Application of fast susceptibility weighted imaging based on deep learning in assessment of acute ischemic stroke / 中华放射学杂志

ABSTRACT

Objective:To explore the value of fast susceptibility weighted imaging (SWI) generated by a deep learning model in assessment of acute ischemic stroke (AIS).Methods:From January 2019 to January 2021, 118 AIS patients [75 males and 43 females, aged 23-100 (66±14) years] who underwent MR examination and SWI sequence scanning within 24 h of symptom onset in the First Medical Center of PLA General Hospital were retrospectively analyzed. MATLAB ′s randperm function was used to divide 118 patients into a training set of 96 cases and a test set of 22 cases at a ratio of 8∶2. Fourty-seven AIS patients [38 males and 9 females, aged 16-75 (58±12) years] from one center of a multicenter study were selected to build the external validation set. SWI image and filtered phase image were combined into complex value image as full sampling reference image. Undersampled SWI images were obtained by retrospective undersampling of reference fully sampled images, and the undersampling multiple was five times which could save 80% of the scanning time, then the complex-valued convolutional neural network (ComplexNet) was used to develop reconstruct fast SWI. Interclass correlation coefficient (ICC) or Kappa tests were used to compare the consistency of image quality and the diagnostic consistency for the presence of susceptibility vessel sign (SVS), cerebral microbleeds and asymmetry of cerebral deep medullary veins (DMVs) in AIS patient on fully sampled SWI and fast SWI based on ComplexNet.Results:In test set, score of image quality was 4.5±0.6 for fully sampled SWI image and 4.6±0.7 for fast SWI based on ComplexNet, and coefficient was excellent (ICC=0.86, P<0.05). Full sampling SWI had good agreement with fast SWI based on ComplexNet in detecting SVS (Kappa=0.79, P<0.05), microbleeds (Kappa=0.86, P<0.05), and DMVs asymmetry (Kappa=0.82, P<0.05) in AIS patients. In the external validation set, score of image quality was 4.1±1.0 for fully sampled SWI image and 4.0±0.9 for fast SWI based on ComplexNet, and coefficient was excellent (ICC=0.97, P<0.05). Full sampling SWI had good agreement with fast SWI based on ComplexNet in detecting SVS (Kappa=0.74, P<0.05), microbleeds (Kappa=0.83, P<0.05), and DMVs asymmetry (Kappa=0.74, P<0.05) in AIS patients. Conclusions:Deep learning techniques can significantly accelerate the speed of SWI, and the consistency of image quality and detected AIS signs between fast SWI based on ComplexNet and fully sampled SWI is good. The fast SWI based on ComplexNet can be applied to the radiographic assessment of clinical AIS patients