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Limited-Angle Computed Tomography (LACT) is a non-destructive evaluationtechnique used in a variety of applications ranging from security to medicine.The limited angle coverage in LACT is often a dominant source of severeartifacts in the reconstructed images, making it a challenging inverse problem.We present DOLCE, a new deep model-based framework for LACT that uses aconditional diffusion model as an image prior. Diffusion models are a recentclass of deep generative models that are relatively easy to train due to theirimplementation as image denoisers. DOLCE can form high-quality images fromseverely under-sampled data by integrating data-consistency updates with thesampling updates of a diffusion model, which is conditioned on the transformedlimited-angle data. We show through extensive experimentation on severalchallenging real LACT datasets that, the same pre-trained DOLCE model achievesthe SOTA performance on drastically different types of images. Additionally, weshow that, unlike standard LACT reconstruction methods, DOLCE naturally enablesthe quantification of the reconstruction uncertainty by generating multiplesamples consistent with the measured data.

DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction