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Mamba architecture adapted for MRI-to-CT synthesis in radiotherapy planning

Researchers have adapted the SegMamba architecture, originally designed for image segmentation, to perform MRI-to-CT synthesis for radiotherapy planning. This novel approach utilizes state-space modeling to capture complex volumetric features and long-range dependencies, aiming to improve accuracy while maintaining a lower parameter count compared to traditional convolutional methods like nnU-Net. Experiments on the SynthRAD2025 dataset demonstrated the potential of Mamba-based models for cross-modality medical image translation, paving the way for their integration into radiotherapy workflows. AI

IMPACT Explores novel state-space models for medical imaging, potentially improving radiotherapy planning accuracy and efficiency.

RANK_REASON Academic paper detailing a novel application of a state-space model architecture for medical image synthesis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Mamba architecture adapted for MRI-to-CT synthesis in radiotherapy planning

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Academic paper detailing a novel application of a state-space model architecture for medical image synthesis. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Konstantinos Barmpounakis, Theodoros P. Vagenas, Maria Vakalopoulou, George K. Matsopoulos ·

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

    arXiv:2603.23295v2 Announce Type: replace 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 …