Ugur Baysal

Use of a priori information in estimating tissue resistivities—application to human data in vivo

Accurate resistivity values are necessary to construct reliable numerical models to solve forward/inverse problems in EEG and to localize activity centres in functional brain imaging. These models require accurate geometry and resistivity distribution. The geometry may be extracted from high resolution images. The resistivity distribution may be estimated by using a statistically constrained minimum mean squared error estimator algorithm that has been developed previously by Baysal and Eyüboğlu. In this study, the data are obtained by EEG and MEG sensors during SEF/SEP experiments that involve nine human subjects. The numerical model is realistic, subject-specific and the scalp, the skull and the brain resistivities are estimated. By performing nine different estimations, we found average resistivities of 3.183, 64.559 and 2.833 omega m for scalp, skull and brain, respectively, all under 9% standard deviation. The discrepancies between these results and other works are discussed in detail.

Micro-morphologic changes around biophysically-stimulated titanium implants in ovariectomized rats

BackgroundOsteoporosis may present a risk factor in achievement of osseointegration because of its impact on bone remodeling properties of skeletal phsiology. The purpose of this study was to evaluate micro-morphological changes in bone around titanium implants exposed to mechanical and electrical-energy in osteoporotic rats.MethodsFifteen 12-week old sprague-dowley rats were ovariectomized to develop osteoporosis. After 8 weeks of healing period, two titanium implants were bilaterally placed in the proximal metaphyses of tibia. The animals were randomly divided into a control group and biophysically-stimulated two test groups with five animals in each group. In the first test group, a pulsed electromagnetic field (PEMF) stimulation was administrated at a 0.2 mT 4 h/day, whereas the second group received low-magnitude high-frequency mechanical vibration (MECHVIB) at 50 Hz 14 min/day. Following completion of two week treatment period, all animals were sacrificed. Bone sites including implants were sectioned, removed en bloc and analyzed using a microCT unit. Relative bone volume and bone micro-structural parameters were evaluated for 144 μm wide peri-implant volume of interest (VOI).ResultsMean relative bone volume in the peri-implant VOI around implants PEMF and MECHVIB was significantly higher than of those in control (P < .05). Differences in trabecular-thickness and -separation around implants in all groups were similar (P > .05) while the difference in trabecular-number among test and control groups was significant in all VOIs (P < .05).ConclusionBiophysical stimulation remarkably enhances bone volume around titanium implants placed in osteoporotic rats. Low-magnitude high-frequency MECHVIB is more effective than PEMF on bone healing in terms of relative bone volume.

Application of electrical impedance tomography in diagnosis of emphysema-a clinical study

In this paper, electrical impedance tomography (EIT) ventilation images from a group of 12 patients (11 patients with emphysema and one patient with only chronic obstructive pulmonary disease (COPD) (chronic bronchitis) and a group of 15 normal subjects were acquired using a Sheffield mark 1 EIT system, at the levels of second, fourth and sixth intercostal spaces. Patients were diagnosed based on CT scans of the thorax, pulmonary function tests and posteroanterior x-ray graphs. One of the patients with emphysema has also a malignant lung tumour. Ventilation-related conductivity changes at total lung capacity (TLC) relative to residual volume were measured quantitatively in EIT images. These quantitative values demonstrate marked differences compared to those values obtained from the EIT images of 15 normal subjects. The EIT images of the patients were also compared with the CT images. In addition to the visual examination of the EIT images a statistical confidence test is applied to compare the images of the patients with the images of the normal subjects. Prior to statistical analysis all images are normalized with TLC to minimize the effect of mismatch between the TLC of different subjects. A normal mean image is created by averaging the normalized images from the normal subjects, at each intercostal space level. Than a 95% confidence interval is defined for each normal mean image. For each image of the patients, a confidence test image, which represents the deviations from the 95% confidence interval of the normal mean image, is created. The regions with emphysematous bulla and parencyhma are detectable in the confidence test images as regions of positive and negative deviations from the confidence interval of the normal mean, respectively. In the test images, it is possible to differentiate emphysematous parenchyma from emphysematous bulla, tumour structure, and COPD. However, the emphysematous bulla, the tumour structure, and COPD result in the same type of defect in the test images and are therefore indistinguishable from each other. In some case, off-plane contributions in the EIT images may result in underestimation of the defects. EIT may be a useful screening device in detecting emphysema rather than a diagnostic tool.

Tissue resistivity estimation in the presence of positional and geometrical uncertainties

Geometrical uncertainties (organ boundary variation and electrode position uncertainties) are the biggest sources of error in estimating electrical resistivity of tissues from body surface measurements. In this study, in order to decrease estimation errors, the statistically constrained minimum mean squared error estimation algorithm (MiMSEE) is constrained with a priori knowledge of the geometrical uncertainties in addition to the constraints based on geometry, resistivity range, linearization and instrumentation errors. The MiMSEE calculates an optimum inverse matrix, which maps the surface measurements to the unknown resistivity distribution. The required data are obtained from four-electrode impedance measurements, similar to injected-current electrical impedance tomography (EIT). In this study, the surface measurements are simulated by using a numerical thorax model. The data are perturbed with additive instrumentation noise. Simulated surface measurements are then used to estimate the tissue resistivities by using the proposed algorithm. The results are compared with the results of conventional least squares error estimator (LSEE). Depending on the region, the MiMSEE yields an estimation error between 0.42% and 31.3% compared with 7.12% to 2010% for the LSEE. It is shown that the MiMSEE is quite robust even in the case of geometrical uncertainties.

An extended Kalman filtering approach for the estimation of human head tissue conductivities by using EEG data: a simulation study.

In this study, we propose an extended Kalman filter approach for the estimation of the human head tissue conductivities in vivo by using electroencephalogram (EEG) data. Since the relationship between the surface potentials and conductivity distribution is nonlinear, the proposed algorithm first linearizes the system and applies extended Kalman filtering. By using a three-compartment realistic head model obtained from the magnetic resonance images of a real subject, a known dipole assumption and 32 electrode positions, the performance of the proposed method is tested in simulation studies and it is shown that the proposed algorithm estimates the tissue conductivities with less than 1% error in noiseless measurements and less than 5% error when the signal-to-noise ratio is 40 dB or higher. We conclude that the proposed extended Kalman filter approach successfully estimates the tissue conductivities in vivo.

Use of a priori information in estimating tissue resistivities: a simulation study

Accurate estimation of tissue resistivities in vivo is needed to construct reliable human body volume conductor models in solving forward and inverse bioelectric field problems. The necessary data for the estimation can be obtained by using the four-electrode impedance measurement technique, usually employed in electrical impedance tomography. In this study, a priori geometrical information with statistical properties of regional resistivities and linearization error as well as instrumentation noise has been incorporated into a new resistivity estimation algorithm which is called a statistically constrained minimum mean squares error estimator (MiMSEE) to improve estimation accuracy. MiMSEE intakes geometrical information from the image which is obtained by using a high-resolution imaging modality. This study is an extension of earlier work by Eyüboğlu et al and obtains simulated measurements from two numerical models containing five and six regions on a background region. Also, estimations are repeated by using up to eight multiple current electrode pairs, in order to observe the effect of estimation performance while increasing the number of measurements up to 96. The results are compared with a conventional least squares error estimator (LSEE) which is used in one-pass algorithms. It is shown that the MiMSEE estimation error is up to 27 times smaller than the LSEE error which is realized for a small, high-contrast region, for example the aorta. In estimating the regional resistivities, the MiMSEE algorithm requires 25.8 (for the five-region resistivity distribution) and 22.2 (for the six-region resistivity distribution) times more computational time than the LSEE. This gap between the computational times of the two algorithms decreases as the number of regions increases.

Use of a priori information in estimating tissue resistivities - application to measured data

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.

The effects of pulsed electromagnetic field (PEMF) on osteoblast-like cells cultured on titanium and titanium-zirconium surfaces.

Background Commercially pure Ti, together with Ti Ni, Ti-6Al-4V, and Ti-6Al-7Nb alloys, are among the materials currently being used for this purpose. Titanium-zirconium (TiZr) has been developed that allows SLActive surface modification and that has comparable or better mechanical strength and improved biocompatibility compared with existing Ti alloys. Furthermore, approaches have targeted making the implant surface more hydrophilic, as with the Straumann SLActive surface, a modification of the SLA surface. Purpose The aim of this study is to evaluate the effects of pulsed electromagnetic field (PEMF) to the behavior of neonatal rat calvarial osteoblast-like cells cultured on commercially pure titanium (cpTi) and titanium-zirconium alloy (TiZr) discs with hydrophilic surface properties. Materials and Methods Osteoblast cells were cultured on titanium and TiZr discs, and PEMF was applied. Cell proliferation rates, cell numbers, cell viability rates, alkaline phosphatase, and midkine (MK) levels were measured at 24 and 72 hours. Results At 24 hours, the number of cells was significantly higher in the TiZr group. At 72 hours, TiZr had a significantly higher number of cells when compared to SLActive, SLActive + PEMF, and machine surface + PEMF groups. At 24 hours, cell proliferation was significantly higher in the TiZr group than SLActive and TiZr + PEMF group. At 72 hours, TiZr group had higher proliferation rate than machine surface and TiZr + PEMF. Cell proliferation in the machine surface group was lower than both SLActive + PEMF and machine surface + PEMF. MK levels of PEMF-treated groups were lower than untreated groups for 72 hours. Conclusions Our findings conclude that TiZr surfaces are similar to cpTi surfaces in terms of biocompatibility. However, PEMF application has a higher stimulative effect on cells cultured on cpTi surfaces when compared to TiZr.

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

In this study Extended Kalman Filtering is proposed for the estimation of human head tissue conductivities by using EEG data. The proposed method first linearizes the relationship between the tissue conductivities and surface potentials (EEG measurements) and then iteratively estimates the tissue conductivities. In the study the mathematical background of the proposed method is presented and then performance of the proposed method is investigated by a simulation study. In the simulation study a three layered realistic head model (composed of scalp, skull and brain compartments) obtained from MR images of a real patient is used. The surface potential is calculated by using an arbitrarily chosen conductivity distribution. Then conductivity estimation is iteratively performed by using the calculated potentials and at each iteration relative error rates are calculated by comparing the orginal conductivities and estimated ones. It is found that the relative error rates decrease below of 1% after five iterations.

A fully automatic photogrammetric system design using a 1.3 MP web camera to determine EEG electrode positions

In this study a fully automatic fotogrammetric system is designed to determine the EEG electrode positions in 3D. The proposed system uses a 1.3 MP web camera rotating over the subjects head. The camera is driven by a step motor. The camera takes photos in every 7.20 angles during the rotation. In order to realize full automation, electrodes are labeled by colored circular markers and an electrode identification algorithm is develeoped for full automation. The proposed method is tested by using a realistic head phantom carrying 25 electrodes. The positions of the test electrodes are also measured by a conventional 3-D digitizer. The measurements are repeated 3 times for repeatibility purposes. It is found that 3-d digitizer localizes the electrodes with an average error of 8.46 mm, 7.63 mm and 8.32 mm, while the proposed system localizes the electrodes with an average error of 1.76 mm, 1.42 mm and 1.53 mm.