Deep learning reconstruction allows low-dose imaging while maintaining image quality: comparison of deep learning reconstruction and hybrid iterative reconstruction in contrast-enhanced abdominal CT

Akio Tamura; Eisuke Mukaida; Yoshitaka Ota; Iku Nakamura; Kazumasa Arakita; Kunihiro Yoshioka

doi:10.21037/qims-21-1216

Deep learning reconstruction allows low-dose imaging while maintaining image quality: comparison of deep learning reconstruction and hybrid iterative reconstruction in contrast-enhanced abdominal CT

Quant Imaging Med Surg. 2022 May;12(5):2977-2984. doi: 10.21037/qims-21-1216.

Authors

Akio Tamura¹, Eisuke Mukaida¹, Yoshitaka Ota², Iku Nakamura³, Kazumasa Arakita⁴, Kunihiro Yoshioka¹

Affiliations

¹ Department of Radiology, Iwate Medical University School of Medicine, Iwate, Japan.
² Division of Central Radiology, Iwate Medical University Hospital, Iwate, Japan.
³ Iwate Medical University School of Medicine, Iwate, Japan.
⁴ Healthcare IT Development Center, Canon Medical Systems Corporation, Otawara, Japan.

Abstract

We aimed to compare the radiation dose and image quality of a low-dose abdominal computed tomography (CT) protocol reconstructed with deep learning reconstruction (DLR) with those of a routine-dose protocol reconstructed with hybrid-iterative reconstruction. This retrospective study enrolled 71 patients [61 men; average age, 71.9 years; mean body mass index (BMI), 24.3 kg/m²] who underwent both low-dose abdominal CT with DLR [advanced intelligent clear-IQ engine (AiCE)] and routine-dose abdominal CT with hybrid-iterative reconstruction [adaptive iterative dose reduction 3D (AIDR 3D)]. Radiation dose parameters included volume CT dose index (CTDIvol), effective dose (ED), and size-specific dose estimate (SSDE). Mean image noise and contrast-to-noise ratio (CNR) were calculated. Image noise was measured in the hepatic parenchyma and bilateral erector spinae muscles. Moreover, subjective assessment of perceived image quality and diagnostic acceptability was performed. The low-dose protocol helped reduce the CTDIvol by 44.3%, ED by 43.7%, and SSDE by 44.9%. Moreover, the noise was significantly lower and CNR significantly higher with the low-dose protocol than with the normal-dose protocol (P<0.001). In the subjective assessment of image quality, there was no significant difference between the protocols with regard to image noise. Overall, AiCE was superior to AIDR 3D in terms of diagnostic acceptability (P=0.001). The use of AiCE can reduce overall radiation dose by more than 40% without loss of image quality compared to routine-dose abdominal CT with AIDR 3D.

Keywords: Computed tomography (CT); advanced intelligent clear-IQ engine (AiCE); contrast-to-noise ratio (CNR); deep learning reconstruction (DLR); noise reduction.