Magnetic Resonance Image Feature Analysis under Deep Learning in Diagnosis of Neurological Rehabilitation in Patients with Cerebrovascular Diseases.

Zhao Rong,Chunyan Yang,Jun Hao,Hufei Chang,Wenjun Ji,Xue Li

doi:10.1155/2021/6051009

Abstract

To explore the impact of magnetic resonance imaging (MRI) image features based on deep learning algorithms on the neurological rehabilitation of patients with cerebrovascular diseases, eighty patients with acute cerebrovascular disease were selected as the research objects. According to whether the patients were with vascular cognitive impairment (VCI), they were divided into VCI group (34 cases) and non-VCI group (46 cases). In addition, based on the convolutional neural network (CNN), a new multimodal CNN image segmentation algorithm was proposed. The algorithm was applied to the segmentation of MRI images of patients with vascular cognitive impairment (VCI) and compared with the segmentation results of CNN and fully CNN (FCN). As a result, the segmentation results of the three different algorithms showed that the Dice coefficient, accuracy, and recall of the multimodal CNN algorithm were 0.78 ± 0.24, 0.81 ± 0.28, and 0.88 ± 0.32, respectively, which were significantly increased compared to those of other two algorithms (P < 0.05). The neurological evaluation results showed that the MMSE and MoCA scores of VCI patients were 15.4 and 14.6 ± 5.31, respectively, which were significantly lower than those of the non-VCI group (P < 0.05). The TMT-a and TMT-b scores of VCI patients were 221.7 and 385.9, respectively, which were significantly higher than those of the non-VCI group (P < 0.05). The FA and MD values of nerve function-related fibers shown in the MRI images of the VCI group were significantly different from those of the non-VCI group (P < 0.05). Therefore, the neurological recovery process of VCI patients was affected by multiple neurocognitive-related fiber structures. To sum up, the multimodal CNN algorithm can sensitively and accurately reflect the degree of neurological impairment in patients with cerebrovascular disease and can be applied to disease diagnosis and neurological evaluation of VCI patients.

Highlights

Cerebrovascular disease (VCD) is a common disease in the neurology department
Erefore, a new multimodal convolutional neural network (CNN) image segmentation algorithm based on CNN was proposed. e algorithm was applied to the segmentation of magnetic resonance imaging (MRI) images of vascular cognitive impairment (VCI) patients and was compared with the segmentation results of CNN and fully CNN (FCN), to explore the effect of the algorithm on the neurological recovery of patients with different types of cerebrovascular diseases
Montreal cognitive assessment (MoCA) Score Results of the Two Groups of Patients. e MoCA scores of the two groups of cerebrovascular disease patients are shown in Figure 6. e results of the cognitive function scores of the VCI group and the non-VCI group showed that the MoCA scores of the VCI patients were 14.6 ± 5.31

Summary

Introduction

Cerebrovascular disease (VCD) is a common disease in the neurology department. Diagnosis and treatment after the onset of VCD is very important for the prevention of disability or death, which can improve or alleviate the pain of patients caused by the onset [1]. Vascular cognitive impairment (VCI) is a complex condition that ranges from mild neurocognitive impairment to dementia. It is caused by obvious or not obvious cerebrovascular diseases caused by cerebral ischemia and hypoxia [2]. Erefore, if the optimal treatment time is missed, the recovery of neurological function will usually cause irreversible damage. Previous clinical trials found that some patients with VCD did not recover well after treatment, and VCI was common in the older population.

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