Electrical and Computer Engineering Department, Northeastern University, Boston, MA, USA; Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Electrical and Computer Engineering Department, Northeastern University, Boston, MA, USA.
Med Image Anal. 2017 Apr;37:56-65. doi: 10.1016/j.media.2017.01.005. Epub 2017 Jan 24.
We present a method to estimate a multivariate Gaussian distribution of diffusion tensor features in a set of brain regions based on a small sample of healthy individuals, and use this distribution to identify imaging abnormalities in subjects with mild traumatic brain injury. The multivariate model receives apriori knowledge in the form of a neighborhood graph imposed on the precision matrix, which models brain region interactions, and an additional L sparsity constraint. The model is then estimated using the graphical LASSO algorithm and the Mahalanobis distance of healthy and TBI subjects to the distribution mean is used to evaluate the discriminatory power of the model. Our experiments show that the addition of the apriori neighborhood graph results in significant improvements in classification performance compared to a model which does not take into account the brain region interactions or one which uses a fully connected prior graph. In addition, we describe a method, using our model, to detect the regions that contribute the most to the overall abnormality of the DTI profile of a subject's brain.
我们提出了一种方法，用于根据一小部分健康个体估计一组脑区的扩散张量特征的多元高斯分布，并使用该分布识别轻度创伤性脑损伤患者的影像学异常。多元模型以施加在精度矩阵上的邻域图的形式接收先验知识，该邻域图用于对脑区之间的相互作用建模，以及额外的 L 稀疏约束。然后使用图形 LASSO 算法对模型进行估计，并使用健康和 TBI 受试者与分布均值的马氏距离来评估模型的判别能力。我们的实验表明，与不考虑脑区相互作用的模型或使用完全连接的先验图的模型相比，添加先验邻域图可显著提高分类性能。此外，我们描述了一种使用我们的模型来检测对受试者大脑的 DTI 图谱的整体异常贡献最大的区域的方法。
