Multi-phase enhanced computed tomography (MPECT) translation from plain CT can help doctors to detect the liver lesion and prevent patients from the allergy during MPECT examination. Existing CT translation methods directly learn an end-to-end mapping from plain CT to MPECT, ignoring the crucial clinical domain knowledge. As clinicians subtract the plain CT from MPECT images as subtraction image to highlight the contrast-enhanced regions and further to facilitate liver disease diagnosis in the clinical diagnosis, we aim to exploit this domain knowledge for automatic CT translation. To this end, we propose a Mask-Aware Transformer (MAFormer) with structure invariant loss for CT translation, which presents the first effort to exploit this domain knowledge for CT translation. Specifically, the proposed MAFormer introduces a mask estimator to predict the subtraction image from the plain CT image. To integrate the subtraction image into the network, the MAFormer devises a Mask-Aware Transformer based Normalization (MATNorm) as normalization layer to highlight the contrast-enhanced regions and capture the long-range dependencies among these regions. Moreover, aiming to preserve the biological structure of CT slices, a structure invariant loss is designed to extract the structural information and minimize the structural similarity between the plain and synthetic CT images to ensure the structure invariant. Extensive experiments have proven the effectiveness of the proposed method and its superiority to the state-of-the-art CT translation methods. Source code is to be released.

中文翻译：

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用于 CT 转换的具有结构不变损耗的掩模感知变压器


平扫 CT 的多时相增强计算机断层扫描 (MPECT) 可以帮助医生在 MPECT 检查期间发现肝脏病变并预防患者过敏。现有的 CT 翻译方法直接学习从普通 CT 到 MPECT 的端到端映射，忽略了关键的临床领域知识。由于临床医生从 MPECT 图像中减去平扫 CT 作为减影图像，以突出对比增强区域，并进一步促进临床诊断中的肝病诊断，我们的目标是利用该领域知识进行自动 CT 翻译。为此，我们提出了一种用于 CT 翻译的具有结构不变损失的 Mask-Aware Transformer (MAFormer)，这是利用该领域知识进行 CT 翻译的首次努力。具体来说，所提出的 MAFormer 引入了掩模估计器来预测平扫 CT 图像的减影图像。为了将减法图像集成到网络中，MAFormer 设计了基于掩模感知变换器的归一化 (MATNorm) 作为归一化层，以突出显示对比度增强区域并捕获这些区域之间的远程依赖性。此外，为了保留CT切片的生物结构，设计了结构不变损失来提取结构信息并最小化普通CT图像和合成CT图像之间的结构相似性以确保结构不变。大量实验证明了该方法的有效性及其优于最先进的 CT 翻译方法。源代码即将发布。