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English(EN) Mamba-driven MRI-to-CT Synthesis for MRI-only Radiotherapy Planning

Mamba架构应用于放射治疗计划中的MRI到CT合成

研究人员已将最初为图像分割设计的SegMamba架构改编用于放射治疗计划的MRI到CT合成。这种新颖的方法利用状态空间建模来捕捉复杂的体积特征和长距离依赖关系,旨在提高准确性,同时与nnU-Net等传统卷积方法相比保持较低的参数数量。在SynthRAD2025数据集上的实验证明了基于Mamba的模型在跨模态医学图像翻译方面的潜力,为其整合到放射治疗工作流程铺平了道路。 AI

影响 探索用于医学成像的新型状态空间模型,有望提高放射治疗计划的准确性和效率。

排序理由 学术论文,详细介绍了状态空间模型架构在医学图像合成中的新颖应用。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

Mamba架构应用于放射治疗计划中的MRI到CT合成

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学术论文,详细介绍了状态空间模型架构在医学图像合成中的新颖应用。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

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

    Mamba驱动的MRI到CT合成用于纯MRI放射治疗计划

    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 …