  Diagnostic stroke imaging with C-arm cone-beam computed tomography (CBCT)
enables reduction of time-to-therapy for endovascular procedures. However, the
prolonged acquisition time compared to helical CT increases the likelihood of
rigid patient motion. Rigid motion corrupts the geometry alignment assumed
during reconstruction, resulting in image blurring or streaking artifacts. To
reestablish the geometry, we estimate the motion trajectory by an autofocus
method guided by a neural network, which was trained to regress the
reprojection error, based on the image information of a reconstructed slice.
The network was trained with CBCT scans from 19 patients and evaluated using an
additional test patient. It adapts well to unseen motion amplitudes and
achieves superior results in a motion estimation benchmark compared to the
commonly used entropy-based method.
