  Rationale and Objectives: Pericardial fat (PF), the thoracic visceral fat
surrounding the heart, promotes the development of coronary artery disease by
inducing inflammation of the coronary arteries. For evaluating PF, this study
aimed to generate pericardial fat count images (PFCIs) from chest radiographs
(CXRs) using a dedicated deep-learning model.
  Materials and Methods: The data of 269 consecutive patients who underwent
coronary computed tomography (CT) were reviewed. Patients with metal implants,
pleural effusion, history of thoracic surgery, or that of malignancy were
excluded. Thus, the data of 191 patients were used. PFCIs were generated from
the projection of three-dimensional CT images, where 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.
  Results: The mean SSIM, MSE, and MAE were as follows: 0.856, 0.0128, and
0.0357, respectively, for the proposed model; and 0.762, 0.0198, and 0.0504,
respectively, for the single CycleGAN-based model.
  Conclusion: PFCIs generated from CXRs with the proposed model showed better
performance than those with the single model. PFCI evaluation without CT may be
possible with the proposed method.

Image and Video Processing;Computer Vision and Pattern Recognition