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

@article{Liu2022DOLCEAM,
  title={DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction},
  author={Jiaming Liu and Rushil Anirudh and Jayaraman J. Thiagarajan and Stewart He and K. Aditya Mohan and Ulugbek S. Kamilov and Hyojin Kim},
  journal={ArXiv},
  year={2022},
  volume={abs/2211.12340}
}
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… 

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