用压缩模型和2D扩散模型实现3D光谱CT材料分解，节省内存且精度更高。
Volumetric Material Decomposition Using Spectral Diffusion Posterior Sampling with a Compressed Polychromatic Forward Model
- 分层处理3D体积，结合压缩正向模型与预训练2D扩散模型
- 在临床级体积上实现高精度材料分解，优于InceptNet等深度学习方法
- 适合需要快速、准确三维材料分离的医学影像研究者
此前我们提出了光谱扩散后验采样（Spectral DPS）框架，通过融合解析光谱系统模型与大规模数据学习的先验，实现一步式精准材料分解。本文将2D Spectral DPS扩展至3D，通过使用预训练2D扩散模型进行逐切片处理，并采用压缩多色正向模型，在保证物理建模准确性的同时解决高内存需求问题。仿真研究表明，所提出的内存高效3D Spectral DPS可实现临床意义体积规模的材料分解。定量分析显示，Spectral DPS在对比度量化、切片间连续性及分辨率保持方面均优于InceptNet和条件DDPM等深度学习算法。本研究为推进体积分光CT的一步式材料分解奠定了基础。
原文摘要 · Abstract (English)
We have previously introduced Spectral Diffusion Posterior Sampling (Spectral DPS) as a framework for accurate one-step material decomposition by integrating analytic spectral system models with priors learned from large datasets. This work extends the 2D Spectral DPS algorithm to 3D by addressing potentially limiting large-memory requirements with a pre-trained 2D diffusion model for slice-by-slice processing and a compressed polychromatic forward model to ensure accurate physical modeling. Simulation studies demonstrate that the proposed memory-efficient 3D Spectral DPS enables material decomposition of clinically significant volume sizes. Quantitative analysis reveals that Spectral DPS outperforms other deep-learning algorithms, such as InceptNet and conditional DDPM in contrast quantification, inter-slice continuity, and resolution preservation. This study establishes a foundation for advancing one-step material decomposition in volumetric spectral CT.
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