Deep learning reconstruction algorithm and high-concentration contrast medium: feasibility of a double-low protocol in coronary computed tomography angiography

Objective To evaluate radiation dose and image quality of a double-low CCTA protocol reconstructed utilizing high-strength deep learning image reconstructions (DLIR-H) compared to standard adaptive statistical iterative reconstruction (ASiR-V) protocol in non-obese patients. Materials and methods From June to October 2022, consecutive patients, undergoing clinically indicated CCTA, with BMI < 30 kg/m(2) were prospectively included and randomly assigned into three groups: group A (100 kVp, ASiR-V 50%, iodine delivery rate [IDR] = 1.8 g/s), group B (80 kVp, DLIR-H, IDR = 1.4 g/s), and group C (80 kVp, DLIR-H, IDR = 1.2 g/s). High-concentration contrast medium was administered. Image quality analysis was evaluated by two radiologists. Radiation and contrast dose, and objective and subjective image quality were compared across the three groups. Results The final population consisted of 255 patients (64 +/- 10 years, 161 men), 85 per group. Group B yielded 42% radiation dose reduction (2.36 +/- 0.9 mSv) compared to group A (4.07 +/- 1.2 mSv; p < 0.001) and achieved a higher signal-to-noise ratio (30.5 +/- 11.5), contrast-to-noise-ratio (27.8 +/- 11), and subjective image quality (Likert scale score: 4, interquartile range: 3-4) compared to group A and group C (all p <= 0.001). Contrast medium dose in group C (44.8 +/- 4.4 mL) was lower than group A (57.7 +/- 6.2 mL) and B (50.4 +/- 4.3 mL), all the comparisons were statistically different (all p < 0.001). Conclusion DLIR-H combined with 80-kVp CCTA with an IDR 1.4 significantly reduces radiation and contrast medium exposure while improving image quality compared to conventional 100-kVp with 1.8 IDR protocol in non-obese patients.