A Systematic Benchmark of GAN Architectures for MRI-to-CT Synthesis

• URL: http://arxiv.org/abs/2603.13520v1
• Date: Fri, 13 Mar 2026 18:54:20 GMT
• Title: A Systematic Benchmark of GAN Architectures for MRI-to-CT Synthesis
• Abstract summary: Generative Adversarial Network (GAN) architectures have been proposed for MRI-to-CT translation.<n>We present a benchmark of ten GAN evaluated on the SynthRAD2025 dataset across three anatomical districts.
• Score: 38.31064949197421
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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