Deep Learning-Based Reconstruction vs. Iterative Reconstruction for Quality of Low-Dose Head-and-Neck CT Angiography with Different Tube-Voltage Protocols in Emergency-Department Patients

Abstract

Objective: To compare the image quality of computed tomography angiography of the supra-aortic arteries (CTSA) at different tube voltages in low doses settings with deep learning-based image reconstruction (DLR) vs. hybrid iterative reconstruction (H-IR). Methods: We retrospectively reviewed 102 patients who underwent CTSA systematically reconstructed with both DLR and H-IR. We assessed the image quality both quantitatively and qualitatively at 11 arterial segmental levels and 3 regional levels. Radiation-dose parameters were recorded and the effective dose was calculated. Eighty-six patients were eligible for analysis Of these patients, 27 were imaged with 120 kVp, 30 with 100 kVp, and 29 with 80 kVp. Results: The effective dose in 120 kVp, 100 kVp and 80 kVp was 1.5 ± 0.4 mSv, 1.1 ± 0.3 mSv and 0.68 ± 0.1 mSv, respectively (p < 0.01). Comparing 80 kVp + DLR vs. 120 and 100 kVp + H-IR CT scans, the mean overall arterial attenuation was about 64% and 34% higher (625.9 ± 118.5 HU vs. 382.3 ± 98.6 HU and 468 ± 118.5 HU; p < 0.01) without a significant difference in terms of image noise (17.7 ± 4.9 HU vs. 17.5 ± 5.2; p = 0.7 and 18.1 ± 5.4; p = 0.3) and signal-to-ratio increased by 59% and 33%, respectively (37.9 ± 12.3 vs. 23.8 ± 9.7 and 28.4 ± 12.5). This protocol also provided superior image quality in terms of qualitative parameters, compared to standard-kVp protocols with H-IR. Highest subjective image-quality grades for vascular segments close to the aorta were obtained with the 100 kVp + DLR protocol. Conclusions: DLR significantly reduced image noise and improved the overall image quality of CTSA with both low and standard tube voltages and at all vascular segments. CT that was acquired with 80 kVp and reconstructed with DLR yielded better overall image quality compared to higher kVp values with H-IR, while reducing the radiation dose by half, but it has limitations for arteries that are close to the aortic arch.

Improved image quality in CT pulmonary angiography using deep learning-based image reconstruction

“…We applied DLIR to CTPA studies in suspected PE. Our results are in line with those that have been described for various cardiovascular CT applications including coronary CTA 19 – 23 , CT for planning of transcatheter aortic valve repair 24 , 25 , CT of the aorta 26 , head and neck CT angiography 27 and triple-rule-out CT 28 . In the field of thoracic imaging, several previous studies have investigated the value of DLIR with regards to pulmonary imaging and lung nodule detection.…”

“…Previously, DLIR has been shown to improve image denoising and increase image quality beyond the levels of achieved with iterative reconstruction techniques in various cardiovascular CT applications including coronary CTA 18 – 23 , CT for planning of transcatheter aortic valve repair 24 , 25 , CT of the aorta 26 , head and neck CT angiography 27 and triple-rule-out CT 28 . There are very limited data about DLIR in CTPA despite this being one of the most commonly performed cardiovascular CT examinations.…”

Application of deep learning image reconstruction-high algorithm in one-stop coronary and carotid-cerebrovascular CT angiography with low radiation and contrast doses

“…Patients in both groups underwent the non-contrast CT first. After the contrast injection, all patients underwent a coronary CTA with prospective electrocardiogram gating, followed by carotid-cerebrovascular scanning after a delay of 1.5 s. Following published articles, the coronary CTA scanning used 80 kVp and smart mA for noise index (NI) of 36 for the DLIR-H group, and 100 kVp and smart mA for NI of 25 for the ASIR-V50% group ( 23 - 26 ). In both groups, the coronary CTA was acquired within one heartbeat triggered using the automatic gating technique, with the best systolic and diastolic phases recommended by the device and snapshot freeze cardiac motion correction technique activated: for patients with a heart rate (HR) <66 bpm, images were collected at the 70–80% of the R-R cardiac interval.…”

Computed Tomography 2.0

“…Detectability of simulated lung lesions was best with the smoothest level in DLR; a dose reduction potential of 81% to 94% was assumed. An overview of recently published articles on deep learning–based image reconstruction 5,9,47–87 is given in Table 5.…”