Parallel Swin Transformer-Enhanced 3D MRI-to-CT Synthesis for MRI-Only Radiotherapy Planning
This addresses the need for MRI-only radiotherapy planning to reduce registration uncertainty and procedural complexity in clinical workflows, though it appears incremental as an enhancement to existing transformer-based methods.
The paper tackles the problem of generating synthetic CT images from MRI scans for radiotherapy planning, where current methods require both MRI and CT acquisitions. Their proposed Parallel Swin Transformer-Enhanced Med2Transformer architecture achieves a mean target dose error of 1.69%, demonstrating clinically acceptable performance.
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: https://github.com/mobaidoctor/med2transformer.