arXiv Machine Learning August 5, 2026 By Ivo Herzig, Pascal Paysan, Daniel Barco, Marc Andr\'e Stadelmann, Frank-Peter Schilling, Igor Peterlik, Michal Walczak, Lijin Aryananda, Woo Sang Ahn, Rudolf Marcel F\"uchslin, Lukas Lichtensteiger

Dual-domain U-Nets with embedded back projection operators for motion-resolved 4D CBCT reconstruction

Read the original on arXiv Machine Learning →

arXiv:2608. 03430v1 Announce Type: cross Abstract: Four-dimensional cone beam CT (4D CBCT) is important for image-guided radiation therapy of thoracic cancers, but its use is limited by long scan times, causing high patient dose and motion/sparse-sampling artifacts.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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