Use ofa prioriinformation in estimating tissue resistivities - application to measured data

Abstract

A statistically constrained minimum mean squares error estimator (MiMSEE) has been shown to be useful in estimating internal resistivity distribution by the use of simulated data. In this study, the performance of the MiMSEE algorithm is tested by using measured data from resistor phantoms. The MiMSEE uses a priori information on body geometry, electrode position, statistical properties of tissue resistivities, instrumentation noise and linearization error to calculate the optimum inverse matrix which maps the surface potentials to unknown regional resistivities. In this study, the MiMSEE is also constrained with the variance-covariance of the modelling error to improve the estimation accuracy. The data are obtained from two different phantom geometries, namely five-region and thorax. Using the measured data, the estimations are realized and errors are calculated. Then, the results are compared with the results obtained by using a conventional least squares error estimator (LSEE). The five-region model results show similarity with the simulation study results of Baysal and Eyüboğlu. On the thorax model, the total estimation error is 34.2% with the MiMSEE compared with 856% with the LSEE. It is concluded that the MiMSEE is more robust than the LSEE and applicable to measured data.

Use ofa prioriinformation in estimating tissue resistivities—application to human datain vivo

“…They showed that the quantitative accuracy of the estimated regional resistivities can be improved if a priori geometrical data are combined with the statistical data on tissue resistivities and the instrumentation noise. The former one (Baysal and Eyüboglu 1998) tested the algorithm on different two-dimensional, circular numerical models and the latter one (Baysal and Eyüboglu 1999) tested the algorithm on resistor phantoms having similar geometries to those in the former (Baysal and Eyüboglu 1998). In both studies, the MiMSEE algorithm was compared to the conventional least-squares estimator (LSEE).…”

Application of Kalman Filter for the estimation of human head tissue conductivities

“…İnsan kafa derisi üzerinde oluşan elektriksel potansiyel dağılımı φ(x,y,z) ile öziletkenlik dağılımı σ(x,y,z) arasındaki ilişki, Poisson denklemi ile verilebilir: Bu çalışmada doku öziletkenliği kestirimi yapılabilmesi için yüzey potansiyelleri -doku öziletkenlikleri arasındaki doğrusal olmayan ilişkinin doğrusallaştırılması işlemi gereklidir. Bu işlem, daha önceden önerilmiş olan ileri dönüşüm matrisinin hesaplanması ile gerçekleştirilebilir [9][10][11][12]. İleri dönüşüm matrisi (M), her veri toplama profilinin öziletkenlik-elektrot potansiyeli karakteristiğine global en küçük kareler yöntemi kullanılarak uydurulan doğruların eğimlerini içerir.…”

Magnetic resonance current density image reconstruction in the spatial frequency domain

“…Depending on the degree of low pass filtering during the derivative operation, F x and F y range from 0.14 to 1.8. In equation [1], the T 2 attenuation effect during the current application time is neglected. Therefore, σ J will be degraded further when T 2 is short compared with T c .…”

“…The information about electrical conductivity distribution inside biological tissues can be greatly used in many areas such as source localization of ECG and EEG signals, estimation of therapeutic current distribution during electrical therapy, and monitoring of physiological functions (1)(2)(3). Electrical impedance tomography (EIT) is a conductivity imaging modality in which many surface electrodes are attached to the subject surface to measure the electric potentials produced when electrical current is injected to the subject.…”