Anatomically informed deep learning framework for generating fast, low-dose synthetic CBCT for prostate radiotherapy.
Authors
Affiliations (7)
Affiliations (7)
- Department of Medical Radiation Physics, Lund University, Lund, Sweden. [email protected].
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden. [email protected].
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden.
- Medical Radiation Physics, Department of Translational Medicine, Lund University, Malmö, Sweden.
- Department of Medical Radiation Physics, Lund University, Lund, Sweden.
- Centre for Mathematical Sciences, Lund University, Lund, Sweden.
- Department of Radiooncology, Rostock University Medical Center, Rostock, Germany.
Abstract
Precise patient positioning and daily anatomical verification are crucial in external beam radiotherapy to ensure accurate dose delivery and minimize harm to healthy tissues. However, Current image-guided radiotherapy techniques struggle to balance high-quality volumetric anatomical visualization and rapid low-dose imaging. Addressing this, reconstructing volumetric images from ultra-sparse X-ray projections holds promise for significantly reducing patient radiation exposure and potentially enabling real-time anatomy verification. Here, we present a novel DL-based framework that generates synthetic volumetric cone-beam CT in real-time from two orthogonal projection views and a reference planning CT for prostate cancer patients. Our model learns the mapping between 2D and 3D domains and generalizes across patients without retraining. We demonstrate that our framework produces high-fidelity volumetric reconstructions in real-time, potentially supporting clinical workflows without hardware modifications. This approach could reduce imaging dose and treatment time while preserving comprehensive anatomical information, offering a pathway for safer, more efficient prostate radiotherapy workflows. The online version contains supplementary material available at 10.1038/s41598-025-23781-7.