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English(EN) Spatio-Temporal Mixture-of-Modality-Experts Diffusion for Quantitative DCE-MRI Synthesis from Incomplete MR Sequences

新的扩散框架可从不完整的序列合成定量DCE-MRI

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了Spatio-Temporal Mixture-of-Modality-Experts (ST-MoME)，一个新颖的扩散框架，旨在从不完整的MR序列合成定量动态对比增强MRI (DCE-MRI) 参数图。该方法通过融合来自各种MRI模态的特征，解决了由于造影剂风险和方案可变性导致DCE-MRI数据不可用的挑战。ST-MoME利用时空门控网络生成体素级别的权重，并使用基于Swin的骨干网络指导图像空间中的去噪过程。在16种不同数据可用性场景下的386名脑肿瘤患者队列中进行评估，ST-MoME在重建DCE参数方面表现出优越的性能，尤其是在关键的肿瘤区域。 AI

影响 这项研究通过从不完整的扫描中合成关键的定量MRI数据，有可能改善肿瘤评估。

排序理由 该集群包含一篇详细介绍一种新的医学图像合成方法的论文。

在 arXiv cs.CV 阅读 →

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新的扩散框架可从不完整的序列合成定量DCE-MRI

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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Junhyeok Lee, Kyu Sung Choi ·

    面向不完整MR序列的定量DCE-MRI合成的时空多模态专家混合扩散模型

    arXiv:2606.25535v1 Announce Type: new Abstract: Quantitative maps from dynamic contrast-enhanced MRI (DCE-MRI) are essential for tumor assessment but are often unavailable due to contrast-agent risks and protocol variability. Prior methods predict these maps from other MRI modali…

  2. arXiv cs.CV TIER_1 English(EN) · Kyu Sung Choi ·

    面向不完整MRI序列的定量DCE-MRI合成的时空模态专家混合扩散模型

    Quantitative maps from dynamic contrast-enhanced MRI (DCE-MRI) are essential for tumor assessment but are often unavailable due to contrast-agent risks and protocol variability. Prior methods predict these maps from other MRI modalities, yet most assume fixed, fully observed inpu…

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