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

TL;DR

This paper introduces a novel 3D MRI-to-CT synthesis method using a parallel Swin Transformer-enhanced architecture, improving image quality and dosimetric accuracy for MRI-only radiotherapy planning.

Contribution

It presents a new hybrid architecture combining convolutional encoding with dual Swin Transformer branches for improved MRI-CT synthesis.

Findings

Higher image similarity and geometric accuracy compared to baseline methods

Clinically acceptable dosimetric performance with 1.69% mean target dose error

Effective modeling of local and long-range dependencies in 3D medical images

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: https://github.com/mobaidoctor/med2transformer.