无需标注数据，通过迭代优化实现扩散MRI去噪，提升图像质量和下游任务表现。
Self-Supervised Diffusion MRI Denoising via Iterative and Stable Refinement
- 利用扩散过程后段信息与自适应采样，构建单阶段自监督去噪框架。
- 在真实与模拟数据上均达到当前最优性能，显著提升微结构建模与纤维追踪精度。
- 适合临床影像处理、神经科学研究等需高精度MRI数据的场景。
磁共振成像（MRI），尤其是扩散MRI（dMRI），常因信噪比低而牺牲时间或空间分辨率以缓解影响，但此类折衷难以满足临床对效率与精度的双重需求。因此，去噪成为关键预处理步骤，尤其在缺乏干净数据的情况下。本文提出Di-Fusion，一种完全自监督的去噪方法，利用扩散过程的后期步骤及自适应采样机制。与以往方法不同，该单阶段框架无需额外噪声模型训练，具备高效且稳定的训练能力，并在采样过程中实现自适应与可控性。在真实与模拟数据上的全面实验表明，Di-Fusion在微结构建模、纤维追踪及其他下游任务中均达到领先性能。代码已开源：https://github.com/FouierL/Di-Fusion。
原文摘要 · Abstract (English)
Magnetic Resonance Imaging (MRI), including diffusion MRI (dMRI), serves as a ``microscope'' for anatomical structures and routinely mitigates the influence of low signal-to-noise ratio scans by compromising temporal or spatial resolution. However, these compromises fail to meet clinical demands for both efficiency and precision. Consequently, denoising is a vital preprocessing step, particularly for dMRI, where clean data is unavailable. In this paper, we introduce Di-Fusion, a fully self-supervised denoising method that leverages the latter diffusion steps and an adaptive sampling process. Unlike previous approaches, our single-stage framework achieves efficient and stable training without extra noise model training and offers adaptive and controllable results in the sampling process. Our thorough experiments on real and simulated data demonstrate that Di-Fusion achieves state-of-the-art performance in microstructure modeling, tractography tracking, and other downstream tasks. Code is available at https://github.com/FouierL/Di-Fusion.
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。