High-resolution neural network-driven mapping of multiple diffusion metrics leveraging asymmetries in the balanced SSFP frequency profile

TL;DR

This paper introduces a neural network approach to simultaneously estimate multiple diffusion MRI metrics from high-resolution balanced SSFP profiles, enabling detailed whole-brain microstructural mapping at high field strengths.

Contribution

The study presents a novel neural network method that leverages asymmetric bSSFP profiles to estimate multiple diffusion metrics simultaneously with high resolution and reliability.

Findings

Neural network accurately predicts mean diffusivity in white and gray matter.

Good agreement of fractional anisotropy, axial, and radial diffusivity with reference data.

Method provides high-resolution, distortion-free whole-brain diffusion maps at high field strengths.

Abstract

We suggest to utilize the rich information content about microstructural tissue properties entangled in asymmetric balanced steady-state free precession (bSSFP) profiles to estimate multiple diffusion metrics simultaneously by neural network (NN) parameter quantification. A 12-point bSSFP phase-cycling scheme with high-resolution whole-brain coverage is employed at 3 T and 9.4 T for NN input. Low-resolution target diffusion data are derived based on diffusion-weighted spin-echo echo-planar-imaging (SE-EPI) scans, i.e., mean, axial, and radial diffusivity (MD, AD, RD), fractional anisotropy (FA) as well as the spherical coordinates (azimuth ${\Phi}$ and inclination ${\Theta}$) of the principal diffusion eigenvector. A feedforward NN is trained with incorporated probabilistic uncertainty estimation. The NN predictions yielded highly reliable results in white matter (WM) and gray matter (GM) structures for MD. The quantification of FA, AD, and RD was overall in good agreement with the reference but the dependence of these parameters on WM anisotropy was somewhat biased, e.g., in corpus callosum. The inclination ${\Theta}$ was well predicted for anisotropic WM structures while the azimuth ${\Phi}$ was overall poorly predicted. The findings were highly consistent across both field strengths. Application of the optimized NN to high-resolution input data provided whole-brain maps with rich structural details. In conclusion, the proposed NN-driven approach showed potential to provide distortion-free high-resolution whole-brain maps of multiple diffusion metrics at high to ultra-high field strengths in clinically relevant scan times.