3D Anatomical Structure-guided Deep Learning for Accurate Diffusion Microstructure Imaging
Journal:
arXiv
Published Date:
Feb 25, 2025
Abstract
Diffusion magnetic resonance imaging (dMRI) is a crucial non-invasive
technique for exploring the microstructure of the living human brain.
Traditional hand-crafted and model-based tissue microstructure reconstruction
methods often require extensive diffusion gradient sampling, which can be
time-consuming and limits the clinical applicability of tissue microstructure
information. Recent advances in deep learning have shown promise in
microstructure estimation; however, accurately estimating tissue microstructure
from clinically feasible dMRI scans remains challenging without appropriate
constraints. This paper introduces a novel framework that achieves
high-fidelity and rapid diffusion microstructure imaging by simultaneously
leveraging anatomical information from macro-level priors and mutual
information across parameters. This approach enhances time efficiency while
maintaining accuracy in microstructure estimation. Experimental results
demonstrate that our method outperforms four state-of-the-art techniques,
achieving a peak signal-to-noise ratio (PSNR) of 30.51$\pm$0.58 and a
structural similarity index measure (SSIM) of 0.97$\pm$0.004 in estimating
parametric maps of multiple diffusion models. Notably, our method achieves a
15$\times$ acceleration compared to the dense sampling approach, which
typically utilizes 270 diffusion gradients.
