Bayesian Experimental Design for Computed Tomography with the Linearised Deep Image Prior

Riccardo Barbano, Johannes Leuschner, Javier Antorán, Bangti Jin, José Miguel Hernández-Lobato
7/11/2022
cs.CVcs.LG

No official code found. Best implementation: educating-dip/bayesian_experimental_design (3 stars, last commit 2023-08-12).

Abstract

We investigate adaptive design based on a single sparse pilot scan for generating effective scanning strategies for computed tomography reconstruction. We propose a novel approach using the linearised deep image prior. It allows incorporating information from the pilot measurements into the angle selection criteria, while maintaining the tractability of a conjugate Gaussian-linear model. On a synthetically generated dataset with preferential directions, linearised DIP design allows reducing the number of scans by up to 30% relative to an equidistant angle baseline.

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educating-dip/bayesian_experimental_designCommunity100%
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Cite this paper

@article{barbano2022bayesian,
  title  = {Bayesian Experimental Design for Computed Tomography with the Linearised Deep Image Prior},
  author = {Riccardo Barbano and Johannes Leuschner and Javier Antorán and Bangti Jin and José Miguel Hernández-Lobato},
  year   = {2022},
  eprint = {2207.05714},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2207.05714v1}
}

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