弥散张量成像（DTI）技术在大脑和脊髓相关的疾病分析、诊断上有着广泛的应用。传统的向量模式算法在处理分析DTI数据时，会破坏数据的结构和内在相关性，增加计算成本。针对DTI数据的特点，本课题提出了基于张量模式的DTI数据处理分析方法。首先，提出了基于二重度量的DTI图像配准方法，以解决DTI图像配准中张量场方向信息比灰度信息更敏感的问题；其次，提出一种基于弥散系数权重矩阵的多线性主成分分析方法，以实现DTI数据的特征提取；再次，提出最优投影支持张量机算法，大幅降低算法的时间复杂度，并实现目标数据的最大可分；最后，把张量模式分类算法扩展到基于Map-Reduce的分布式平台上。本项目提出的基于张量模式的DTI数据模式分类方法，能够保留DTI数据的结构信息和内在相关性，实现目标数据的最大可分，大幅降低计算成本。本项研究将为基于DTI数据的智能医疗诊断系统提供理论依据和算法支持。

Diffusion tensor imaging (DTI) has broad prospects of application in disease detection and analysis, especially in brain and spinal cord. The traditional vector-based algorithms often fail in dealing with DTI data because the original data structure would be destroyed and the computing cost would be increased. In this project, the following solutions are proposed to address the above issues. Firstly, the double-registration method is developed to solve the problem that the direction is more sensitive than grey level in diffusion tensor image. Secondly, according to the distribution characteristics of diffusion coefficient, the multi-linear principle component analysis method is proposed to perform feature extraction and dimension reduction for DTI data. Thirdly, the optimum projection support tensor machine (OPSTM) is designed for automated classification of DTI data as optimal as possible. The OPSTM can both decrease the time complexity and increase the accuracy. Finally, the OPSTM algorithm is expanded to Map-Redue based distributed computing platform for automated classification for large-scale DTI data. The proposed methods from this project can keep the original information and internal structure correlation to a large extent, make target data maximum divisible, and substantially decrease computing cost. This research can provide the theory basis and algorithm support for intelligent medical system using DTI data.