Linearly constrained minimum variance spatial filtering for localization of conductivity changes in electrical impedance tomography

被引:1
作者
Fernandez-Corazza, M. [1 ,2 ,3 ]
von Ellenrieder, N. [1 ,2 ]
Muravchik, C. H. [1 ,4 ]
机构
[1] Univ Nacl La Plata, Fac Ingn, LEICI, RA-1900 Buenos Aires, DF, Argentina
[2] Consejo Nacl Invest Cient & Tecn CONICET, Buenos Aires, DF, Argentina
[3] Univ Nacl La Plata, Fac Ingn, Dept Ciencias Basicas, La Plata, Buenos Aires, Argentina
[4] Comis Invest Cient Prov Buenos Aires CIC PBA, La Plata, Buenos Aires, Argentina
关键词
electrical impedance tomography; spatial filtering; linearly constrained minimum variance spatial filter; localization of conductivity changes; IMAGE-RECONSTRUCTION; BRAIN-FUNCTION; REGULARIZATION;
D O I
10.1002/cnm.2703
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
We localize dynamic electrical conductivity changes and reconstruct their time evolution introducing the spatial filtering technique to electrical impedance tomography (EIT). More precisely, we use the unit-noise-gain constrained variation of the distortionless-response linearly constrained minimum variance spatial filter. We address the effects of interference and the use of zero gain constraints. The approach is successfully tested in simulated and real tank phantoms. We compute the position error and resolution to compare the localization performance of the proposed method with the one-step Gauss-Newton reconstruction with Laplacian prior. We also study the effects of sensor position errors. Our results show that EIT spatial filtering is useful for localizing conductivity changes of relatively small size and for estimating their time-courses. Some potential dynamic EIT applications such as acute ischemic stroke detection and neuronal activity localization may benefit from the higher resolution of spatial filters as compared to conventional tomographic reconstruction algorithms. Copyright (c) 2015 John Wiley & Sons, Ltd.
引用
收藏
页码:1 / 16
页数:16
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