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.
ABSTRACT
Objective To compare the image quality produced by MR high resolution vessel wall imaging (HR?VWI) and ultrasound (US) in evaluating carotid plaque load. Methods This prospective study enrolled 21 patients with carotid plaques undergoing HR?VWI and subsequent 2D US between August 2016 to January 2017 in Hebei General Hospitial. The plaque thickness (PT), lumen area (LA), wall area (WA) and total vessel area (TVA) of the plaques were measured and normalized wall index (NWI) was calculated on both HR?VWI images and US for those plaques with image quality score≥3 and matching between the two methods. The plaque load index was compared by using the independent sample t test or the non?parametric Wilcoxon test, and the correlation between the indexes was based on the Pearson test. Results Forty?five carotid plaques were matched with HR?VWI and US. There was no significant difference in PT, LA, WA, TVA and NWI detected by HR?VWI and ultrasound (P>0.05). The parameters measured by two methods were correlated (r values were 0.83, 0.85, 0.32, 0.83 and 0.59, P<0.05). Conclusion There is a good consistency between HR?VWI and conventional ultrasound in the measurement of carotid plaque load.