Anatomy-based quality metric of diffusion-weighted MRI data for accurate derivation of muscle fiber orientation

TL;DR

This paper introduces a new quality metric based on spatial co-localization of diffusion tensor eigenvectors in DW-MRI, enabling better assessment and optimization of muscle fiber orientation detection in soft tissue imaging.

Contribution

It proposes a novel mixing index metric for DW-MRI quality assessment, linking spatial and directional noise, and demonstrates its effectiveness in bipennate muscle imaging.

Findings

High SNR improves fiber orientation clarity

Mixing index correlates with muscle compartment separation

Metric enables protocol optimization for DW-MRI

Abstract

Diffusion-weighted MRI (DW-MRI) is used to quantitatively characterize the microscopic structure of soft tissue due to the anisotropic diffusion of water in muscle. Applications such as fiber tractography or modeling of tumor spread in soft tissue require precise detection of muscle fiber orientation, which is derived from the principal eigenvector of the diffusion tensor. For clinical applications, high image quality and high signal-to-noise ratio (SNR) of DW-MRI for fiber orientation must be balanced with an appropriate scan duration. Muscles with known structural heterogeneity, e.g. bipennate muscles such as the thigh rectus femoris, provide a natural quality benchmark to determine fiber orientation at different scan parameters. Here, we analyze DW-MR images of the thigh of a healthy volunteer at different SNRs and use PCA to identify subsets of voxels with different directions of diffusion tensor eigenvectors. We propose to use the mixing index of spatial co-localization of the clustered eigenvectors as a quality metric for fiber orientation detection. Comparing acquisitions at different SNRs, we find that high SNR results in a low mixing index, reflecting a clear separation of the two compartments of the bipennate muscle on either side of the central tendon. Because the mixing index allows joint estimation of spatial and directional noise in DW-MRI as a single parameter, it will allow future quantitative optimization of DW-MRI protocols for soft tissue.