Evaluating the Posterior Sampling Ability of Plug&Play Diffusion Methods in Sparse-View CT
This addresses the challenge of accurate uncertainty estimation in medical imaging for sparse data scenarios, but it is incremental as it focuses on evaluation rather than proposing a new method.
The paper tackled the problem of evaluating the posterior sampling ability of Plug&Play diffusion models in sparse-view CT reconstruction, finding that these models deviate from the true posterior as the number of projections decreases, based on quantitative evaluation across three datasets and methods.
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.