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.

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.

In this study, photogrammetric coordinate measurement and color-based identification of EEG electrode positions on the human head are simultaneously implemented. A rotating, 2MP digital camera about 20 cm above the subject's head is used and the images are acquired at predefined stop points separated azimuthally at equal angular displacements. In order to realize full automation, the electrodes have been labeled by colored circular markers and an electrode recognition algorithm has been developed. The proposed method has been tested by using a plastic head phantom carrying 25 electrode markers. Electrode locations have been determined while incorporating three different methods: (i) the proposed photogrammetric method, (ii) conventional 3D radiofrequency (RF) digitizer, and (iii) coordinate measurement machine having about 6.5 mum accuracy. It is found that the proposed system automatically identifies electrodes and localizes them with a maximum error of 0.77 mm. It is suggested that this method may be used in EEG source localization applications in the human brain.

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.

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.