The Second Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang 150001, China.
Enrollment and Employment Division, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang 150040, China.
Comput Math Methods Med. 2022 Apr 27;2022:5435207. doi: 10.1155/2022/5435207. eCollection 2022.
It is important to study the evaluation algorithm for the stroke rehabilitation treatment effect to make accurate evaluation and optimize the stroke disease treatment plan according to the evaluation results. To address the problems of poor restoration effect of positron emission tomography (PET) image and recognition restoration effect of evaluation data and so on. In the paper, we propose a stroke rehabilitation treatment effect evaluation algorithm based on cross-modal deep learning. Magnetic resonance images (MRI) and PET of stroke patients were collected as evaluation data to construct a multimodal evaluation dataset, and the data were divided into positive samples and negative samples. According to the mapping relationship between MRI and PET, three-dimensional cyclic adversarial is used to generate the neural network model to recover the missing PET data. Using the cross-modal depth learning network model, the RGB image, depth image, gray image, and normal images of MRI and PET are taken as the feature images and the multifeature fusion method is used to fuse the feature images, output the recognition results of MRI and PET, and evaluate the effect of stroke rehabilitation treatment according to the recognition results. The results show that the proposed algorithm can accurately restore PET images, the evaluation data recognition effect is good, and the evaluation data recognition accuracy is higher than 95%. The evaluation accuracy of stroke rehabilitation treatment effect is high, the evaluation time varies between 0.56 s and 0.91 s, and the practical application effect is good.
研究脑卒中康复治疗效果的评价算法，对于根据评价结果进行准确评价和优化脑卒中疾病治疗方案具有重要意义。针对正电子发射断层扫描（PET）图像恢复效果差、评价数据识别恢复效果差等问题。文中提出了一种基于跨模态深度学习的脑卒中康复治疗效果评价算法。采集脑卒中患者的磁共振成像（MRI）和正电子发射断层扫描（PET）作为评价数据，构建多模态评价数据集，并将数据分为正样本和负样本。根据 MRI 和 PET 的映射关系，利用三维循环对抗生成神经网络模型来恢复缺失的 PET 数据。利用跨模态深度学习网络模型，将 MRI 和 PET 的 RGB 图像、深度图像、灰度图像和法线图像作为特征图像，采用多特征融合方法对特征图像进行融合，输出 MRI 和 PET 的识别结果，并根据识别结果评价脑卒中康复治疗效果。结果表明，所提算法能够准确地恢复 PET 图像，评价数据识别效果良好，评价数据识别准确率高于 95%。脑卒中康复治疗效果评价准确性高，评价时间在 0.56~0.91 s 之间，实际应用效果良好。
