The influence of a deep learning image reconstruction algorithm on the image quality and auto-analysis of pulmonary nodules at ultra-low dose chest CT: a phantom study

Yue Yao; Baobin Guo; Jianying Li; Quanxin Yang; Xiaohui Li; Lei Deng

doi:10.21037/qims-21-815

The influence of a deep learning image reconstruction algorithm on the image quality and auto-analysis of pulmonary nodules at ultra-low dose chest CT: a phantom study

Quant Imaging Med Surg. 2022 May;12(5):2777-2791. doi: 10.21037/qims-21-815.

Authors

Yue Yao¹, Baobin Guo¹, Jianying Li², Quanxin Yang¹, Xiaohui Li^#¹, Lei Deng^#¹

Affiliations

¹ Department of Radiology, the Second Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China.
² GE Healthcare, Beijing, China.

^# Contributed equally.

Abstract

Background: To investigate the effect of a new deep learning image reconstruction (DLIR) algorithm on the detection, characterization and image quality of pulmonary nodules (PNs) in ultra-low dose chest computed tomography (CT) in comparison with the adaptive statistical iterative reconstruction (ASIR-V) algorithm.

Methods: Nine artificial pulmonary nodules [six ground glass nodules (GGNs) and three solid nodules (SNs); density: -800 HU, -630 HU, 100 HU; diameter: 12 mm, 10 mm, 8 mm] were randomly placed in a thorax anthropomorphic phantom (Lungman, Kyoto Kagaku Inc.) and scanned on a 256-row CT (Revolution CT, GE Healthcare). Eight scans were performed at 70 kVp with different tube currents (20, 30, 50, 70, 90, 100, 120, 150 mA). Raw data were reconstructed using the filtered back projection (FBP), ASIR-V (30%, 50%, 80%) and DLIR (Low, Medium, High; TrueFidelity™) at 0.625 mm thickness. The effective radiation dose was recorded. All images were automatically analyzed using a commercially available artificial intelligence software (Intelligent 4D Imaging System for Chest CT 5.5, YITU Healthcare) and CT value, standard deviation (SD), long and short diameters of each nodule and SD of air (background) were measured. The detection rate, deformation degree (long diameter/short diameter), signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of pulmonary nodules were calculated.

Results: Nodule CT values were the same in all mA settings for all three types of reconstruction algorithms (all P>0.05). DLIR groups had significantly lower SD and higher SNR and CNR values, with better overall image quality than ASIR-V and FBP groups at each mA, ranging from 65-85% reduction in SD, 67-83% increase in SNR with DLIR-H over 50%ASIR-V and 75-91% reduction in SD and 77-89% increase in SNR with DLIR-H over FBP (all P<0.05). At ultra-low dose conditions (30 mA), the DLIR-H images had the highest detection rate of PNs (100%). In addition, the DLIR-M had a minimal negative effect on the characterization of PNs.

Conclusions: DLIR algorithm can be a potential reconstruction technique to optimize image quality and improve detection rate of PNs in ultra-low dose lung screening.

Keywords: Computed tomography (CT); deep learning; image reconstruction; pulmonary nodule (PN).