ArXiv Preprint
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.
Jiaming Liu, Rushil Anirudh, Jayaraman J. Thiagarajan, Stewart He, K. Aditya Mohan, Ulugbek S. Kamilov, Hyojin Kim
2022-11-22
