评估插件式扩散模型在稀疏投影CT中的后验采样能力
Evaluating the Posterior Sampling Ability of Plug&Play Diffusion Methods in Sparse-View CT
- 提出两种后验评估指标，针对多峰后验场景
- 发现投影数减少时，后验近似误差显著增大
- 适合关注医学图像重建可靠性与不确定性建模的研究者
插件式（Plug&Play）扩散模型是计算机断层扫描（CT）重建的前沿方法。这类方法通常适用于弦图包含充足信息、后验分布集中在单一模式的情况，因此常使用图像到图像的评价指标如PSNR/SSIM。然而，我们关注的是从极少数投影的弦图中重建可压缩流场图像，此时后验分布不再集中，甚至呈现多峰特性。为此，本文旨在评估PnP扩散模型的近似后验，并引入两种后验评估性质。我们在三个不同数据集上，对三种PnP扩散方法在多个投影数条件下进行了定量评估。令人惊讶的是，对于每种方法，当投影数减少时，其近似后验均偏离真实后验。
原文摘要 · Abstract (English)
Plug&Play (PnP) diffusion models are state-of-the-art methods in computed tomography (CT) reconstruction. Such methods usually consider applications where the sinogram contains a sufficient amount of information for the posterior distribution to be concentrated around a single mode, and consequently are evaluated using image-to-image metrics such as PSNR/SSIM. Instead, we are interested in reconstructing compressible flow images from sinograms having a small number of projections, which results in a posterior distribution no longer concentrated or even multimodal. Thus, in this paper, we aim at evaluating the approximate posterior of PnP diffusion models and introduce two posterior evaluation properties. We quantitatively evaluate three PnP diffusion methods on three different datasets for several numbers of projections. We surprisingly find that, for each method, the approximate posterior deviates from the true posterior when the number of projections decreases.
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