用3D双分支Transformer提升MRI转CT精度，支持纯MRI放疗规划
Parallel Swin Transformer-Enhanced 3D MRI-to-CT Synthesis for MRI-Only Radiotherapy Planning
- 采用卷积+双Swin Transformer架构，兼顾局部细节与全局上下文
- 在公开和临床数据上实现更高图像相似度与1.69%的靶区剂量误差
- 适合需要简化流程、避免辐射暴露的放疗规划研究者使用
MRI提供优异软组织对比度且无电离辐射，但缺乏电子密度信息，无法直接用于剂量计算。当前放疗流程需结合MRI与CT扫描，增加配准不确定性和操作复杂性。合成CT生成可实现纯MRI放疗规划，但受限于MRI-CT间的非线性关系及解剖变异。本文提出Parallel Swin Transformer-Enhanced Med2Transformer，一种3D架构，融合卷积编码与双Swin Transformer分支，分别建模局部解剖细节与长程上下文依赖。多尺度移位窗口注意力与分层特征聚合提升了解剖保真度。在公开及临床数据集上的实验表明，该方法相比基线模型具有更高的图像相似性与几何精度。剂量学评估显示性能符合临床要求，平均靶区剂量误差为1.69%。代码已开源：https://github.com/mobaidoctor/med2transformer。
原文摘要 · Abstract (English)
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.
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