3D Anatomical Structure-guided Deep Learning for Accurate Diffusion Microstructure Imaging

• URL: http://arxiv.org/abs/2502.17933v1
• Date: Tue, 25 Feb 2025 07:52:51 GMT
• Title: 3D Anatomical Structure-guided Deep Learning for Accurate Diffusion Microstructure Imaging
• Authors: Xinrui Ma, Jian Cheng, Wenxin Fan, Ruoyou Wu, Yongquan Ye, Shanshan Wang,
• Abstract summary: 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.<n> 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$0.004 in estimating parametric maps of multiple diffusion models.
• Score: 7.986579222123474
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Diffusion magnetic resonance imaging (dMRI) is a crucial non-invasive technique for exploring the microstructure of the living human brain. Traditional hand-crafted and model-based tissue microstructure reconstruction methods often require extensive diffusion gradient sampling, which can be time-consuming and limits the clinical applicability of tissue microstructure information. Recent advances in deep learning have shown promise in microstructure estimation; however, accurately estimating tissue microstructure from clinically feasible dMRI scans remains challenging without appropriate constraints. This paper introduces a novel framework that achieves high-fidelity and rapid diffusion microstructure imaging by simultaneously leveraging anatomical information from macro-level priors and mutual information across parameters. This approach enhances time efficiency while maintaining accuracy in microstructure estimation. Experimental results demonstrate that our method outperforms four state-of-the-art techniques, achieving a peak signal-to-noise ratio (PSNR) of 30.51$\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.

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