2 research outputs found
A Learning-based Method for Online Adjustment of C-arm Cone-Beam CT Source Trajectories for Artifact Avoidance
During spinal fusion surgery, screws are placed close to critical nerves
suggesting the need for highly accurate screw placement. Verifying screw
placement on high-quality tomographic imaging is essential. C-arm Cone-beam CT
(CBCT) provides intraoperative 3D tomographic imaging which would allow for
immediate verification and, if needed, revision. However, the reconstruction
quality attainable with commercial CBCT devices is insufficient, predominantly
due to severe metal artifacts in the presence of pedicle screws. These
artifacts arise from a mismatch between the true physics of image formation and
an idealized model thereof assumed during reconstruction. Prospectively
acquiring views onto anatomy that are least affected by this mismatch can,
therefore, improve reconstruction quality. We propose to adjust the C-arm CBCT
source trajectory during the scan to optimize reconstruction quality with
respect to a certain task, i.e. verification of screw placement. Adjustments
are performed on-the-fly using a convolutional neural network that regresses a
quality index for possible next views given the current x-ray image. Adjusting
the CBCT trajectory to acquire the recommended views results in non-circular
source orbits that avoid poor images, and thus, data inconsistencies. We
demonstrate that convolutional neural networks trained on realistically
simulated data are capable of predicting quality metrics that enable
scene-specific adjustments of the CBCT source trajectory. Using both
realistically simulated data and real CBCT acquisitions of a
semi-anthropomorphic phantom, we show that tomographic reconstructions of the
resulting scene-specific CBCT acquisitions exhibit improved image quality
particularly in terms of metal artifacts. Since the optimization objective is
implicitly encoded in a neural network, the proposed approach overcomes the
need for 3D information at run-time.Comment: 12 page