对比10种GAN模型，为MRI转CT提供可复现的性能基准。
A Systematic Benchmark of GAN Architectures for MRI-to-CT Synthesis
- 统一训练协议下比较十种GAN架构在三个部位的表现。
- 配对模型优于无配对模型，Pix2Pix平衡性最佳。
- 结果可指导放疗中MRI-only流程的模型选型。
磁共振成像（MRI）到计算机断层扫描（CT）的转换被提出作为支持仅使用MRI临床工作流、同时减少电离辐射暴露的有效方案。尽管已有众多生成对抗网络（GAN）架构用于MRI到CT的转换，但跨异构模型的系统性、公平比较仍较缺乏。本文在SynthRAD2025数据集上对十种GAN架构进行了全面基准测试，覆盖腹部、胸部和头颈部三个解剖区域。所有模型均采用统一验证协议，包含相同的预处理与优化设置。性能通过多种互补指标评估：体素级精度、结构保真度、感知质量及分布层面的真实性，并分析计算复杂度。有监督配对模型始终优于无配对方法，证实了体素级监督的重要性。Pix2Pix在各部位表现均衡，且具备良好的质量-复杂度权衡。多部位联合训练提升了结构鲁棒性，而单部位训练则最大化体素级保真度。该基准为仅使用MRI的放射治疗工作流中的模型选择提供了量化与计算指导，并建立了一个可复现的未来比较研究框架。为确保实验可复现性，代码与全部结果已公开于https://github.com/arco-group/MRI_TO_CT.git。
原文摘要 · Abstract (English)
The translation from Magnetic resonance imaging (MRI) to Computed tomography (CT) has been proposed as an effective solution to facilitate MRI-only clinical workflows while limiting exposure to ionizing radiation. Although numerous Generative Adversarial Network (GAN) architectures have been proposed for MRI-to-CT translation, systematic and fair comparisons across heterogeneous models remain limited. We present a comprehensive benchmark of ten GAN architectures evaluated on the SynthRAD2025 dataset across three anatomical districts (abdomen, thorax, head-and-neck). All models were trained under a unified validation protocol with identical preprocessing and optimization settings. Performance was assessed using complementary metrics capturing voxel-wise accuracy, structural fidelity, perceptual quality, and distribution-level realism, alongside an analysis of computational complexity. Supervised Paired models consistently outperformed Unpaired approaches, confirming the importance of voxel-wise supervision. Pix2Pix achieved the most balanced performance across districts while maintaining a favorable quality-to-complexity trade-off. Multi-district training improved structural robustness, whereas intra-district training maximized voxel-wise fidelity. This benchmark provides quantitative and computational guidance for model selection in MRI-only radiotherapy workflows and establishes a reproducible framework for future comparative studies. To ensure the reproducibility of our experiments we make our code public, together with the overall results, at the following link:https://github.com/arco-group/MRI_TO_CT.git
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