results of the tissue segmentation obtained by conventional methods were unreasonable in some sense. This paper presents a new method of tissue segmentation based on the principle of spectroscopic decomposition of MR images, which consider the tissue segmentation as the problem of the estimation of MR physical parameters of the issues. This method can be used for suppressing not only fat MR signal in magnetic resonance imaging （MRI） but also water signal in magnetic resonance spectroscopy （MRS）. Thus, this method is called the spatial and spectral MR imaging.