From Promise to Practical Reality: Transforming Diffusion MRI Analysis with Fast Deep Learning Enhancement

TL;DR

This paper introduces FastFOD-Net, a deep learning framework that significantly enhances diffusion MRI fiber orientation estimates, enabling faster, more reliable clinical analysis across various neurological conditions.

Contribution

The work presents a novel, optimized deep learning method for FOD enhancement, validated on clinical data, improving accuracy and efficiency over previous approaches.

Findings

FastFOD-Net is 60 times faster than previous methods.

It performs reliably on clinical data from healthy and neurological disorder subjects.

The framework reduces measurement errors, aiding clinical diagnosis and research.

Abstract

Fiber orientation distribution (FOD) is an advanced diffusion MRI modeling technique that represents complex white matter fiber configurations, and a key step for subsequent brain tractography and connectome analysis. Its reliability and accuracy, however, heavily rely on the quality of the MRI acquisition and the subsequent estimation of the FODs at each voxel. Generating reliable FODs from widely available clinical protocols with single-shell and low-angular-resolution acquisitions remains challenging but could potentially be addressed with recent advances in deep learning-based enhancement techniques. Despite advancements, existing methods have predominantly been assessed on healthy subjects, which have proved to be a major hurdle for their clinical adoption. In this work, we validate a newly optimized enhancement framework, FastFOD-Net, across healthy controls and six neurological disorders. This accelerated end-to-end deep learning framework enhancing FODs with superior performance and delivering training/inference efficiency for clinical use ($60\times$ faster comparing to its predecessor). With the most comprehensive clinical evaluation to date, our work demonstrates the potential of FastFOD-Net in accelerating clinical neuroscience research, empowering diffusion MRI analysis for disease differentiation, improving interpretability in connectome applications, and reducing measurement errors to lower sample size requirements. Critically, this work will facilitate the more widespread adoption of, and build clinical trust in, deep learning based methods for diffusion MRI enhancement. Specifically, FastFOD-Net enables robust analysis of real-world, clinical diffusion MRI data, comparable to that achievable with high-quality research acquisitions.