Improvement in image quality in obese patients by using a new x-ray tube and Deep Learning Image Reconstruction in Coronary Computed Tomography Angiography

Abstract

Purpose: When performing coronary computed tomography angiography (CCTA) in obese patients, it is a challenge to obtain diagnostic image quality. Theoretically, higher tube currents improve image quality in obese patients by reducing noise, but this comes with an increased radiation as a trade-off. Deep learning image reconstruction (DLIR) is a technique that should reduce noise and improve image quality without the need of a higher radiation dose. This study assessed the impact of higher tube currents and DLIR on image quality, diagnostic utility, and noise. Methods: The study consisted of 51 symptomatic obese (BMI > 30 kg/m 2 ) patients with low to moderate risk of coronary artery disease (CAD). All patients underwent CCTA twice, first with the Revolution CT scanner and then with the upgraded Revolution Apex scanner with the ability to increase tube currents. Images were reconstructed using ASiR-V 50% and DLIR and evaluated by an observer using a Likert score. Results: Image quality was significantly improved with the Revolution Apex scanner and reconstruction with DLIR, resulting in an odds ratio of 1,23 (p = 0,017), and noise was significantly reduced by 41%. Diagnostic utility was also improved with 88% vs. 69% “diagnostic” image sets of those performed with Revolution Apex + DLIR and Revolution Apex/CT + ASiR-V, respectively. No significant differences were found between Revolution CT and Revolution Apex when reconstructing with ASiR-V. Conclusion: In obese patients, the combination of higher tube currents and reconstruction with DLIR significantly improves the subjective image quality and diagnostic utility and reduces noise.