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使用纤维球成像评估基于峰度的扩散磁共振成像白质组织模型。

Evaluating kurtosis-based diffusion MRI tissue models for white matter with fiber ball imaging.

作者信息

Jensen Jens H, McKinnon Emilie T, Glenn G Russell, Helpern Joseph A

机构信息

Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, USA.

Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina, USA.

出版信息

NMR Biomed. 2017 May;30(5). doi: 10.1002/nbm.3689. Epub 2017 Jan 13.

Abstract

In order to quantify well-defined microstructural properties of brain tissue from diffusion MRI (dMRI) data, tissue models are typically employed that relate biological features, such as cell morphology and cell membrane permeability, to the diffusion dynamics. A variety of such models have been proposed for white matter, and their validation is a topic of active interest. In this paper, three different tissue models are tested by comparing their predictions for a specific microstructural parameter to a value measured independently with a recently proposed dMRI method known as fiber ball imaging (FBI). The three tissue models are all constructed with the diffusion and kurtosis tensors, and they are hence compatible with diffusional kurtosis imaging. Nevertheless, the models differ significantly in their details and predictions. For voxels with fractional anisotropies (FAs) exceeding 0.5, all three are reasonably consistent with FBI. However, for lower FA values, one of these, called the white matter tract integrity (WMTI) model, is found to be in much better accord with FBI than the other two, suggesting that the WMTI model has a broader range of applicability.

摘要

为了从扩散磁共振成像(dMRI)数据中量化脑组织明确的微观结构特性,通常采用将生物特征(如细胞形态和细胞膜通透性)与扩散动力学相关联的组织模型。针对白质已经提出了多种此类模型,其验证是一个备受关注的热门话题。在本文中,通过将三种不同组织模型对特定微观结构参数的预测结果与使用最近提出的一种称为纤维球成像(FBI)的dMRI方法独立测量的值进行比较,对这三种模型进行了测试。这三种组织模型均由扩散张量和峰度张量构建而成,因此它们与扩散峰度成像兼容。然而,这些模型在细节和预测方面存在显著差异。对于分数各向异性(FA)超过0.5的体素,所有三种模型与FBI的结果都相当一致。然而,对于较低的FA值,其中一种称为白质束完整性(WMTI)模型的模型,被发现比其他两种模型与FBI的结果更吻合,这表明WMTI模型具有更广泛的适用性。

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