用解剖结构引导深度学习，15倍提速精准重建脑微结构影像
3D Anatomical Structure-guided Deep Learning for Accurate Diffusion Microstructure Imaging
- 融合宏观解剖先验与参数互信息，约束深度学习优化过程
- 仅需稀疏采样即达30.51分峰值信噪比，较密集采样快15倍
- 适合临床快速脑微结构成像，尤其对扫描时间敏感场景
扩散磁共振成像（dMRI）是无创探索活体人脑微结构的关键技术。传统手工设计和基于模型的组织微结构重建方法通常需要大量扩散梯度采样，耗时长，限制了其临床应用。近年来深度学习在微结构估计中展现出潜力，但在临床可行的dMRI扫描下准确估计仍具挑战，缺乏有效约束。本文提出一种新框架，通过同时利用宏观解剖先验信息和参数间互信息，实现高保真、快速的微结构成像。实验表明，该方法优于四种先进方法，在多个扩散模型参数图估计中达到30.51±0.58的峰值信噪比（PSNR）和0.97±0.004的结构相似性指数（SSIM）。值得注意的是，该方法相比通常使用270个扩散梯度的密集采样方式，实现了15倍加速。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。