Application value of deep learning reconstruction algorithm in CT imaging of abdominal phantoms
10.3760/cma.j.cn112271-20221226-00498
- VernacularTitle:深度学习重建算法在腹部CT成像中应用的体模研究
- Author:
Yurong ZHANG
1
;
Qianqian YUAN
;
Jianbo GAO
;
Jie LIU
Author Information
1. 郑州大学第一附属医院放射科，郑州 450052
- Keywords:
Radiation dosage;
Tomography, X-Ray Computer;
Abdomen;
Phantom
- From:
Chinese Journal of Radiological Medicine and Protection
2023;43(8):645-652
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the value of the deep learning image reconstruction (DLIR) algorithm in improving the CT image quality of abdominal phantoms under different radiation doses by comparing the DLIR algorithm with the conventional Adaptive Statistical Iterative Reconstruction-V (ASIR-V) technique.Methods:Two groups with tube voltages of 100 kV and 120 kV (also referred to as the 100 kV and 120 kV groups, respectively) were involved. Each group was further divided into six subgroups based on different volumetric CT dose indices (CTDI vol: 2, 4, 6, 8, 10 and 15 mGy). Subsequently, CT images based on the filtered back projection (FBP) algorithm were obtained and were then reconstructed using the ASIR-V algorithm with different weights (ASIR-V 50%, 80%, and 100%) and the DLIR algorithm with different levels (DLIR-L, M, and -H). As a result, 84 groups of images were obtained in total. Afterward, this study compared and analyzed the variations in CT values, noise, signal-to-noise ratios (SNRs), contrast-to-noise ratios (CNRs), and subjective scores of various parts in various CTDI vol subgroups under different reconstruction conditions. In addition, the subjective scores of the image quality were compared using the Kruskal-Wallis H test, while objective indices and radiation doses were compared through the univariate analysis of variance (ANOVA) and the paired t test. Results:Under the same tube voltage, there were statistically significant differences in the noise, SNRs, and CNRs of various parts in various CTDI vol subgroups under different reconstruction conditions ( F = 415.39, 315.30, P < 0.001), while there was no statistically significant difference in the noise, SNRs, and CNRs of images constructed using ASIR-V 50% and DLIR-L ( P > 0.05). Under different tube voltages, the subjective scores of both groups show statistically significant differences (100 kV group: H = 13.47, P = 0.036; 120 kV group: H = 12.99, P = 0.043). Moreover, two physicians offered consistent subjective scores, with Kappa values > 0.70. Among these images, DLIR-H images showed the highest subjective scores, followed by DLIR-M and ASIR-V 50% images, which had roughly consistent subjective scores. Moreover, the subjective scores of the 100 kV group were slightly higher than those of the 120 kV group. With the ASIR-V 50% images of the subgroup with a CTDI vol of 15 mGy as references, the DLIR-L, -M, and -H reduced radiation doses by more than 30%, 70% and 85%, respectively on the premise that diagnostic requirements were met. Conclusions:The DLIR algorithm can not only significantly reduce the image noise and improve the image quality, but also effectively decrease the radiation doses on the premise of meeting the diagnostic requirements. It is recommended that 100 kV tube voltage combined with a medium- or high-level DLIR algorithm should be applied to low-dose abdominal CT scans in clinical applications.
