Nonstop gated cone-beam computed tomography for respiratory gating pancreatic cancer radiotherapy using dual-domain deep learning reconstruction.

Background and purpose: Radiotherapy for pancreatic cancer requires effective respiratory motion management and precise image guidance due to the proximity of multiple dose-limiting organs. Respiratory gating is commonly used for patients who cannot tolerate breath hold, with gated cone-beam computed tomography (gCBCT) routinely employed to verify gating thresholds and patient positioning. However, the gCBCT scans are lengthy (2-8 min). This study investigates the feasibility of a novel nonstop gated CBCT (ngCBCT) technique as a replacement for current clinical gCBCT, reducing scan time to ∼1 min and decreasing imaging dose by >40%. Materials and methods: Clinical gCBCT scans from 15 pancreatic cancer patients (58 half-fan and 98 full-fan scans) were retrospectively collected. The ngCBCT projections were emulated by alternate-cycle down-sampling gCBCT projection data. A dual-domain convolutional neural network (DDCNN) was optimized for pancreatic cancer imaging and evaluated for ngCBCT reconstruction. Results: DDCNN-based ngCBCT reconstructions achieved image quality comparable to gCBCT, preserving soft-tissue contrast and anatomical details. The projection-domain CNN played a vital role in yielding high image quality, while the image-domain CNN provided additional modest improvements to the reconstruction. Incorporating a 15% dropout rate to DDCNN further enhanced model robustness and generalizability. Conclusions: This work demonstrates the first successful application of ngCBCT to pancreatic cancer imaging. By achieving high-quality reconstructions with a one-minute scan, ngCBCT has the potential to transform respiratory gating pancreas radiotherapy, offering improved patient comfort and enhanced treatment efficiency. The approach paves the way for extending ngCBCT to other abdominal sites impacted by respiratory motion.