[Improving the image quality and quantitative accuracy of arterial spin labeling based on generative adversarial...

Objective: To investigate the use of deep learning approaches to improve the image quality of arterial spin labeling (ASL) and optimize its quantitative accuracy for cerebral blood flow (CBF). Methods: The clinical and imaging data of 101 patients with cerebrovascular disease in Tianjin Huanhu Hospital from May 2018 to August 2019 were retrospectively collected. Patients were divided into a training set (71 cases) and a validation set (30 cases). In training set, there were 53 cases of male,18 cases of female,with age of 55.0 (41.3, 64.5), and in validation set, there were 23 cases of male,7 cases of female,with age of57.5 (49.0, 65.0). Quantitative perfusion weighted imaging was used as the reference standard, and the original ASL-CBF images were reconstructed by training a deep learning generative adversarial network (GAN). The image quality of original ASL-CBF and GAN-CBF was compared by the structural similarity index and the normalized root mean square error. Pearson correlation coefficient was used to analyze the correlation between ASL-CBF, GAN-CBF and quantitative perfusion in different cerebral vascular blood supply areas and stroke areas, and to verify the improvement effect of GAN on the image quality and quantitative accuracy of ASL. Results: There were significant differences in gender, age, disease type, site and size between training set and validation set (all P>0.05). Compared with ASL-CBF, GAN-CBF had a higher structural similarity index (0.888 vs 0.801, P<0.001), and a lower normalized root mean square error (0.628 vs 0.775, P<0.001). The correlation between GAN-CBF and quantitative perfusion in different blood vessel supply areas and stroke lesion areas was improved compared with the original ASL-CBF, in which the perforating branch of the middle artery (r=0.853) and stroke lesion areas (r=0.765) were the most obvious (all P<0.001). Conclusion: The generative adversarial network could improve the image quality and quantization accuracy of ASL without increasing the scanning time, and expand the clinical application value of ASL.