AI 中文总结
本研究提出物理辅助深度学习去噪框架，利用CNN、MLP、LSTM对IMPULSED dMRI信号去噪，使拟合失败率从57.6%降至17.7%，稳定了IMPULSED微环境参数拟合。
AI 中文摘要
扩散加权磁共振成像（dMRI）是量化细胞微环境参数的有力工具。本研究提出一种物理辅助深度学习（DL）去噪框架，旨在提升dMRI信号质量并增强后续生物物理模型拟合的鲁棒性。利用IMPULSED-dMRI信号模型生成成对的无噪声与莱斯噪声污染的dMRI信号数据集，评估三种去噪架构：卷积神经网络（CNN）、多层感知器（MLP）和长短期记忆（LSTM）网络。对去噪后的信号进行拟合以估计细胞直径$d$、细胞内体积分数$V_{\text{in}}$和细胞外表观扩散系数$D_{\text{ex}}$。基于DL的处理显著改善了dMRI信号去噪效果，MLP与LSTM性能相近，LSTM整体表现略优，二者均优于CNN。在后续模型拟合步骤中，LSTM使参数平均绝对误差（MAE）有小幅降低，最主要的益处是拟合稳定性提升，整体拟合失败率从57.6%降至17.7%。该框架可提升dMRI信号质量并稳定后续基于IMPULSED的微环境参数拟合。
英文摘要
Diffusion-weighted MRI (dMRI) is a powerful tool for quantifying cellular microenvironment parameters. This study proposes a physics-assisted deep learning (DL)-based denoising framework designed to enhance dMRI signal quality and improve the robustness of subsequent biophysical model fitting. A dataset of paired noise-free and Rician-noise-corrupted dMRI signals was generated using the IMPULSED-dMRI signal model. Three denoising architectures were evaluated: Convolutional Neural Networks (CNN), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM) networks. Denoised signals were then fitted to estimate cell diameter $d$, intracellular volume fraction $V_{\mathrm{in}}$, and extracellular apparent diffusion coefficient $D_\mathrm{ex}$. DL-based processing substantially improved dMRI signal denoising. The MLP and LSTM achieved similar performance, with the LSTM slightly better overall, and both outperformed the CNN. In the subsequent model fitting step, the LSTM produced modest reductions in parameter MAE. The dominant benefit was fitting stabilization, with the overall fitting failure rate reduced from 57.6\% to 17.7\%. The proposed framework improves dMRI signal quality and stabilizes subsequent IMPULSED-based microenvironmental parameter fitting.
Comments26 pages, 6 figures, 1 table
