The application value of deep learning reconstruction algorithm in improving quality of low dose pancreatic CT images
10.3760/cma.j.cn112149-20210413-00365
- VernacularTitle:深度学习算法在胰腺低剂量CT扫描中改善图像质量的应用价值
- Author:
Qiaoling WU
1
;
Yun WANG
;
Xiheng WANG
;
Zhuangfei MA
;
Huadan XUE
;
Zhengyu JIN
Author Information
1. 中国医学科学院 北京协和医学院 北京协和医院放射科，北京 100730
- Keywords:
Pancreas;
Tomography, X-ray computed;
Deep learning reconstruction algorithm;
Radiation dosage
- From:
Chinese Journal of Radiology
2022;56(4):437-442
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore application value of improving quality of the low dose pancreatic CT images by using deep learning reconstruction (DLR).Methods:From August to December 2020, 68 patients who underwent contrast-enhanced pancreatic CT were prospectively collected in Peking Union Medical College Hospital. All patients were randomly divided into routine dose group (34 patients, with tube voltage of 120 kV) and low dose group (34 patients, with tube voltage of 100 kV). All patients underwent non-contrast, arterial phase, parenchymal phase and delay phase scans. The four-phase images of low dose group were reconstructed by using filtered back projection (FBP), hybrid iterative reconstruction (AIDR) and DLR which were marked with LD-FBP, LD-AIDR and LD-DLR, respectively. The four-phase images of routine dose group were reconstructed by using AIDR algorithm which were marked with RD-AIDR. The CT value, image noise (SD), signal to noise ratio (SNR) and contrast to noise ratio (CNR) of pancreas were measured. The ANOVA test was performed in comparison with objective parameters of different reconstruction algorithms, and LSD test was performed in pairwise comparison. The subjective image scores were obtained and were compared using Kruskal-Wallis test.Results:CT value, SD, SNR and CNR of non-contrast, arterial phase, parenchymal phase and delay phase had significant difference among different reconstruction images of routine dose group and low dose group (all P<0.05). The CT value of LD-FBP, LD-AIDR, and LD-DLR images were significantly higher than those of RD-AIDR images in parenchymal phase and delay phase (all P<0.05). There were statistically significant differences in each pairwise comparison of SD and SNR of four phase images (all P<0.05). There were statistically significant differences of CNR among LD-FBP, LD-DLR and RD-AIDR in four phase images (all P<0.05). The CNR of RD-AIDR was better than that of LD-FBP, and CNR of LD-DLR was better than that of RD-AIDR. DLR algorithm improved the SD, SNR and CNR of four phases of pancreatic images. The improvement of SNR was more significant after contrast enhancement, and the improvement of CNR was more significant in the non-contrast and delay phases. Subjective image scores of different reconstruction images were statistically different in four phase images (all P<0.001). Overall image scores of LD-DLR and RD-AIDR had no significant differences in four phase ( Z value of four phases were 1.00, 2.24, 0.45 and 1.34, respectively; P value of four phases were 0.317, 0.025, 0.655 and 0.180, respectively). Conclusion:The DLR technology can decrease radiation dose of pancreatic CT, improve image quality and satisfy diagnostic requirement. The DLR technology can also reduce image noise, improve the SNR and CNR in low dose contrast-enhanced pancreatic CT.
