Deep Learning-Based Augmented Contrast-Enhancement and Denoising for Reduced-Iodine and Low-Radiation 70-kVp Cerebral CT Angiography: A Prospective Study

Objective: To evaluate the feasibility of cerebral computed tomography angiography (CTA) obtained with reduced iodine and low radiation at 70 kVp and the effect of deep learning-based augmented contrast enhancement (DL-ACE) and denoising (DL-DN) algorithms on the CTA quality. Materials and Methods:In this prospective study, 47 healthy volunteers (male:female, 31:16; mean age ± standard deviation, 57.8 ± 10.9 years) were randomly assigned to one of three CTA protocols: Group A (n = 16; 100 kVp, 40 mL of 350 mgI/mL), Group B (n = 16; 70 kVp, 40 mL of 270 mgI/mL), and Group C (n = 15; 70 kVp, 28 mL of 270 mgI/mL [ultralow iodine]), with an injection rate of 2.5 mL/s for all.Images were reconstructed using filtered back projection (FBP), and images in Groups B and C were additionally reconstructed using DL-ACE and DL-DN.Arterial attenuation, image noise, contrast-to-noise ratio (CNR), and subjective image quality were compared among five image sets.Results: Compared with Group A, Groups B and C received 23.7% lower radiation doses.With FBP, arterial attenuation was significantly higher in Groups B (435.8 ± 50.2 Hounsfield units [HU]) and C (391.8 ± 52.1 HU) than in Group A (321.1 ± 47.4 HU) (P < 0.001), while CNR did not differ significantly (Group A, 19.9 ± 4.7; Group B, 20.3 ± 3.8; and Group C, 18.4 ± 4.6) due to higher image noise in Groups B and C.After applying DL-ACE and DL-DN in Groups B and C, arterial attenuation increased by 45.4% and image noise decreased by 34.5%, resulting in significantly higher arterial attenuation, CNR, and subjective image quality compared with Group A (P < 0.001).Conclusion: Cerebral CTA at 70-kVp using ultralow iodine enhanced arterial attenuation but increased image noise compared with the 100-kVp CTA protocol.DL-ACE and DL-DN significantly increased arterial attenuation and reduced image noise, resulting in higher CNR and better subjective image quality.