IMPACT OF KNOWLEDGE-BASED ITERATIVE MODEL RECONSTRUCTION ON THE FEASIBILITY OF CORONARY COMPUTED TOMOGRAPHY ANGIOGRAPHY IN OBESE PATIENTS

Abstract

Background: Obesity is a common clinical problem that can complicate investigation of coronary artery disease (CAD). Knowledge-based iterative model reconstruction (IMR) represents a novel reconstruction technique in coronary computed tomography angiography (CCTA) with reduced radiation and improved image quality. The purpose of this study is to assess the feasibility of IMR-CCTA in patients with a Body Mass Index (BMI) ≥ 30 kg/m2. Methods: We evaluated 407 symptomatic patients between January 2015 and March 2017 undergoing CCTA on a Philips iCT 256 slice scanner with an IMR algorithm. An experienced interpreter evaluated for non-diagnostic segments, severity (>50% stenosis) and plaque morphology in a standard 16-segment model. Diagnostic scans were determined by need for additional functional or anatomical assessment due to non-definite segment severity. Logistic regression was used for data analysis. Results: Diagnostic scans were comparable between both BMI groups (89%/88%). Subgroup analysis (BMI≥30) revealed improved diagnostic ability with lower age 40) but with increased radiation dose. Conclusion: CCTA represents an excellent modality to investigate CAD, and a knowledge-based IMR algorithm demonstrates high diagnostic ability in patients with increased BMI (≥ 30). Feasibility of diagnostic scans is also high in morbidly obese patients (BMI>40) but with increased radiation dose.