白质纤维束分割方法通过识别连接不同脑区的白质通路,为脑连接分析提供了重要的神经通路参考信息。然而,传统的白质纤维束分割方法主要依赖于弥散磁共振图像(Diffusion magnetic resonance imaging,dMRI),由于获取弥散磁共振图像比较耗...白质纤维束分割方法通过识别连接不同脑区的白质通路,为脑连接分析提供了重要的神经通路参考信息。然而,传统的白质纤维束分割方法主要依赖于弥散磁共振图像(Diffusion magnetic resonance imaging,dMRI),由于获取弥散磁共振图像比较耗时,这极大地限制了其在临床中的应用。为解决此问题,提出了一种基于T1加权图像的白质纤维束分割方法,通过计算T1加权图像的结构张量来提示可能的纤维走向,进而提高白质纤维束的分割精度。此外,本文在模型训练期间引入弥散磁共振图像的特权信息来指导模型学习,从而提升白质束分割模型性能,具有挑战性的束分割效果提升明显,其中左穹窿(Left fornix,FX_left)的Dice得分提高了5%,右穹窿(Right fornix,FX_right)的Dice得分提高了6%。。本研究弥补了在缺少弥散磁共振图像的场景下无法进行神经通路分析的不足,扩展了神经通路分析的应用场景。展开更多
Both functional magnetic resonance imaging （fMRI） and diffusion tensor imaging （DTI） can provide different information of the human brain, so using the wavelet transform method can achieve a fusion of these two ty...Both functional magnetic resonance imaging （fMRI） and diffusion tensor imaging （DTI） can provide different information of the human brain, so using the wavelet transform method can achieve a fusion of these two types of image data and can effectively improve the depression recognition accuracy. Multi-resolution wavelet decomposition is used to transform each type of images to the frequency domain in order to obtain the frequency components of the images. To each subject, decomposition components of two images are then added up separately according to their frequencies. The inverse discrete wavelet transform is used to reconstruct the fused images. After that, principal component analysis （PCA） is applied to reduce the dimension and obtain the features of the fusion data before classification. Based on the features of the fused images, an accuracy rate of 80. 95 % for depression recognition is achieved using a leave-one-out cross-validation test. It can be concluded that this wavelet fusion scheme has the ability to improve the current diagnosis of depression.展开更多
基金The National Natural Science Foundation of China(No.30900356,81071135)
文摘Both functional magnetic resonance imaging （fMRI） and diffusion tensor imaging （DTI） can provide different information of the human brain, so using the wavelet transform method can achieve a fusion of these two types of image data and can effectively improve the depression recognition accuracy. Multi-resolution wavelet decomposition is used to transform each type of images to the frequency domain in order to obtain the frequency components of the images. To each subject, decomposition components of two images are then added up separately according to their frequencies. The inverse discrete wavelet transform is used to reconstruct the fused images. After that, principal component analysis （PCA） is applied to reduce the dimension and obtain the features of the fusion data before classification. Based on the features of the fused images, an accuracy rate of 80. 95 % for depression recognition is achieved using a leave-one-out cross-validation test. It can be concluded that this wavelet fusion scheme has the ability to improve the current diagnosis of depression.
