Population‐based Bayesian regularization for microstructural diffusion MRI with NODDIDA
Population‐based Bayesian regularization for microstructural diffusion MRI with NODDIDA
复制标题
使用 NODDIDA 对微结构扩散 MRI 进行基于群体的贝叶斯正则化
DOI：
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发表时间：
2019
影响因子：
3.3
通讯作者：
Alejandro F Frangi
中科院分区：
文献类型：
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作者：
Meghdoot Mozumder;J. Pozo;S. Coelho;Alejandro F Frangi
有关大脑微观结构的信息可以通过扩散磁共振成像（dMRI）来探测。神经突取向色散和密度成像与扩散率评估（NODDIDA）是提出的最简单的微观结构模型之一。然而，从临床上合理的 dMRI 采集中估计 NODDIDA 参数是不合适的，不同的参数集可以同样好地描述相同的测量结果。解决这个问题的一些方法侧重于为这种非凸优化开发更好的优化策略。然而，这并不能从根本上解决不适定问题。本文介绍了贝叶斯估计框架，该框架通过对健康成年人群体（以下称为基于群体的先验）的广泛 dMRI 测量集的知识进行规范化。
Information on the brain microstructure can be probed by Diffusion Magnetic Resonance Imaging (dMRI). Neurite Orientation Dispersion and Density Imaging with Diffusivities Assessment (NODDIDA) is one of the simplest microstructural model proposed. However, the estimation of the NODDIDA parameters from clinically plausible dMRI acquisition is ill‐posed, and different parameter sets can describe the same measurements equally well. A few approaches to resolve this problem focused on developing better optimization strategies for this non‐convex optimization. However, this fundamentally does not resolve ill‐posedness. This article introduces a Bayesian estimation framework, which is regularized through knowledge from an extensive dMRI measurement set on a population of healthy adults (henceforth population‐based prior).
影响因子：
5.7
作者：
Reisert, Marco;Kellner, Elias;Kiselev, Valerij G.
通讯作者：
Kiselev, Valerij G.