ID 原文 译文
56238 同时,患者高频段脑网络平均聚类系数增加,平均特征路径长度减少,小世界特性显著提升(在α,β,和γ均为p <0. 001). This indicated that the information integration increased after SCS. Also, compared with the pre-SCS state,the average clustering coefficient increased and the average path length decreased in the post-SCS state in highfrequency band (from α to γ). Also, the small-world network characteristics of the patients in the high frequencyband are significantly enhanced after SCS (p < 0. 001 in α, β, and γ).
56239 相对于健康对照组,这些指标变化的方向趋向于正常人静息态的脑功能. Compared with the healthy control group,the change directions of these indices tend to the brain function indices of resting states in normal subjects.
56240 样本熵和互样本熵在脊髓电刺激后与健康对照组在某些脑区内或脑区间上无显著差异(比如,β和γ频段的样本熵值在额叶和中央区(p> 0. 05)),但是高频段的脑网络参数仍有显著差异(比如,高频段小世界网络特征p <0. 001). There was no significant difference between pre-SCS in MCS patient state and the resting state in healthy controlsubjects in the sample entropy and cross sample entropy of some regions, such as the sample entropy of the β andγ frequency bands in the frontal and the central regions(p > 0. 05). However, the parameters of brain networksstill exist significant differences between the post-SCS and resting state.
56241 因此,我们认为这些变化是脊髓电刺激对大脑产生了一个"短时程效应". Therefore, we think that these indiceschanges after SCS may be induced from the “short-range effect” of SCS.
56242 我们推测脊髓电刺激对脑功能的重塑有一定的促进作用. We speculate that the SCS has theability to remodeling brain function through the “short-range effect”.
56243 本研究对脊髓电刺激的内在机理提供了新的解释,同时也为微意识状态患者的脑功能评估提供了新的思路 This study provided a new interpretationof the underlying mechanism of the SCS, and also provided a new perspective for assessing the brain function ofthe patients with MCS.
56244 高分辨率的磁共振图像可以提供细粒度的解剖信息,但是获取数据需要较长的扫描时间. Super-resolution (SR) MRI images can provide fine-grained anatomical information, however it takesa long time to acquire data.
56245 本文提出了一种基于自注意力机制生成对抗网络的超分辨率磁共振图像重构方法 (SA-SR-GAN),利用生成对抗网络从低分辨率磁共振图像生成高分辨率磁共振图像,将自注意力机制集成到超分辨率生成对抗网络框架中,用于计算输入特征的权重参数,同时引入了谱归一化处理,使判别器网络训练过程更加稳定. In order to accelerate the acquisition of MR images while maintaining high-qualityimages, extensive research has been performed on image reconstruction through the deep learning method. In thisstudy, a reconstruction framework by using self-attention based super-resolution generative adversarial networks(SA-SR-GAN) is proposed to generate super resolution MR image from low resolution MR image. Moreover, theself-attention mechanism is integrated into super-resolution generative adversarial networks (SR-GAN) framework,which is used to calculate the weight parameters of the input features.
56246 本文使用40组3D磁共振图像(每组图像包含256个切片)训练网络,并用10组图像进行测试. At the same time, spectral normalizationis added to make the discriminator network training process more stable. The network was trained with 40 3Dimages (each 3D image contains 256 slices) and tested with 10 images.
56247 实验结果表明,所提出的超分辨率自注意力生成对抗网络方法生成的超分辨率的磁共振图像的PSNR和SSIM值高于同类比较方法. The experimental results show that thePSNR and SSIM values of the super-resolution magnetic resonance image generated by the proposed SA-SR-GANmethod are higher than the state-of-the-art reconstruction methods.