Assessment of Image Quality of Coronary Computed Tomography Angiography in Obese Patients by Comparing Deep Learning Image Reconstruction With Adaptive Statistical Iterative Reconstruction Veo

Objective The aim of the study was to evaluate the image quality of coronary computed tomography (CT) angiography (CCTA) in obese patients by using deep learning image reconstruction (DLIR) in comparison with adaptive statistical iterative reconstruction Veo (ASiR-V). Methods We prospectively evaluated 60 obese patients (body mass index [BMI] ≥ 30 kg/m2) who underwent coronary CT angiography in a single center. All CT scans were performed with GE Revolution 256-row CT at 120 kV (group A; 20 men, 10 women; mean age = 54.3 years; mean BMI = 33.4 kg/m2) or 100 kV (group B; 18 men; 12 women; mean age = 56.8 years; mean BMI = 32.9 kg/m2). Images in group A were reconstructed using ASiR-V, whereas images in group B were reconstructed using ASiR-V, DLIR-medium (DLIR-M), and DLIR-high (DLIR-H). Three blinded independent readers assessed the subjective image quality and measured the objective image quality. Radiation dose estimates were calculated and compared between patients by using 0.014 and 0.026 mSv·mGy−1 cm−1 corresponding to chest and heart conversion coefficients, respectively. Results The subjective score was significantly higher for images reconstructed using 120-kV ASiR-V (3.8), DLIR-M (3.9), and DLIR-H (4.0) compared with those reconstructed using 100-kV ASiR-V (3.5). Image noise was significantly lower in images reconstructed using DLIR-H compared with those reconstructed using other reconstruction algorithm (P
Source: Journal of Computer Assisted Tomography - Category: Radiology Tags: Cardiovascular: Angiography Source Type: research