  Anisotropic X-ray Dark-field Tomography (AXDT) is a recently developed
imaging modality that enables the visualization of oriented microstructures
using lab-based X-ray grating interferometer setups. While there are very
promising application scenarios, for example in materials testing of fibrous
composites or in medical diagnosis of brain cell connectivity, AXDT faces
challenges in practical applicability due to the complex and time-intensive
acquisitions required to fully sample the anisotropic X-ray scattering
functions. However, depending on the specific imaging task at hand, a full
sampling may not be required, allowing for reduced acquisitions. In this work
we are investigating a performance prediction approach for AXDT using
task-specific detectability indices. Based on this approach we present a
task-driven acquisition optimization method that enables reduced acquisition
schemes while keeping the task-specific image quality high. We demonstrate the
feasibility and efficacy of the method in experiments with simulated and
experimental data.
