Study on Memory Function of Stroke Patients under Exercise Relearning Based on DWI Image Analysis Based on Optimized Registration Algorithm

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Abstract

Objective. This paper uses an optimized registration algorithm to analyze the diffusion-weighted imaging (DWI) scan parameters of acute ischemic stroke (AIS) in the memory function of stroke patients under exercise relearning (MRP). Methods. This study used a random case-control study. 65 patients with stroke and hemiplegia were randomly divided into a control group: conventional rehabilitation intervention (32 cases), and a treatment group: MRP (33 cases). Each patient uses 4 parameters for DWI examination and obtains 4 sets of images, group 1 is the control sequence, group 2 uses parallel acquisition technology, group 3 uses parallel acquisition technology and reduces the number of excitations, group 4 uses parallel acquisition technology to reduce repetition time (TR) and echo time (TE) and enlarge the field of view, and the scan time of each group is 177, 81, 23, and 18 s in sequence. At the time of enrollment and after 12 weeks of treatment, patients in each group were evaluated with Fugl-Meyer motor function score (FMA) and modified Pap index (MBI) for hand and wrist motor function and ADL. Results. After treatment, the FMA and MBI values of the treatment group were significantly higher than those of the control group (P<0.05). Conclusion. By adopting a parallel acquisition technique and reducing the number of excitations (group 3) scanning scheme, not only the scanning time is significantly shortened, but also the image quality can meet the diagnostic requirements, which has great application value for AIS patients who need emergency treatment. MRP can obviously promote the hand and wrist motor function and daily living ability of stroke hemiplegic patients.

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Zhao, H., Li, B., Zhang, X., Guo, X., & Qian, L. (2021). Study on Memory Function of Stroke Patients under Exercise Relearning Based on DWI Image Analysis Based on Optimized Registration Algorithm. Scientific Programming, 2021. https://doi.org/10.1155/2021/1536515

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