Scale Invariant Feature Transformation Algorithm-Based Magnetic Resonance Imaging-Assisted Diagnosis of Patients with Stroke and Rehabilitation Nursing

'e aim of this paper was to explore the effects of magnetic resonance imaging (MRI) diagnosis in the treatment and rehabilitation of stroke patients. 80 stroke patients admitted to hospital were selected for the experiment and divided randomly into a control group (group A) and an experimental group (group B), with 40 patients in each group. Patients in group B received MRI examination and rehabilitation training every month under their disease progresses, while patients in group A received routine rehabilitation training instead of MRI examination. 'e results showed that various indicators of patients, including Fugl-Meyer Assessment (FMA), Berg Balance Scale (BBS), Timed “Up&Go” test (TUG), 6-minute walking test (6MWT), modified Barthel index (MBI), and stroke-specific quality of life scale (SS-QOL), in group B were better than those in group A. It indicated that personalized rehabilitation training under MRI examination had a better effect on recovery of patients. Besides, the MRI images optimized by scale invariant feature transformation (SIFT) algorithm had better resolution, which were helpful for doctors’ diagnosis of disease. MBI scale and SS-QOL showed that the self-care ability and quality of life of patients in group B were better than those of group A. 'erefore, the above results indicated that MRI diagnosis could accurately judge disease progress of patients to draw up personalized rehabilitation training plan, which had positive effects on promoting treatment and prognosis of stroke patients.