  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.
