Magnetic resonance imaging (MRI) is gaining popularity in guiding radiation treatment for intrahepatic cancers due to its superior soft tissue contrast and potential of monitoring individual motion and liver function. This study investigates a deep learning-based method that generates synthetic CT volumes from T1-weighted MR Dixon images in support of MRI-based intrahepatic radiotherapy treatment planning. Training deep neutral networks for this purpose has been challenged by mismatches between CT and MR images due to motion and different organ filling status. This work proposes to resolve such challenge by generating ‘semi-synthetic’ CT images from rigidly aligned CT and MR image pairs. Contrasts within skeletal elements of the ‘semi-synthetic’ CT images were determined from CT images, while contrasts of soft tissue and air volumes were determined from voxel-wise intensity classification results on MR images. The resulting ‘semi-synthetic’ CT images were paired with their corre...

中文翻译：

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使用经过“半合成” CT数据训练的U型网络从MR Dixon图像生成腹部合成CT

磁共振成像（MRI）由于其优越的软组织对比度以及监视个体运动和肝功能的潜力，因此在指导肝内癌的放射治疗中越来越受欢迎。这项研究调查了一种基于深度学习的方法，该方法可从T1加权MR Dixon图像生成合成CT量，以支持基于MRI的肝内放疗治疗计划。由于运动和不同的器官充盈状态，CT和MR图像之间的不匹配挑战了为此目的训练深层中性网络。这项工作建议通过从严格对齐的CT和MR图像对生成“半合成” CT图像来解决此类挑战。根据CT图像确定“半合成” CT图像的骨骼元素内的对比度，而软组织和空气体积的对比是根据MR图像上按体素的强度分类结果确定的。将生成的“半合成” CT图像与其相应的图像配对。