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Chinese Journal of Endourology(Electronic Edition) ›› 2022, Vol. 16 ›› Issue (06): 539-545. doi: 10.3877/cma.j.issn.1674-3253.2022.06.011

• Clinical Research • Previous Articles     Next Articles

Preliminary application of deep learning image reconstruction double-low technique in CT urography

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

  1. 1. Department of Radiology, the Third Affiliated Hospital, Sun Yat-sen University, Guangzhou 510630, China
  • Received:2022-05-19 Online:2022-12-01 Published:2022-11-25
  • Contact: Zhan'ao Meng

Abstract:

Objective

To explore the feasibility of 100 kV deep learning image reconstruction (DLIR) in CT urography (CTU) and compare them with the traditional 120kV adaptive statistical iterative reconstruction-V (ASIR-V) standard scheme.

Methods

According to the scanning scheme, 60 patients with abdominal CTU enhancement scan were divided into standard plan group (group S) and double low plan group (group L). The contrast dose, volume CT dose index and dose length product of group L and group S were recorded, and the effective dose was calculated. Group S was reconstructed with 60%ASIR-V (S-AV60), and group L was reconstructed with 60%ASIR-V (L-AV60), DLIR-M and DLIR-H. The CT values and standard deviation (SD) of right renal pelvis, right renal parenchyma, left renal pelvis, left renal parenchyma, right ureter, left ureter, bladder and psoas major muscle were measured, and the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. The original axial images, reconstructed volume rendering (VR) images and maximum intensity projection (MIP) images were subjectively scored by two experienced clinicians. t-test was used to compare ED between the two groups. One-way ANOVA was used to analyze the objective evaluation parameters such as CT, SD, SNR and CNR. Kruskal-Wallis test was used to analyze the subjective score of the two radiologists. Kappa test was used to analyze the consistency of subjective scores of the two radiologists.

Results

Compared with group S, the effective dose and contrast dose of group L decreased by 29.1% (P<0.001) and 32.1% (P<0.001), respectively. The SD of DLIR-H group was the lowest and the SNR,CNR was the largest. The independent score and the final image score of the four factors were the highest in the DLIR-H group (P<0.001). The subjective image quality scores of 4 CTU were also consistent among 2 radiologists (Kappa=0.770, P<0.001).

Conclusions

In CT urography (CTU), the double-low technique of DLIR reconstruction can significantly reduce the radiation dose (29.1%) and contrast dose (32.1%). Compared with the 60%ASIR-V standard scheme, DLIR-H can further improve the image quality and is a better reconstruction algorithm.

Key words: Deep learning, Image reconstruction, CT urography, Artificial intelligence(AI)

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