心包脂肪（PF）——心脏周围的胸腔内脏脂肪——通过诱发冠状动脉炎症来促进冠状动脉疾病的发展。为了评估 PF，我们使用专用的深度学习模型从胸部 X 光片 (CXR) 生成心包脂肪计数图像 (PFCI)。我们回顾了 269 名连续接受冠状动脉计算机断层扫描 (CT) 的患者的数据。我们排除了有金属植入物、胸腔积液、胸外科手术史或恶性肿瘤的患者。因此，使用了 191 名患者的数据。我们通过三维 CT 图像的投影生成 PFCI，其中脂肪堆积由高像素值表示。所提出的方法结合了包括 CycleGAN 在内的三种不同的深度学习模型，从 CXR 生成 PFCI。使用基于 CycleGAN 的单一模型从 CXR 生成 PFCI，以便与所提出的方法进行比较。为了评估生成的 PFCI 的图像质量，（i）使用所提出的方法生成的 PFCI 和（ii）生成的 PFCI 的结构相似性指数测量（SSIM）、均方误差（MSE）和平均绝对误差（MAE）使用单个模型进行比较。对于所提出的模型，平均 SSIM、MSE 和 MAE 分别为 8.56 × 10-1、1.28 × 10-2 和 3.57 × 10-2，以及 7.62 × 10-1、1.98对于基于 CycleGAN 的单个模型，分别为 × 10-2 和 5.04 × 10-2。使用所提出的模型从 CXR 生成的 PFCI 显示出比使用单个模型生成的性能更好的性能。使用所提出的方法可以在没有 CT 的情况下评估 PF。版权所有 © 2023 大学放射科医生协会。由爱思唯尔公司出版。保留所有权利。

Pericardial fat (PF)-the thoracic visceral fat surrounding the heart-promotes the development of coronary artery disease by inducing inflammation of the coronary arteries. To evaluate PF, we generated pericardial fat count images (PFCIs) from chest radiographs (CXRs) using a dedicated deep-learning model.We reviewed data of 269 consecutive patients who underwent coronary computed tomography (CT). We excluded patients with metal implants, pleural effusion, history of thoracic surgery, or malignancy. Thus, the data of 191 patients were used. We generated PFCIs from the projection of three-dimensional CT images, wherein fat accumulation was represented by a high pixel value. Three different deep-learning models, including CycleGAN were combined in the proposed method to generate PFCIs from CXRs. A single CycleGAN-based model was used to generate PFCIs from CXRs for comparison with the proposed method. To evaluate the image quality of the generated PFCIs, structural similarity index measure (SSIM), mean squared error (MSE), and mean absolute error (MAE) of (i) the PFCI generated using the proposed method and (ii) the PFCI generated using the single model were compared.The mean SSIM, MSE, and MAE were 8.56 × 10-1, 1.28 × 10-2, and 3.57 × 10-2, respectively, for the proposed model, and 7.62 × 10-1, 1.98 × 10-2, and 5.04 × 10-2, respectively, for the single CycleGAN-based model.PFCIs generated from CXRs with the proposed model showed better performance than those generated with the single model. The evaluation of PF without CT may be possible using the proposed method.Copyright © 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.