  Metal artifacts in computed tomography (CT) arise from a mismatch between
physics of image formation and idealized assumptions during tomographic
reconstruction. These artifacts are particularly strong around metal implants,
inhibiting widespread adoption of 3D cone-beam CT (CBCT) despite clear
opportunity for intra-operative verification of implant positioning, e.g. in
spinal fusion surgery. On synthetic and real data, we demonstrate that much of
the artifact can be avoided by acquiring better data for reconstruction in a
task-aware and patient-specific manner, and describe the first step towards the
envisioned task-aware CBCT protocol. The traditional short-scan CBCT trajectory
is planar, with little room for scene-specific adjustment. We extend this
trajectory by autonomously adjusting out-of-plane angulation. This enables
C-arm source trajectories that are scene-specific in that they avoid acquiring
"poor images", characterized by beam hardening, photon starvation, and noise.
The recommendation of ideal out-of-plane angulation is performed on-the-fly
using a deep convolutional neural network that regresses a detectability-rank
derived from imaging physics.
