DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction
This work addresses image quality and uncertainty estimation in LACT, which is used in security and medical applications, representing a novel method for a known bottleneck rather than an incremental improvement.
The authors tackled the problem of severe artifacts in limited-angle computed tomography (LACT) reconstruction by introducing DOLCE, a model-based probabilistic diffusion framework that integrates data-consistency with a conditional diffusion model, achieving state-of-the-art performance on real datasets and enabling uncertainty quantification.
Limited-Angle Computed Tomography (LACT) is a non-destructive evaluation technique used in a variety of applications ranging from security to medicine. The limited angle coverage in LACT is often a dominant source of severe artifacts in the reconstructed images, making it a challenging inverse problem. We present DOLCE, a new deep model-based framework for LACT that uses a conditional diffusion model as an image prior. Diffusion models are a recent class of deep generative models that are relatively easy to train due to their implementation as image denoisers. DOLCE can form high-quality images from severely under-sampled data by integrating data-consistency updates with the sampling updates of a diffusion model, which is conditioned on the transformed limited-angle data. We show through extensive experimentation on several challenging real LACT datasets that, the same pre-trained DOLCE model achieves the SOTA performance on drastically different types of images. Additionally, we show that, unlike standard LACT reconstruction methods, DOLCE naturally enables the quantification of the reconstruction uncertainty by generating multiple samples consistent with the measured data.