Parallel Swin Transformer-Enhanced 3D MRI-to-CT Synthesis for MRI-Only Radiotherapy Planning

Abstract:MRI provides superior soft tissue contrast without ionizing radiation; however, the absence of electron density information limits its direct use for dose calculation. As a result, current radiotherapy workflows rely on combined MRI and CT acquisitions, increasing registration uncertainty and procedural complexity. Synthetic CT generation enables MRI only planning but remains challenging due to nonlinear MRI-CT relationships and anatomical variability. We propose Parallel Swin Transformer-Enhanced Med2Transformer, a 3D architecture that integrates convolutional encoding with dual Swin Transformer branches to model both local anatomical detail and long-range contextual dependencies. Multi-scale shifted window attention with hierarchical feature aggregation improves anatomical fidelity. Experiments on public and clinical datasets demonstrate higher image similarity and improved geometric accuracy compared with baseline methods. Dosimetric evaluation shows clinically acceptable performance, with a mean target dose error of 1.69%. Code is available at: this https URL.

This preprint has been accepted for publication in the proceedings of the IEEE 23rd International Symposium on Biomedical Imaging (ISBI 2026). The final published version is available at this https URL. The copyright for this work has been transferred to IEEE

Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)

arXiv:2602.05387 [cs.CV]

(or arXiv:2602.05387v2 [cs.CV] for this version)

https://doi.org/10.1109/ISBI61048.2026.11515734 DOI(s) linking to related resources