摘要
针对脑出血磁感应断层成像(MIT)中正问题模型过于简化、图像重建质量较低、算法收敛效率低、病变与背景间伪影较大、耗时较长等问题,该文提出一种用于脑出血MIT的改进牛顿-拉夫逊(NR)算法。将线性反投影(LBP)算法计算结果作为改进NR算法的迭代初值,在目标函数中加入自适应加速惩罚项和L2范数惩罚项,提高算法每一步迭代的效率,减少重建图像的伪影。引入投影算子P施加物理意义上的约束,提高收敛速度并改善成像质量。利用Comsol Multiphysics构建了包含头皮、颅骨、脑脊液和脑实质的真实3维颅脑模型。仿真计算了相位差检测值和灵敏度矩阵用于后续的图像重建。利用所提改进NR算法与5种图像重建算法分别对3个位置出血量分别为24 ml,14 ml,2 ml的脑出血进行磁感应断层成像。实验结果表明,所提算法相比其他5种算法重建图像的质量更高,成像时间平均只需NR算法的1/3。使用更少的迭代次数重建出更高质量的图像,并且能实现2 ml脑出血的图像重建,为脑出血的MIT检测提供一种新的有效算法。
To solve the problems of over-simplified positive problem model,low image reconstruction quality,low algorithm convergence efficiency,large artifacts between lesion and background,and long time consuming in IntraCerebral Hemorrhage(ICH)Magnetic Induction Tomography(MIT),an improved Newton-Raphson(NR)algorithm for MIT of intracerebral hemorrhage is proposed.The calculation results of Linear Back Projection(LBP)algorithm are used as the iterative initial values of the improved NR algorithm,the adaptive acceleration penalty term and the L2 norm penalty term are added to the objective function to improve the efficiency of each iteration of the algorithm and reduce the artifacts of the reconstructed image.A real threedimensional brain model including scalp,skull,cerebrospinal fluid and brain parenchyma is constructed by Comsol Multiphysics.The phase difference detection value and sensitivity matrix are simulated and calculated for subsequent image reconstruction.The proposed improved NR algorithm and five image reconstruction algorithms are used to perform magnetic induction tomography on intracerebral hemorrhage with blood loss of 24 ml,14 ml and 2 ml at three locations,respectively.The experimental results show that the proposed algorithm has higher quality of reconstructed images than the other five algorithms.The average imaging time is only 1/3 of the NR algorithm.The higher quality image is reconstructed with fewer iterations,the image reconstruction of 2 ml intracerebral hemorrhage can be realized,which provides a new and effective algorithm for MIT detection of intracerebral hemorrhage.
作者
曹弘贵
叶波
姜瑛
罗思琦
曹众楷
欧阳俊林
CAO Honggui;YE Bo;JIANG Ying;LUO Siqi;CAO Zhongkai;OUYANG Junlin(Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China;Yunnan Key Laboratory of Artificial Intelligence,Kunming 650500,China)
出处
《电子与信息学报》
EI
CSCD
北大核心
2023年第12期4477-4488,共12页
Journal of Electronics & Information Technology
基金
国家自然科学基金(62203195)
云南省中青年学术和技术带头人后备人才项目(202305AC160062)
云南省大学生创新创业训练计划(2021106740015)。
关键词
脑出血
磁感应断层成像
有限元分析
牛顿-拉夫逊算法
图像重建
IntraCerebral Hemorrhage(ICH)
Magnetic Induction Tomography(MIT)
Finite element analysis
Newton-Raphson algorithm
Image reconstruction
