Magnetic resonance imaging reconstruction algorithm under complex convolutional neural network in diagnosis and prognosis of cerebral infarction

• Published in: PloS one Vol. 16; no. 5; p. e0251529
• Main Authors: Dong, Jie, Zhao, Shujun, Meng, Yun, Zhang, Yong, Li, Suxiao
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 17.05.2021; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Artificial neural networks; Biology and Life Sciences; Cerebral infarction; Computer programs; Data analysis; Data collection; Data encryption; Decision analysis; Diagnosis; Drafting software; Editing; Electronic mail; Evaluation; Funding; Image processing; Image reconstruction; Infarction; Magnetic resonance; Magnetic resonance imaging; Medical diagnosis; Medical imaging; Medical prognosis; Medical treatment; Medicine and Health Sciences; Methodology; Methods; Microelectronics; Movement; Neural networks; Physics; Posture; Prognosis; Research and Analysis Methods; Resonance; Reviews; Software; Stroke; Stroke (Disease); Visualization; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0251529

This study was to explore the application value of magnetic resonance imaging (MRI) image reconstruction model based on complex convolutional neural network (CCNN) in the diagnosis and prognosis of cerebral infarction. Two image reconstruction methods, frequency domain reconstruction network (FDRN) and image domain reconstruction network (IDRN), were introduced based on the CCNN algorithm. In addition, they were integrated to form two new MRI image reconstruction models, namely D-FDRN and D-IDRN. The peak signal to noise ratio (PSNR) value and structural similarity index measure (SSIM) value of the image were compared and analyzed before and after the integration. The MRI images of patients with cerebral infarction in the dataset were undertaken as the data source, the average diffusion coefficient (DCavg) and apparent diffusion coefficient (ADC) values of different parts of the MRI image were measured, respectively. The correlation of the vein abnormality grading (VABG) to the infarct size and the degree of stenosis of the responsible vessel was analyzed in this study. The results showed that the PSNR and SSIM values of the MRI reconstructed image of the D-IDRN algorithm based on the CCNN algorithm in this study were higher than those of other algorithms. There was a positive correlation between the VABG and the infarct size (r = 0.48 and P = 0.002), and there was a positive correlation between the VABG the degree of stenosis of the responsible vessel (r = 0.58 and P < 0.0001). The ADC value of the central area of the infarct on the affected side was significantly greatly lower than that of the normal side ( P < 0.01), and the DCavg value of the central area of the infarct was much lower in contrast to the normal side ( P < 0.05). It indicated that an image reconstruction algorithm constructed in this study could improve the quality of MRI images. The ADC value and DCavg value changed in the infarct central area could be used as the basis for the diagnosis of cerebral infarction. If the vein was abnormal, the patient suffered from severe vascular stenosis, large infarction area, and poorer prognosis.