Reliability-weighted patch learning for synthetic CT reconstruction in head and neck adaptive radiotherapy: A proof-of-concept study.
Authors
Affiliations (7)
Affiliations (7)
- Department of Radiation Oncology, Mayo Clinic, Jacksonville, Florida, USA.
- Medical Physics and Biomedical Engineering Lab (MPBEL), Yonsei University College of Medicine, Seoul, South Korea.
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, South Korea.
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
- Ewha Medical Research Institute, School of Medicine, Ewha Womans University, Seoul, South Korea.
- Ewha Medical Artificial Intelligence Research Institute, Ewha Womans University College of Medicine, Seoul, South Korea.
- Department of Bioengineering, School of Medicine, Ewha Womans University, Seoul, South Korea.
Abstract
Cone-beam computed tomography (CBCT) is essential for adaptive radiotherapy (ART) but is limited by poor image quality. Existing deep learning methods for synthetic CT (sCT) generation face a critical trade-off: diffusion models offer high fidelity but are impractically slow, while fast-inference networks often lack structural accuracy and optimization stability. To develop HQ-PatchNet, a reliability-driven deep learning framework designed to improve CBCT-to-sCT image quality while maintaining rapid inference speed for online ART workflows. HQ-PatchNet utilizes a vision transformer (ViT)-integrated CycleGAN architecture. It introduces a reliability-driven, SSIM-based selective patch weighting mechanism controlled by a temperature decay schedule. This dynamically suppresses artifact-dominated regions and focuses optimization on anatomically reliable structures. The model was evaluated on 304 paired head-and-neck (HN) scans against conventional networks and recent diffusion models using quantitative metrics (MAE, SSIM, NCC, PSNR), computational speed, and clinical dosimetric accuracy (3D gamma index, 2%/2 mm). HQ-PatchNet improved image quality, reducing Mean Absolute Error (MAE) from 44.81 ± 11.24 HU to 11.85 ± 4.31 HU and achieving an SSIM of 0.960 ± 0.055, comparable to state-of-the-art diffusion models. Inference required only 6.42 ± 1.71 s per volume, compared to > 15 min for diffusion approaches. The model reduced severe metal and truncation artifacts, and representative qualitative assessment suggested visually consistent sCT generation under interfractional anatomical variation. Dosimetric assessment of complex HN IMRT plans yielded a high average gamma passing rate of 99.32 ± 0.56%. HQ-PatchNet provides a computationally efficient and clinically promising approach for daily CBCT-guided online ART by improving CBCT-to-sCT image quality while maintaining rapid inference speed. Further contour-based, multi-institutional, and multi-site validation remains necessary to confirm its broader clinical utility.