基于3D残差扩散模型，提升PET/MR图像去噪效果与细节保留。
Volumetric Conditional Score-based Residual Diffusion Model for PET/MR Denoising
- 采用条件分数残差设计，结合3D块训练策略优化体积数据处理。
- 在临床数据上实现更优的去噪效果，峰值信噪比显著提升。
- 适合医学影像分析、多模态成像研究者使用，尤其关注图像保真度。
PET成像能定量评估分子与生理过程，但其固有的高噪声水平严重影响图像解读与定量分析。随着深度学习发展，基于扩散模型的去噪方法表现优异，但在处理体积数据时仍存在局限。现有模型往往忽视PET的3D特性，导致解剖一致性丢失。本文提出的条件分数残差扩散（CSRD）模型通过改进得分函数与3D块训练策略，有效降低计算开销并加速去噪过程。该模型融合PET与MRI的体积数据，保持空间一致性和解剖细节。实验表明，CSRD在定性与定量评估中均优于现有先进方法，显著提升图像质量且保留关键细节。
原文摘要 · Abstract (English)
PET imaging is a powerful modality offering quantitative assessments of molecular and physiological processes. The necessity for PET denoising arises from the intrinsic high noise levels in PET imaging, which can significantly hinder the accurate interpretation and quantitative analysis of the scans. With advances in deep learning techniques, diffusion model-based PET denoising techniques have shown remarkable performance improvement. However, these models often face limitations when applied to volumetric data. Additionally, many existing diffusion models do not adequately consider the unique characteristics of PET imaging, such as its 3D volumetric nature, leading to the potential loss of anatomic consistency. Our Conditional Score-based Residual Diffusion (CSRD) model addresses these issues by incorporating a refined score function and 3D patch-wise training strategy, optimizing the model for efficient volumetric PET denoising. The CSRD model significantly lowers computational demands and expedites the denoising process. By effectively integrating volumetric data from PET and MRI scans, the CSRD model maintains spatial coherence and anatomical detail. Lastly, we demonstrate that the CSRD model achieves superior denoising performance in both qualitative and quantitative evaluations while maintaining image details and outperforms existing state-of-the-art methods.
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