Image Features of Magnetic Resonance Angiography under Deep Learning in Exploring the Effect of Comprehensive Rehabilitation Nursing on the Neurological Function Recovery of Patients with Acute Stroke

Abstract

This study was to explore the effects of imaging characteristics of magnetic resonance angiography (MRA) based on deep learning on the comprehensive rehabilitation nursing on the neurological recovery of patients with acute stroke. In this study, 84 patients with acute stroke who were treated in hospital were selected as the research objects, and they were rolled into a control group (routine care) and an experimental group (comprehensive rehabilitation care). The dense dilated block-convolution neural network (DD-CNN) algorithm under deep learning for cerebrovascular was adopted to assess the effect of comprehensive rehabilitation care on the neurological recovery of patients with acute stroke. The results showed that the Berg scale scores, Fugl-Meyer scores, and Functional Independence Measure (FIM) scores of the experimental group of patients after 6 weeks and 12 weeks of comprehensive rehabilitation nursing were greatly different from those before treatment, showing statistical differences (P < 0.05). Compared with conventional magnetic resonance imaging (MRI) images, MRA images based on CNN algorithm, Dense Net algorithm, and DD-CNN algorithm can more clearly show the patient's cerebral artery occlusion. The average dice similarity coefficient (DSC) values of CNN algorithm, Dense Net algorithm, and DD-CNN algorithm were determined to be 84.3%, 95.7%, and 97.8%, respectively; the average sensitivity (Sen) values of the three algorithms were 76.1%, 95.4%, and 96.8%, respectively; and the average accuracy (Acc) values were 87.9%, 96.3%, and 97.9%, respectively. Thus, there were statistically obvious differences among the three algorithms in terms of average values of DSC, Sen, and Acc (P < 0.05). The MRA images processed by the DD-CNN algorithm showed that the degree of neurological recovery of the experimental group was observably greater than that of the control group, and the difference was statistically obvious (P < 0.05). In short, the image features of MRA based on the deep learning DD-CNN algorithm showed good application value in studying the effect of comprehensive rehabilitation nursing on the neurological recovery of patients with acute stroke, and it was worthy of promotion.

Figure 1

The overall frame diagram of device-independent FMM-MRF.

Figure 2

The processing flowchart of DD-CNN.

Figure 3

Comparison of the average age and the course of disease between the two groups of patients.

Figure 4

Comparison on nature of the disease for patients in the two groups.

Figure 5

Effect evaluation of comprehensive rehabilitation nursing for two groups of patients. ^∗The difference was statistically obvious in contrast to the value before treatment (P < 0.05).

Figure 6

MRA image of a 55-year-old male patient. (a) the original MRA image; (b) the MRA image processed by the CNN algorithm; (c) the MRA image processed by the dense net algorithm; and (d) the MRA image processed by the DD-CNN algorithm.

Figure 7

Quantitative evaluation results of MRA images of different algorithms. ^∗The difference was statistically obvious in contrast to the value before treatment (P < 0.05).

Figure 8

Analysis of MRA images of patients with acute stroke treated with comprehensive rehabilitation nursing to evaluation of the neurological function recovery. ^∗The difference was statistically obvious in contrast to the value before treatment (P < 0.05).