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Task-driven acquisition in anisotropic x-ray dark-field tomography

Anisotropic X-ray Dark-field Tomography (AXDT) is a novel imaging modality aimed at the reconstruction of spherical scattering functions in every three-dimensional volume element, based on the directional X-ray dark-field contrast as measured by an X-ray grating interferometer. In this work, we re-derive a detectability index for the AXDT forward model directly using the spherical function formulation, and use it to compute optimized acquisition trajectories using a greedy algorithm. The results demonstrate that the optimized trajectories can represent task-specific features in AXDT accurately using only a fraction of the data.