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Chinese Journal of Hepatic Surgery(Electronic Edition) ›› 2022, Vol. 11 ›› Issue (05): 469-475. doi: 10.3877/cma.j.issn.2095-3232.2022.05.009

• Clinical Research • Previous Articles     Next Articles

Application of deep learning algorithm combined with "triple-low" technique in CT angiography of upper abdominal arteries

Ke Zhang1, Yue Zhang1, Wei Jiang1, Yuefei Guo1, Zhuoxin Guo1, Zhan'ao Meng1,()   

  1. 1. Department of Radiology, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China
  • Received:2022-05-11 Online:2022-10-10 Published:2022-10-13
  • Contact: Zhan'ao Meng

Abstract:

Objective

To evaluate the application value of deep learning image reconstruction (DLIR) combined with low radiation dose, low contrast agent dose and low contrast agent injection speed (triple-low technique) in the CT angiography (CTA) of upper abdominal arteries.

Methods

60 patients receiving CTA of the upper abdominal arteries in the Third Affiliated Hospital of Sun Yat-sen University from June to October 2021 were recruited in this study. The informed consents of all patients were obtained and the local ethical committee approval was received. Among them, 33 patients were male and 27 female, aged from 19 to 86 years, with a median age of 49 years. According to the scanning plan, all patients were divided into the standard plan group (group S, n=30) and triple-low plan group (group L, n=30). The effective dose (ED), contrast agent dose and contrast agent injection speed in two groups were recorded. 60%ASIR-V (S-AV60) image reconstruction was performed in group S, and 60%ASIR-V (L-AV60), 80%ASIR-V(L-AV80), DLIR-M (L-DM) and DLIR-H (L-DH) image reconstructions were conducted in group L. Parameters of objective evaluation of image quality included CT value, standard deviation (SD) value, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the upper abdominal artery. For the subjective image quality evaluation, the reconstructed images were scored with double-blind manner by two radiologists. ED and contrast agent dose were compared between two groups by t test. SD, SNR, CNR and other objective image quality evaluation parameters were compared by one-way ANOVA. Subjective image quality score was compared by Kruskal-Wallis test. The consistency of subjective score between two radiologists was assessed by Kappa test.

Results

In group L, ED was (5.1±1.3) mSv, significantly lower than (10.5±2.1) mSv in group S (t=-12.397, P<0.05). The contrast agent dose in group L was (65±11) ml, significantly lower than (100±21) ml in group S (t=-8.150, P<0.05). Compared with the injection speed of 5.0 ml/s in group S, the injection speed was3.5 ml/s in group L, which was decreased by 30%. For CTA image of the upper abdomen, the SD inL-DH group was the smallest, and the SNR and CNR were the largest (P<0.05). In L-DH group, the subjective image quality scores of five parameters including sharpness, image noise, image artifact, small branch display and clinical diagnosis were 4.7±0.5, 4.6±0.5, 4.8±0.4, 4.5±0.5 and 4.7±0.5, respectively. Among the five reconstructions, the subjective image quality score in the L-DH group was the highest (H=118.424, 114.258, 113.367, 121.463, 118.778; P<0.05). Fair consistency of 5 subjective image quality scores of CTA of upper abdominal arteries by 2 radiologists were observed (κ=0.672, P<0.05).

Conclusions

In the CTA of upper abdominal arteries, "triple-low" technique can significantly reduce the radiation dose, contrast agent dose and contrast agent injection speed. Compared with the recommended 60%ASIR-V standard plan, high-level DLIR combined with "triple-low" technique can further improve the image quality, which is a favorable reconstruction algorithm.

Key words: Deep learning image reconstruction, Adaptive statistical iterative reconstruction-V, Computed tomographic angiography

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