针对钠磁共振图像噪声特性，提出新型去噪模型提升成像质量。
Rician Denoising Diffusion Probabilistic Models For Sodium Breast MRI Enhancement
- 将瑞利噪声转换为高斯噪声，改进扩散模型以适配钠MRI
- 在无参考评估下，去噪效果优于传统DDPM与CNN方法
- 适合从事医学影像增强、尤其是钠MRI研究的学者使用
钠MRI是一种用于体内可视化和量化钠浓度的成像技术，在多种生物过程及乳腺癌表征中具有潜在应用价值。然而，相较于常规质子MRI，其信号噪声比（SNR）和空间分辨率均较低。深度学习中的去噪扩散概率模型（DDPM）虽在多种去噪任务中表现优异，但主要针对高斯噪声，难以应对钠MRI特有的瑞利噪声，易导致特征失真。本文提出瑞利去噪扩散概率模型（RDDPM），在去噪过程中每一步将瑞利噪声转换为高斯噪声。通过三项无参考图像质量评估指标验证，RDDPM在性能上持续优于标准DDPM及其他基于CNN的去噪方法。
原文摘要 · Abstract (English)
Sodium MRI is an imaging technique used to visualize and quantify sodium concentrations in vivo, playing a role in many biological processes and potentially aiding in breast cancer characterization. Sodium MRI, however, suffers from inherently low signal-to-noise ratios (SNR) and spatial resolution, compared with conventional proton MRI. A deep-learning method, the Denoising Diffusion Probabilistic Models (DDPM), has demonstrated success across a wide range of denoising tasks, yet struggles with sodium MRI's unique noise profile, as DDPM primarily targets Gaussian noise. DDPM can distort features when applied to sodium MRI. This paper advances the DDPM by introducing the Rician Denoising Diffusion Probabilistic Models (RDDPM) for sodium MRI denoising. RDDPM converts Rician noise to Gaussian noise at each timestep during the denoising process. The model's performance is evaluated using three non-reference image quality assessment metrics, where RDDPM consistently outperforms DDPM and other CNN-based denoising methods.
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