一种多点拟合的恒定立体角纤维重建模型  

Fiber Reconstruction Model with Constant Solid Angle Based on Multi-point Fitting

作　　者：李浩东 王远军[1] LI Haodong;WANG Yuanjun(Institute of Medical Imaging Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学医学影像工程研究所,上海200093

出　　处：《小型微型计算机系统》2024年第5期1116-1121,共6页Journal of Chinese Computer Systems

基　　金：上海市自然科学基金项目(18ZR1426900)资助。

摘　　要：基于弥散磁共振成像DTI的纤维追踪技术是非侵入性活体脑神经研究的关键技术.恒定立体角重建模型CSA是基于DTI发展而来的一种纤维重建模型,能够根据采样球壳上的数据对弥散方向分布函数进行线性径向投影计算,从而进行纤维重建.目前,恒定立体角纤维重建模型存在鲁棒性较差,重建纤维过于杂乱以及弥散方向分布偏差的问题.针对上述问题,本文提出MCSA(Multipoint Constant Solid Angle)模型,首先引入可以使弥散方向分布函数更加准确的最小二乘法,接着通过自适应高斯函数引入多点拟合弥散信息提高模型鲁棒性和抗噪性.最后,本文分别使用Fibercup、ISMRM2015年模拟数据以及Stanford HARDI真实影像数据对传统CSA模型以及本文提出MCSA模型进行对比分析,结果表明,利用本文提出MCSA模型重建的纤维更加符合客观规律,并且在一定程度上减少了假阳性纤维的生成.The fiber tracking technology based on DTI(diffusion tensor imaging)is a key technology for non-invasive in vivo brain nerve research.CSA(Constant Solid Angle)is a fiber reconstruction model in DTI.It can calculate the dispersion direction distribution function by linear radial projection according to the data on the sampled spherical shell,so as to reconstruct the fiber.At present,the DTI based fiber reconstruction model with constant solid angle has the problems of poor robustness,too messy reconstruction fibers and deviation of dispersion direction distribution.To solve the above problems,this paper proposes the MCSA(Multipoint Constant Solid Angle)model.First,the least squares method is introduced to make the dispersion direction distribution function more accurate,and then the adaptive Gaussian function is introduced to improve the robustness and noise resistance of the model by introducing multi-point fitting dispersion information.Finally,this paper uses Fibercup,ISMRM 2015 simulation data and Stanford HARDI real image data to compare and analyze the traditional CSA model and the MCSA model proposed in this paper.The results show that the fibers reconstructed using the MCSA model proposed in this paper are more consistent with the objective law,and to some extent,the generation of false positive fibers is reduced.

关 键 词：弥散张量成像 纤维重建 恒定立体角模型 邻域信息 移动最小二乘法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]