Learning to Avoid Poor Images: Towards Task-aware C-arm Cone-beam CT Trajectories
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Date
2019
Publication Type
Conference Paper
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yes
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Abstract
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. © 2019, Springer Nature Switzerland AG.
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published
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Book title
Medical Image Computing and Computer Assisted Intervention – MICCAI 2019
Journal / series
Volume
11768
Pages / Article No.
11 - 19
Publisher
Springer
Event
10th International Workshop on Machine Learning in Medical Imaging (MLMI 2019) held in conjunction with 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2019)
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Subject
Robotic imaging; Deep reinforcement learning