Simultaneous q-Space Sampling Optimization and Reconstruction for Fast and High-fidelity Diffusion Magnetic Resonance Imaging

TL;DR

This paper introduces SSOR, a framework that optimizes q-space sampling and reconstruction in diffusion MRI, significantly reducing scan times while maintaining high image quality and robustness to noise.

Contribution

The paper presents a novel joint optimization and reconstruction method for q-space sampling in dMRI, combining spherical harmonic representation with regularization techniques.

Findings

Achieves high-quality diffusion MRI with reduced scan times

Demonstrates robustness to noise in experimental results

Outperforms existing methods in quantitative and qualitative metrics

Abstract

Diffusion Magnetic Resonance Imaging (dMRI) plays a crucial role in the noninvasive investigation of tissue microstructural properties and structural connectivity in the \textit{in vivo} human brain. However, to effectively capture the intricate characteristics of water diffusion at various directions and scales, it is important to employ comprehensive q-space sampling. Unfortunately, this requirement leads to long scan times, limiting the clinical applicability of dMRI. To address this challenge, we propose SSOR, a Simultaneous q-Space sampling Optimization and Reconstruction framework. We jointly optimize a subset of q-space samples using a continuous representation of spherical harmonic functions and a reconstruction network. Additionally, we integrate the unique properties of diffusion magnetic resonance imaging (dMRI) in both the q-space and image domains by applying $l1$-norm and total-variation regularization. The experiments conducted on HCP data demonstrate that SSOR has promising strengths both quantitatively and qualitatively and exhibits robustness to noise.