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

Jie Dong,Yun Meng,Suxiao Li,Yong Zhang,Shujun Zhao

doi:10.1371/journal.pone.0251529

Abstract

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.

Highlights

With the improvement of people’s living standards in recent years, the incidence of cerebral infarction has increased extremely, accounting for about 80% of all cerebrovascular diseases
3.1 Quality analysis on image of the reconstruction algorithm based on complex convolutional neural network (CCNN)
It illustrated that the D-image domain reconstruction network (IDRN) and D-frequency domain reconstruction network (FDRN) images after fusion of FDRN and IDRN showed higher peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) value than those before the integration

Summary

Introduction

With the improvement of people’s living standards in recent years, the incidence of cerebral infarction has increased extremely, accounting for about 80% of all cerebrovascular diseases. At present, imaging technology is mainly used to diagnose and treat patients with cerebral infarction. Compared with X-ray and computed tomography (CT) scanning technology, MRI has clearer scan images and relatively low ionizing radiation, so it is often used in the diagnosis and treatment of patients with cerebral infarction [2]. MRI scan imaging takes a long time, so that the subject has to maintain the same posture for a long time during the detection process, which leads to the involuntary movement of the subject, and eventually causes motion artifacts, so as to bring greater impacts on the diagnosis and treatment of diseases [3]. Shortening the imaging time is a hotspot and focus in the field of MRI

Methods

Results

Discussion

Conclusion