Quantitative Detection of Pericardial Adhesions Using Four-Dimensional Computed Tomography: A Novel Motion-Based Analysis Framework - Tong Ren; Shuo Wang; Nan Cheng; Zekun Feng; Menglu Li; Li Zhang; Rong Wang (Bioengineering)
Objective: Pericardial adhesions can unexpectedly occur prior to cardiac surgery or catheter ablation, even in patients without known risk factors, potentially increasing procedural risks. This study proposed and validated a novel, quantitative, and noninvasive method for detecting pericardial adhesions using four-dimensional computed tomography (4D CT). Methods: We evaluated preoperative 4D CT datasets from 20 patients undergoing cardiac surgery with and without pericardial adhesions. Our novel approach integrates expert-guided pericardial segmentation, symmetric diffeomorphic registration, and motion disparity analysis. The method quantifies tissue motion differences by computing the displacement fields between the pericardium and epicardial adipose tissue (EAT), with a particular focus on the left anterior descending (LAD) region. Results: Statistical analysis revealed significant differences between adhesion and non-adhesion groups (p < 0.01) using two newly developed metrics: peak ratio (PR) and distribution width index (DWI). Adhesion cases demonstrated characteristic high PR values (>100) with low DWI values (<0.3), while non-adhesion cases showed moderate PR values (<50) with higher DWI values (>0.4). Conclusions: This proof-of-concept study validated a novel quantitative framework for assessing pericardial adhesions using 4D CT imaging and provides an objective and computationally efficient tool for preoperative assessment in clinical settings. These findings suggest the potential clinical utility of this framework in surgical planning and risk assessment.