NCCT-to-CECT synthesis with contrast-enhanced knowledge and anatomical perception for multi-organ segmentation in non-contrast CT images

Abstract

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.