Evren Değirmenci

Anisotropic conductivity imaging with MREIT using equipotential projection algorithm

Magnetic resonance electrical impedance tomography (MREIT) combines magnetic flux or current density measurements obtained by magnetic resonance imaging (MRI) and surface potential measurements to reconstruct images of true conductivity with high spatial resolution. Most of the biological tissues have anisotropic conductivity; therefore, anisotropy should be taken into account in conductivity image reconstruction. Almost all of the MREIT reconstruction algorithms proposed to date assume isotropic conductivity distribution. In this study, a novel MREIT image reconstruction algorithm is proposed to image anisotropic conductivity. Relative anisotropic conductivity values are reconstructed iteratively, using only current density measurements without any potential measurement. In order to obtain true conductivity values, only either one potential or conductivity measurement is sufficient to determine a scaling factor. The proposed technique is evaluated on simulated data for isotropic and anisotropic conductivity distributions, with and without measurement noise. Simulation results show that the images of both anisotropic and isotropic conductivity distributions can be reconstructed successfully.

Practical Realization of Magnetic Resonance Conductivity Tensor Imaging (MRCTI)

Magnetic resonance conductivity tensor imaging (MRCTI) is an emerging modality which reconstructs images of anisotropic conductivity distribution within a volume conductor. Images are reconstructed based on magnetic flux density distribution induced by an externally applied probing current, together with a resultant surface potential value. The induced magnetic flux density distribution is measured using magnetic resonance current density imaging techniques. In this study, MRCTI data acquisition is experimentally implemented and anisotropic conductivity images of test phantoms are reconstructed using recently proposed MRCTI reconstruction algorithms.

Image Reconstruction in Magnetic Resonance Conductivity Tensor Imaging (MRCTI)

Almost all magnetic resonance electrical impedance tomography (MREIT) reconstruction algorithms proposed to date assume isotropic conductivity in order to simplify the image reconstruction. However, it is well known that most of biological tissues have anisotropic conductivity values. In this study, four novel anisotropic conductivity reconstruction algorithms are proposed to reconstruct high resolution conductivity tensor images. Performances of these four algorithms and a previously proposed algorithm are evaluated in several aspects and compared.

Detection of ECG characteristic points using multiresolution analysis

Electrocardiography (ECG) signals and the information obtained from the analysis of these signals constitute the main source of information in determination of numerous cardiovascular system disease as well as in utility analysis of methods to be used for treatment of these diseases. In this study, ECG signals measured from living being are analyzed in computer environment in two parts namely filtering and feature extraction. In filtering phase, low frequency fluctuation on the baseline of the signal are straightened first using wavelet transform based filtering and then electric network noise are removed by designing a low pass butterworth filter. In feature extraction phase, timings of ECG characteristic waves (P, QRS complex, T) are determined using wavelet decomposition method. Obtained results show %95 or over success in determination of P, QRS and T timings. Measured ECG data used in this study are provided from Mersin University Medicine Faculty Biophysics Department and MIT physio database (www.phsionet.org).

Current source design for MREIT technique and its experimental application

Conductivity distribution can be imaged using Magnetic Resonance Electrical Impedance Tomography (MREIT) technique. This technique is composed of Magnetic Resonance Imaging (MRI) and Electrical Impedance Tomography (EIT) techniques and based on imaging conductor object using MRI system while applying electrical current to the conductor. In an MREIT system, current should be applied to the object in synchrony with the MR pulse sequence. In this study, a current source compatible with MRI system has been developed. The current source is designed so as to be controlled by the computer and used by an interface. The results obtained by the MREIT system, applying 20 mA current to the experimental phantom with the designed current source, are given and discussed in the study.

Comparison of magnetic resonance electrical impedance tomography (MREIT) reconstruction algorithms

Several algorithms have been proposed for image reconstruction in MREIT. These algorithms reconstruct conductivity distribution either directly from magnetic flux density measurements or from reconstructed current density distribution. In this study, performance of all major algorithms are evaluated and compared on a common platform, in terms of their reconstruction error, reconstruction time, perceptual image quality, immunity against measurement noise, required electrode size. J-Substitution (JS) and Hybrid J-Substitution algorithms have the best reconstruction accuracy but they are among the slowest. Another current density based algorithm, Equipotential Projection (EPP) algorithm along with magnetic flux density based Bz Sensitivity (BzS) algorithm has moderate reconstruction accuracy. BzS algorithm is the fastest.

Effects of silica nanoparticles on isolated rat uterine smooth muscle

Abstract In spite of their widespread use, toxicity of silica nanoparticles (SiO2 NPs) to mammalian has not been extensively investigated. In the present study, it is aimed to investigate the effects and the mechanism of action of 20 nm sized SiO2 NPs on isolated uterine smooth muscle. A total number of 84 preparations of uterine strips were used in the experiments. Study was designed as four groups: group I (control), group II (0.2 mM SiO2 NPs), group III (0.4 mM SiO2 NPs) and group IV (0.8 mM SiO2 NPs). Spontaneous contractions were recorded using mechanical activity recording system. Superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) activities and malondialdehyde (MDA) levels were measured using the spectrophotometric methods. Apoptosis of the cells was detected using immunofluorescence staining assay. SiO2 NP distribution and ultrastructural changes were determined by transmission electron microscopy. In groups II–IV, the frequency of contraction was significantly lower than that of the group I, whereas the contraction energy significantly decreased only in group IV. SOD and GSH-Px activities were significantly lower in experimental groups compared to the control group. MDA level and apoptotic cells were significantly higher in all SiO2 groups compared to the control group. Numerous SiO2 NPs in cytoplasm and connective tissue were observed in all dose groups. These findings showed that 20 nm sized SiO2 NPs enter the connective tissue and cytoplasm of uterine muscle cells and cause oxidative stress and apoptosis leading to impaired uterine contractile activity.

Determination of drug activity on pulmonary arterial hypertension using frequency-domain analysis of measured ECG signals

Electrocardiography (ECG) signals and the information obtained through the analysis of these signals constitute the main source of diagnosis of many cardiovascular system diseases. Pulmonary arterial hypertension (PAH) is a disease without any known treatment and causes failure in the right side of the heart and finally death. In this study, experimentally measured ECG signals were analyzed in frequency domain to observe the effects of PAH disease as well as medications used in the treatment of PAH on ECG main waves (P wave, T wave, QRS Complex). Wavelet transform based algorithms were used for filtering raw ECG signals and feature extraction phases. Then, for each main wave, fundamental frequency values, fundamental frequency amplitudes and the full width half maximum (FWHM) values of frequency spectrum were obtained and the changes of these parameters in disease and treatment situations were examined statistically.

Determination of the Effect of SiO 2 Nanoparticles on Spontaneous Activity of Rat Uterus Smooth Muscles using Wavelet Scalogram Analysis

Today, nanomaterials are used in many industrial areas such as electronics, optics, textiles, as well as in medical devices, biosensors, etc. Among various types of nanoparticles (NPs), silica nanoparticles (SiO2 NPs) are one of the most used NPs, especially in nanostructuring, drug delivery, cosmetic and optical imaging agents. Although NPs have a great importance in nanotechnology, they also have a potential toxic effect, which cause health problems. Uterus is a hollow muscular organ situated deep within the female pelvic cavity. The uterine smooth muscle, which is situated in uterus, is able to produce regular spontaneous contractions without any hormonal or nervous input [17]. Uterine contractions are important in many reproductive functions including the transport of sperms and embryo, menstruation, pregnancy and parturition. In this study, the effect of SiO2 NPs on uterine contractions are investigated. Experimentally recorded spontaneous activity of uterus are analyzed using wavelet transform. Obtained results show that, increasing doses of SiO2 NPs decrease the contraction frequency significantly, while energy of contractions does not change considerably.

Determination of Drug Activity on Pulmonary Arterial Hypertension using Time Domain Parameters of ECG

Electrocardiography (ECG) signals and the information obtained from the analysis of these signals are the main source of information in determination of numerous cardiovascular system diseases. Pulmonary arterial hypertension (PAH) is a progressive disease of the pulmonary vasculature which results failure in the right side of heart and finally death. In this study, experimentally measured ECG signals were analyzed in computer environment for determination of the effect of PAH disease and its possible treatments on ECG signal’s main waves (P wave, QRS complex and T wave). Wavelet transform based computer algorithms were developed for both filtering the raw signals and feature extraction. Finally, determined main wave amplitudes and durations were statistically analyzed and obtained results were discussed.