基于压缩感知高阶张量扩散磁共振稀疏成像方法  

Higher Order Tensor Diffusion Magnetic Resonance Sparse Imaging Based on Compressed Sensing

作　　者：冯远静[1] 吴烨[1] 张贵军[1] 梁荣华[1] 

机构地区：[1]浙江工业大学信息工程学院信息处理与自动化研究所,杭州310023

出　　处：《模式识别与人工智能》2015年第8期710-719,共10页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.61379020;61075062);浙江省自然科学基金项目(No.LY13F030007);浙江省钱江人才计划项目(No.2012R10051)资助

摘　　要：高阶张量扩散磁共振成像技术是高分辨率显示活体脑白质微结构信息的重要方法,但其数据采集时间较长、纤维重构分辨率较低等缺点限制其在临床上的应用.文中在高阶张量模型的基础上提出一种稀疏加权的纤维方向分布估计方法.该方法首先建立高阶张量球面反卷积纤维方向估计模型,然后提出一种纤维方向的稀疏表示方法,最后建立稀疏约束反卷积的l1范数优化模型.针对优化模型的求解,提出一种用低阶训练稀疏字典解决高阶优化问题的计算方法.模拟数据和临床数据的实验表明,纤维方向分布估计方法有效提高高阶张量成像方法的角度分辨率,降低角度识别误差.High Order Tensor （HOT） diffusion magnetic resonance imaging is an important method to reveal the microstructural information of living brain white matter. However, its time-consuming data acquisition and low fiber reconstructing resolution limit its clinical application. In this paper, a fiber orientation estimation method with weighted sparse is presented based on HOT model. Firstly, the HOT spherical deconvolution model for fiber orientation estimation is established. Then, a sparse representation method of fiber orientation is put forward. Finally, a l1-norm optimization model is built for the sparse constraint deconvolution. A computing method of applying the training results of sparse dictionary with lower order into the high-order problem is proposed to achieve the solution of the optimization problem. The experimental results on simulated and vivo data show that the fiber orientation estimation method improves the angular resolution of HOT tensor imaging method and reduces the angle recognition error.

关 键 词：高阶张量 稀疏表示 反卷积 压缩感知 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]