Optimized Deconvolutional Algorithm‐based CT Perfusion Imaging in Diagnosis of Acute Cerebral Infarction

Abstract

To apply deconvolution algorithm in computer tomography (CT) perfusion imaging of acute cerebral infarction (ACI), a convolutional neural network (CNN) algorithm was optimized first. RIU-Net was applied to segment CT image, and then equipped with SE module to enhance the feature extraction ability. Next, the BM3D algorithm, Dn CNN, and Cascaded CNN were compared for denoising effects. 80 patients with ACI were recruited and grouped for a retrospective analysis. The control group utilized the ordinary method, and the observation group utilized the algorithm proposed. The optimized model was utilized to extract the feature information of the patient’s CT images. The results showed that after the SE module pooling was added to the RIU-Net network, the utilization rate of the key features was raised. The specificity of patients in observation group was 98.7%, the accuracy was 93.7%, and the detected number was (1.6 ± 0.2). The specificity of patients in the control group was 93.2%, the accuracy was 87.6%, and the detected number was (1.3 ± 0.4). Obviously, the observation group was superior to the control group in all respects (P < 0.05). In conclusion, the optimized model demonstrates superb capabilities in image denoising and image segmentation. It can accurately extract the information to diagnose ACI, which is suggested clinically.

Current State of Evidence for Neuroimaging Paradigms in Management of Acute Ischemic Stroke

“…39,40 This information is processed to generate tissue maps with different perfusion characteristics including cerebral blood flow (CBF), CBV, mean transit time (MTT) and time to maximum intensity (Tmax) parameters. [41][42][43] Post-processing software use thresholds to detect and report quantitative estimates of ischemic core (considered non-salvageable brain tissue) and ischemic penumbra (considered salvageable brain tissue). The parameters and thresholds vary between software vendors and imaging modalities (CT vs MR).…”

“…Perfusion imaging is another set of imaging modalities with serial image acquisition to detect the distribution and delays of vascular flow in the brain tissue and can be obtained using IV contrast or arterial spin labelled water molecules 39,40 . This information is processed to generate tissue maps with different perfusion characteristics including cerebral blood flow (CBF), CBV, mean transit time (MTT) and time to maximum intensity (Tmax) parameters 41–43 . Post‐processing software use thresholds to detect and report quantitative estimates of ischemic core (considered non‐salvageable brain tissue) and ischemic penumbra (considered salvageable brain tissue).…”