Mamba-driven MRI-to-CT Synthesis for MRI-only Radiotherapy Planning

• URL: http://arxiv.org/abs/2603.23295v1
• Date: Tue, 24 Mar 2026 14:56:39 GMT
• Title: Mamba-driven MRI-to-CT Synthesis for MRI-only Radiotherapy Planning
• Abstract summary: Mamba is a state-space model for cross-modality translation.<n>We adapt both the U-Mamba and the SegMamba architecture, originally proposed for segmentation, to perform cross-modality image generation.<n>Our 3D Mamba architecture effectively captures complex features and long-range volumetric dependencies, thus allowing accurate CT synthesis.
• Score: 4.3531418255555545
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Radiotherapy workflows for oncological patients increasingly rely on multi-modal medical imaging, commonly involving both Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). MRI-only treatment planning has emerged as an attractive alternative, as it reduces patient exposure to ionizing radiation and avoids errors introduced by inter-modality registration. While nnU-Net-based frameworks are predominantly used for MRI-to-CT synthesis, we explore Mamba-based architectures for this task, aiming to showcase the advantages of state-space modeling for cross-modality translation compared to standard convolutional neural networks. Specifically, we adapt both the U-Mamba and the SegMamba architecture, originally proposed for segmentation, to perform cross-modality image generation. Our 3D Mamba architecture effectively captures complex volumetric features and long-range dependencies, thus allowing accurate CT synthesis while maintaining fast inference times. Experiments were conducted on a subset of SynthRAD2025 dataset, comprising registered single-channel MRI-CT volume pairs across three anatomical regions. Quantitative evaluation is performed via a combination of image similarity metrics computed in Hounsefield Units (HU) and segmentation-based metrics obtained from TotalSegmentator to ensure geometric consistency is preserved. The findings pave the way for the integration of state-space models into radiotherapy workflows.

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