Zhang Tuo, Guo Lei, Li Kaiming, Zhu Dajing, Cui Guangbin, Liu Tianming
School of Automation, Northwestern Polytechnical University, Xi'an, China.
Med Image Comput Comput Assist Interv. 2011;14(Pt 2):42-9. doi: 10.1007/978-3-642-23629-7_6.
Study of structural and functional connectivities of the human brain has received significant interest and effort recently. A fundamental question arises when attempting to measure the structural and/or functional connectivities of specific brain networks: how to best identify possible Regions of Interests (ROIs)? In this paper, we present a novel ROI prediction framework that localizes ROIs in individual brains based on learned fiber shape models from multimodal task-based fMRI and diffusion tensor imaging (DTI) data. In the training stage, ROIs are identified as activation peaks in task-based fMRI data. Then, shape models of white matter fibers emanating from these functional ROIs are learned. In addition, ROIs' location distribution model is learned to be used as an anatomical constraint. In the prediction stage, functional ROIs are predicted in individual brains based on DTI data. The ROI prediction is formulated and solved as an energy minimization problem, in which the two learned models are used as energy terms. Our experiment results show that the average ROI prediction error is 3.45 mm, in comparison with the benchmark data provided by working memory task-based fMRI. Promising results were also obtained on the ADNI-2 longitudinal DTI dataset.
近年来，对人类大脑结构和功能连接性的研究受到了广泛关注并投入了大量精力。在试图测量特定脑网络的结构和/或功能连接性时，出现了一个基本问题：如何最好地识别可能的感兴趣区域（ROI）？在本文中，我们提出了一种新颖的ROI预测框架，该框架基于从多模态任务功能磁共振成像（fMRI）和扩散张量成像（DTI）数据中学习到的纤维形状模型，在个体大脑中定位ROI。在训练阶段，ROI被识别为基于任务的fMRI数据中的激活峰值。然后，学习从这些功能ROI发出的白质纤维的形状模型。此外，学习ROI的位置分布模型以用作解剖学约束。在预测阶段，基于DTI数据在个体大脑中预测功能ROI。ROI预测被公式化并作为能量最小化问题求解，其中两个学习到的模型用作能量项。我们的实验结果表明，与基于工作记忆任务的fMRI提供的基准数据相比，平均ROI预测误差为3.45毫米。在ADNI - 2纵向DTI数据集上也获得了有希望的结果。
