Comparison of 18F-FDG, 18F-Fluoroacetate, and 18F-FEPPA for Imaging Liver Fibrosis in a Bile Duct-Ligated Rat Model.

Developing sensitive diagnostic methods for a longitudinal evaluation of the status of liver fibrosis is a priority. This study is aimed at assessing the significance of longitudinal positron emission tomography (PET) imaging with 18F-labeling tracers for assessing liver fibrosis in a rat model with bile duct ligation (BDL). Twenty-one 6-week-old Sprague-Dawley male rats were used in this study. Longitudinal PET images using [18F]N-2-(2-fluoroethoxy)benzyl)-N-(4-phenoxypyridin-3-yl)acetamide ([18F]FEPPA) (n = 3), [18F]fluoroacetate ([18F]FAc) (n = 3), and 18F-fluoro-2-deoxy-D-glucose ([18F]FDG) (n = 3) were obtained at 0, 1, and 2 weeks after BDL. Biochemical assays, histological assays, immunohistochemical staining assays, and next generation sequencing analyses were also performed at 0 (n = 3), 1 (n = 3), 2 (n = 3), and 3 (n = 3) weeks after BDL, which demonstrated the severe damage in rat livers after BDL. Regarding [18F]FEPPA and [18F]FDG, there was a significantly higher uptake in the liver after BDL (both P < 0.05), which lasted until week 2. However, the uptake of [18F]FAc in the liver was not significantly different before and after BDL (P = 0.28). Collectively, both [18F]FEPPA and [18F]FDG can serve as sensitive probes for detecting the liver fibrosis. However, [18F]FAc is not recommended to diagnose liver fibrosis.

Deep learning-based workflow for automatic extraction of atria and epicardial adipose tissue on cardiac computed tomography in atrial fibrillation.

BACKGROUND: Preoperative estimation of the volume of the left atrium (LA) and epicardial adipose tissue (EAT) on computed tomography (CT) images is associated with an increased risk of atrial fibrillation (AF) recurrence. We aimed to design a deep learning-based workflow to provide reliable automatic segmentation of the atria, pericardium, and EAT for future applications in the management of AF. METHODS: This study enrolled 157 patients with AF who underwent first-time catheter ablation between January 2015 and December 2017 at Taipei Veterans General Hospital. Three-dimensional (3D) U-Net models of the LA, right atrium (RA), and pericardium were used to develop a pipeline for total, LA-EAT, and RA-EAT automatic segmentation. We defined fat within the pericardium as tissue with attenuation between -190 and -30 HU and quantified the total EAT. Regions between the dilated endocardial boundaries and endocardial walls of the LA or RA within the pericardium were used to detect voxels attributed to fat, thus estimating LA-EAT and RA-EAT. RESULTS: The LA, RA, and pericardium segmentation models achieved Dice coefficients of 0.960 ± 0.010, 0.945 ± 0.013, and 0.967 ± 0.006, respectively. The 3D segmentation models correlated well with the ground truth for the LA, RA, and pericardium ( r = 0.99 and p < 0.001 for all). The Dice coefficients of our proposed method for EAT, LA-EAT, and RA-EAT were 0.870 ± 0.027, 0.846 ± 0.057, and 0.841 ± 0.071, respectively. CONCLUSION: Our proposed workflow for automatic LA, RA, and EAT segmentation using 3D U-Nets on CT images is reliable in patients with AF.