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Multi-atlas-based CT synthesis from conventional MRI with patch-based refinement for MRI-based radiotherapy planning

机译：常规MRI的基于多图集的CT合成以及基于补丁的优化用于基于MRI的放射治疗计划

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Accurate CT synthesis, sometimes called electron density estimation, from MRI is crucial for successful MRI-based radiotherapy planning and dose computation. Existing CT synthesis methods are able to synthesize normal tissues but are unable to accurately synthesize abnormal tissues (i.e., tumor), thus providing a suboptimal solution. We propose a multi-atlas-based hybrid synthesis approach that combines multi-atlas registration and patch-based synthesis to accurately synthesize both normal and abnormal tissues. Multi-parametric atlas MR images are registered to the target MR images by multi-channel deformable registration, from which the atlas CT images are deformed and fused by locally-weighted averaging using a structural similarity measure (SSIM). Synthetic MR images are also computed from the registered atlas MRIs by using the same weights used for the CT synthesis; these are compared to the target patient MRIs allowing for the assessment of the CT synthesis fidelity. Poor synthesis regions are automatically detected based on the fidelity measure and refined by a patch-based synthesis. The proposed approach was tested on brain cancer patient data, and showed a noticeable improvement for the tumor region.
机译：MRI的准确CT合成（有时也称为电子密度估计）对于成功进行基于MRI的放射治疗计划和剂量计算至关重要。现有的CT合成方法能够合成正常组织，但是不能准确地合成异常组织（即肿瘤），因此提供了次优的解决方案。我们提出了一种基于多图集的混合合成方法，该方法结合了多图集配准和基于贴片的合成来准确合成正常组织和异常组织。通过多通道可变形配准将多参数地图集MR图像配准到目标MR图像，然后使用结构相似性度量（SSIM）通过局部加权平均将地图集CT图像变形并融合。还可以通过使用与CT合成相同的权重，从已注册的图集MRI计算出合成MR图像；将这些与目标患者的MRI进行比较，以评估CT合成保真度。可根据保真度度量自动检测不良的合成区域，并通过基于补丁的合成对其进行完善。对脑癌患者的数据进行了测试，结果表明肿瘤区域有明显改善。

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