Bayesian Experimental Design for Computed Tomography with the Linearised Deep Image Prior
FOS: Computer and information sciences
Computer Science - Machine Learning
03 medical and health sciences
0302 clinical medicine
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
Machine Learning (cs.LG)
DOI:
10.48550/arxiv.2207.05714
Publication Date:
2022-01-01
AUTHORS (5)
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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