CT respiratory motion synthesis using joint supervised and adversarial learning

arXiv:2404.00163v1 Announce Type: new
Abstract: Objective: Four-dimensional computed tomography (4DCT) imaging consists in reconstructing a CT acquisition into multiple phases to track internal organ and tumor motion. It is commonly used in radiotherapy treatment planning to establish planning target volumes. However, 4DCT increases protocol complexity, may not align with patient breathing during treatment, and lead to higher radiation delivery. Approach: In this study, we propose a deep synthesis method to generate pseudo respiratory CT phases from static images for motion-aware …

abstract acquisition adversarial adversarial learning arxiv complexity cs.cv however imaging multiple patient planning protocol synthesis treatment type