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.

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.

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.

Electrocardiography (ECG) signals and the information obtained through the analysis of these signals constitute the main source of diagnosis for many cardiovascular system diseases. Therefore, accurate analyses of ECG signals are very important for correct diagnosis. In this study, an ECG analysis toolbox together with a user-friendly graphical user interface, which contains the all ECG analysis steps between the recording unit and the statistical investigation, is developed. Furthermore, a new feature calculation methodology is proposed for ECG analysis, which carries distinct information than amplitudes and durations of ECG main waves and can be used in artificial intelligence studies. Developed toolbox is tested using both Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia ECG Database and an experimentally collected dataset for performance evaluation. The results show that ECG analysis toolbox presented in this study increases the accuracy and reliability of the ECG main wave detection analysis, highly fasten the process duration compared to manual ones and the new feature set can be used as a new parameter for decision support systems about ECG based on artificial intelligence.

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Magnetic resonance electrical impedance tomography (MREIT) is a technique that produces images of conductivity in tissues and phantoms. In this technique electrical currents are applied to an object and the resulting magnetic flux density is measured using magnetic resonance imaging (MRI) and the conductivity distribution is reconstructed using these MRI data. Currently the technique is used in research environments, primarily studying phantoms and animals. In order to translate MREIT to clinical applications, strict safety standards need to be established, especially for safe current limits. However, there are currently no standards for safe current limits specific to MREIT. Until such standards are established, human MREIT applications need to conform to existing electrical safety standards in medical instrumentation, such as the IEC601. This protocol limits patient auxiliary currents to 100μA for low frequencies. However, published MREIT studies have utilized currents 10 to 400 times larger than this limit, bringing into question whether the clinical applications of MREIT are attainable under current standards. In this study, we investigated the feasibility of MREIT to accurately reconstruct the relative conductivity of a simple agarose phantom using 200μA total injected current and we tested the performance of two MREIT reconstruction algorithms. These reconstruction algorithms used are the iterative sensitivity matrix method (SMM) by Ider and Birgul in 1998 with Tikhonov regularization and the Harmonic BZ proposed by Oh et al in 2003. The reconstruction techniques were tested at both 200μA and 5mA injected currents to investigate their noise sensitivity at low and high current conditions. It should be noted that 200μA total injected current into a cylindrical phantom generates only 14.7μA current in imaging slice. Similarly, 5mA total injected current results in 367μA in imaging slice. Total acquisition time for 200μA and 5mA experiments were about one hour and 8.5 minutes respectively. The results demonstrate that conductivity imaging is possible at low currents using the suggested imaging parameters and reconstructing the images using iterative SMM with Tikhonov regularization, which appears to be more tolerant to noisy data than Harmonic BZ.

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In spite of their widespread use, toxicity of silica nanoparticles (SiO 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 SiO 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 SiO NPs), group III (0.4 mM SiO NPs) and group IV (0.8 mM SiO 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. SiO 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 SiO groups compared to the control group. Numerous SiO NPs in cytoplasm and connective tissue were observed in all dose groups. These findings showed that 20 nm sized SiO NPs enter the connective tissue and cytoplasm of uterine muscle cells and cause oxidative stress and apoptosis leading to impaired uterine contractile activity.