School of Physics and Microelectronics, Zhengzhou University, Zhengzhou, P.R. China.
Department of Magnetic Resonance, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, P.R. China.
PLoS One. 2021 May 17;16(5):e0251529. doi: 10.1371/journal.pone.0251529. eCollection 2021.
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.
本研究旨在探索基于复杂卷积神经网络（CCNN）的磁共振成像（MRI）图像重建模型在脑梗死诊断和预后中的应用价值。基于 CCNN 算法引入了两种图像重建方法，即频域重建网络（FDRN）和图像域重建网络（IDRN）。此外，它们被集成在一起，形成了两种新的 MRI 图像重建模型，即 D-FDRN 和 D-IDRN。对集成前后的图像进行峰值信噪比（PSNR）值和结构相似性指数测量值（SSIM）值的比较和分析。以数据集内的脑梗死患者的 MRI 图像为数据源，分别测量 MRI 图像不同部位的平均扩散系数（DCavg）和表观扩散系数（ADC）值。分析静脉异常分级（VABG）与梗死灶大小及责任血管狭窄程度的相关性。结果表明，基于 CCNN 算法的 D-IDRN 算法对 MRI 重建图像的 PSNR 和 SSIM 值均高于其他算法。VABG 与梗死灶大小呈正相关（r = 0.48，P = 0.002），与责任血管狭窄程度呈正相关（r = 0.58，P < 0.0001）。患侧梗死中心区 ADC 值明显低于健侧（P < 0.01），梗死中心区 DCavg 值明显低于健侧（P < 0.05）。这表明本研究构建的图像重建算法可以提高 MRI 图像的质量。梗死中心区 ADC 值和 DCavg 值的变化可作为脑梗死诊断的依据。如果静脉异常，患者存在严重血管狭窄、大面积梗死、预后较差。
