Contrast-enhanced computed tomography (CECT) is constantly used for delineating organs-at-risk (OARs) in radiation therapy planning. The delineated OARs are needed to transfer from CECT to non-contrast CT (NCCT) for dose calculation. Yet, the use of iodinated contrast agents (CA) in CECT and the dose calculation errors caused by the spatial misalignment between NCCT and CECT images pose risks of adverse side effects. A promising solution is synthesizing CECT images from NCCT scans, which can improve the visibility of organs and abnormalities for more effective multi-organ segmentation in NCCT images. However, existing methods neglect the difference between tissues induced by CA and lack the ability to synthesize the details of organ edges and blood vessels. To address these issues, we propose a contrast-enhanced knowledge and anatomical perception network (CKAP-Net) for NCCT-to-CECT synthesis. CKAP-Net leverages a contrast-enhanced knowledge learning network to capture both similarities and dissimilarities in domain characteristics attributable to CA. Specifically, a CA-based perceptual loss function is introduced to enhance the synthesis of CA details. Furthermore, we design a multi-scale anatomical perception transformer that utilizes multi-scale anatomical information from NCCT images, enabling the precise synthesis of tissue details. Our CKAP-Net is evaluated on a multi-center abdominal NCCT-CECT dataset, a head an neck NCCT-CECT dataset, and an NCMRI-CEMRI dataset. It achieves a MAE of 25.96 ± 2.64, a SSIM of 0.855 ± 0.017, and a PSNR of 32.60 ± 0.02 for CECT synthesis, and a DSC of 81.21 ± 4.44 for segmentation on the internal dataset. Extensive experiments demonstrate that CKAP-Net outperforms state-of-the-art CA synthesis methods and has better generalizability across different datasets.

中文翻译：

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NCCT-to-CECT 合成，具有对比增强知识和解剖感知，用于非对比 CT 图像中的多器官分割


在放射治疗计划中，对比增强计算机断层扫描 （CECT） 经常用于描绘风险器官 （OAR）。需要将描绘的 OAR 从 CECT 转移到非对比 CT （NCCT） 以进行剂量计算。然而，在 CECT 中使用碘造影剂 （CA） 以及 NCCT 和 CECT 图像之间的空间错位引起的剂量计算误差会带来不良副作用的风险。一个很有前途的解决方案是从 NCCT 扫描中合成 CECT 图像，这可以提高器官和异常的可见性，从而在 NCCT 图像中更有效地进行多器官分割。然而，现有的方法忽略了 CA 诱导的组织之间的差异，并且缺乏合成器官边缘和血管细节的能力。为了解决这些问题，我们提出了一种用于 NCCT 到 CECT 合成的对比增强知识和解剖感知网络 （CKAP-Net）。CKAP-Net 利用对比增强的知识学习网络来捕获可归因于 CA 的领域特征的相似性和不相似性。具体来说，引入了基于 CA 的感知损失函数来增强 CA 细节的综合。此外，我们设计了一个多尺度解剖感知变压器，它利用来自 NCCT 图像的多尺度解剖信息，能够精确合成组织细节。我们的 CKAP-Net 在多中心腹部 NCCT-CECT 数据集、头颈部 NCCT-CECT 数据集和 NCMRI-CEMRI 数据集上进行评估。它在内部数据集上实现了 25.96 ± 2.64 的 MAE，0.855 ± 0.017 的 SSIM，以及 32.60 ± 0.02 的 PSNR，以及 DSC 的 81.21 ± 4.44。 广泛的实验表明，CKAP-Net 优于最先进的 CA 合成方法，并且在不同的数据集中具有更好的泛化性。