@inproceedings{02052b9752dc43b3971db73428a76bf3,
title = "Learning to Avoid Poor Images: Towards Task-aware C-arm Cone-beam CT Trajectories",
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.",
keywords = "Deep reinforcement learning, Robotic imaging",
author = "Zaech, {Jan Nico} and Cong Gao and Bastian Bier and Russell Taylor and Andreas Maier and Nassir Navab and Mathias Unberath",
note = "Funding Information: We gratefully acknowledge support of the NVIDIA Corporation for donating GPUs, and Gerhard Kleinzig and Sebastian Vogt from SIEMENS for making an ARCADIS Orbic 3D available. JNZ was supported by a DAAD FITweltweit fellowship. Funding Information: Acknowledgement. We gratefully acknowledge support of the NVIDIA Corporation for donating GPUs, and Gerhard Kleinzig and Sebastian Vogt from SIEMENS for making an ARCADIS Orbic 3D available. JNZ was supported by a DAAD FITweltweit fellowship. Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 ; Conference date: 13-10-2019 Through 17-10-2019",
year = "2019",
doi = "10.1007/978-3-030-32254-0_2",
language = "English (US)",
isbn = "9783030322533",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "11--19",
editor = "Dinggang Shen and Pew-Thian Yap and Tianming Liu and Peters, {Terry M.} and Ali Khan and Staib, {Lawrence H.} and Caroline Essert and Sean Zhou",
booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings",
address = "Germany",
}