Evaluation of image quality on low contrast media with deep learning image reconstruction algorithm in prospective ECG-triggering coronary CT angiography

Abstract Purpose To assess the impact of low-dose contrast medium (CM) injection protocol with deep learning image reconstruction (DLIR) algorithm on image quality in coronary CT angiography (CCTA). Methods and Materials 210 patients undergoing CCTA were prospectively and randomly assigned to three groups with different contrast volume protocols (at 320mgI/mL concentration and constant flow rate of 5ml/s): Group A, 0.7mL/kg (n = 70); Group B, 0.6mL/kg (n = 70); Group C, 0.5mL/kg (n = 70). All patients were examined via a prospective ECG-triggered scan protocol within one heartbeat. A high level DLIR (DLIR-H) algorithm was used for image reconstruction with a thickness and interval of 0.625mm. The CT values of ascending aorta (AA), descending aorta (DA), three main coronary arteries, pulmonary artery (PA), and superior vena cava (SVC) were measured and analyzed for objective assessment. Two radiologists assessed the image quality and diagnostic confidence using a 5-point Likert scale. Results The CM doses were 46.81 ± 6.41mL, 41.96 ± 7.51mL and 34.65 ± 5.38mL for Group A, B and C, respectively. The objective assessments on AA, DA and the three main coronary arteries and the overall subjective scoring showed no difference among the three groups (all p > 0.05). Group A had higher enhancements in PA and SVC than groups B and C (all p < 0.05). Conclusions CCTA reconstructed with DLIR could be realized with adequate enhancement in coronary arteries, excellent image quality and diagnostic confidence at low contrast dose of 0.5mL/kg. The use of lower tube voltages may further reduce the contrast dose requirement.