Development of pericardial fat count images using a combination of three different deep-learning models

TL;DR

This study developed a deep-learning approach combining three models to generate pericardial fat images from chest X-rays, potentially enabling fat assessment without CT scans.

Contribution

The paper introduces a novel multi-model deep-learning method that outperforms single-model approaches in generating pericardial fat images from chest radiographs.

Findings

Proposed model achieved higher SSIM (0.856) than single model (0.762).

Generated PFCIs showed lower MSE and MAE, indicating better image quality.

Method enables fat assessment without the need for CT imaging.

Abstract

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.