A Deep Learning Approach to Multi-Fiber Parameter Estimation and Uncertainty Quantification in Diffusion MRI.

ArXiv Pub Date : 2025-02-28
William Consagra, Lipeng Ning, Yogesh Rathi
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Abstract

Diffusion MRI (dMRI) is the primary imaging modality used to study brain microstructure in vivo. Reliable and computationally efficient parameter inference for common dMRI biophysical models is a challenging inverse problem, due to factors such as variable dimensionalities (reflecting the unknown number of distinct white matter fiber populations in a voxel), low signal-to-noise ratios, and non-linear forward models. These challenges have led many existing methods to use biologically implausible simplified models to stabilize estimation, for instance, assuming shared microstructure across all fiber populations within a voxel. In this work, we introduce a novel sequential method for multi-fiber parameter inference that decomposes the task into a series of manageable subproblems. These subproblems are solved using deep neural networks tailored to problem-specific structure and symmetry, and trained via simulation. The resulting inference procedure is largely amortized, enabling scalable parameter estimation and uncertainty quantification across all model parameters. Simulation studies and real imaging data analysis using the Human Connectome Project (HCP) demonstrate the advantages of our method over standard alternatives. In the case of the standard model of diffusion, our results show that under HCP-like acquisition schemes, estimates for extra-cellular parallel diffusivity are highly uncertain, while those for the intra-cellular volume fraction can be estimated with relatively high precision.

弥散MRI中多纤维参数估计与不确定度量化的深度学习方法。
弥散MRI (Diffusion MRI, dMRI)是研究活体脑结构的主要成像方式。由于诸如可变维度(反映一个体素中不同白质纤维种群的未知数量)、低信噪比和非线性正演模型等因素,对常见dMRI生物物理模型进行可靠且计算效率高的参数推断是一个具有挑战性的逆问题。这些挑战导致许多现有方法使用生物学上不可信的简化模型来稳定估计,例如,假设一个体素内所有纤维种群的微观结构共享。在这项工作中,我们引入了一种新的多光纤参数推理的顺序方法,该方法将任务分解为一系列可管理的子问题。这些子问题使用深度神经网络来解决,这些神经网络是根据特定问题的结构和对称性定制的,并通过模拟进行训练。由此产生的推理过程在很大程度上是平摊的,使所有模型参数的可扩展参数估计和不确定性量化成为可能。使用人类连接体项目(HCP)的模拟研究和真实成像数据分析证明了我们的方法优于标准替代方法。在标准扩散模型的情况下,我们的研究结果表明,在类hcp采集方案下,细胞外平行扩散率的估计具有高度不确定性,而细胞内体积分数的估计具有相对较高的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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