Application of artificial intelligence in vascular reconstruction based on cerebral CT perfusion data
10.3760/cma.j.cn112149-20200914-01087
- VernacularTitle:人工智能在基于颅脑CT灌注数据血管后处理的应用
- Author:
Xiaoying HUANG
1
;
Yunfeng BAO
;
Xiamin LI
;
Fangkai GUO
;
Zhifei LI
;
Chunhui SHAN
;
Yingmin CHEN
Author Information
1. 河北省人民医院医学影像科，石家庄 050051
- Keywords:
Tomography, X-ray computed;
Artificial intelligence;
Cerebral artery
- From:
Chinese Journal of Radiology
2021;55(8):817-822
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the application value of artificial intelligence (AI) in image post-processing of reconstructed CTA based on CT cerebral perfusion (CTP).Methods:Clinical and radiological data of 100 patients suspected of cerebrovascular diseases in Hebei General Hospital from January to July 2020 were retrospectively selected. All patients were divided into A and B group on average according to the different examination schemes. Cerebral CTP examination was performed in group A (the temporal maximum intensity projective data set generated by the first 5 time phases in the maximum period of the difference between arteriovenous CT values selected as subgroup A1, and the corresponding original thin-layer images selected as subgroup A2), single phase CTA examination was performed in group B, manual and AI image post-processing were performed respectively. Subjective scoring of the image data was performed, and the objective bid evaluation indexes such as CT value, noise (SD), signal-to-noise ratio (SNR), contrast to noise ratio (CNR) were measured, the qualified rate of artificial and AI vascular segmentation was counted, and post-processing time were recorded. The objective evaluation indexes were compared between three groups using one-way ANOVA, and the Kruskal-Wallis H test was used to compare the difference of subjective scores.Results:Statistically significant differences were observed in subjective score and objective evaluation index of original images among group A1, group A2 and group B (all P<0.05). Among them, arterial enhancement, arteriolar detail display score, cerebral artery CT value, SNR and CNR in group A1 were higher than those in group A2 and group B (all P<0.05). In a total of 100 patients with 1 100 blood vessels, the qualified rates of AI vascular segmentation in group A1 [98.4% (541/550)] and group B [98.7% (543/550)] were higher than those of manual [82.9% (456/550), 87.1% (479/550), χ2=77.392, 56.521, P<0.001], but the qualified rate of AI vascular segmentation of group A2 [78.4% (431/550)] was lower than that of manual [85.6% (471/550), χ2=9.855, P=0.002]. The completion time of AI post-processing were reduced by 56.30%, 49.63%, 50.81%, respectively than those with manual. Conclusion:Compared with manual image post-processing, AI has certain advantages in image quality and work efficiency of reconstructed CTA post-processing based on CTP de-noising dataset, and it is worth popularizing and applying in the image post-processing of cerebrovascular disease, combined with artificial quality control.
