Conventional injected-current electrical impedance tomography (EIT) and magnetic resonance imaging (MRI) techniques can be combined to reconstruct high resolution true conductivity images. The magnetic flux density distribution generated by the internal current density distribution is extracted from MR phase images. This information is used to form a fine detailed conductivity image using an Ohm's law based update equation. The reconstructed conductivity image is assumed to differ from the true image by a scale factor. EIT surface potential measurements are then used to scale the reconstructed image in order to find the true conductivity values. This process is iterated until a stopping criterion is met. Several simulations are carried out for opposite and cosine current injection patterns to select the best current injection pattern for a 2D thorax model. The contrast resolution and accuracy of the proposed algorithm are also studied. In all simulation studies, realistic noise models for voltage and magnetic flux density measurements are used. It is shown that, in contrast to the conventional EIT techniques, the proposed method has the capability of reconstructing conductivity images with uniform and high spatial resolution. The spatial resolution is limited by the larger element size of the finite element mesh and twice the magnetic resonance image pixel size.

Magnetic resonance electrical impedance tomography (MR-EIT) is an emerging imaging technique that reconstructs conductivity images using magnetic flux density measurements acquired employing MRI together with conventional EIT measurements. In this study, experimental MR-EIT images from phantoms with conducting and insulator objects are presented. The technique is implemented using the 0.15 T Middle East Technical University MRI system. The dc current method used in magnetic resonance current density imaging is adopted. A reconstruction algorithm based on the sensitivity matrix relation between conductivity and only one component of magnetic flux distribution is used. Therefore, the requirement for object rotation is eliminated. Once the relative conductivity distribution is found, it is scaled using the peripheral voltage measurements to obtain the absolute conductivity distribution. Images of several insulator and conductor objects in saline filled phantoms are reconstructed. The L2 norm of relative error in conductivity values is found to be 13%, 17% and 14% for three different conductivity distributions.

Electrical impedance tomography (EIT) produces cross-sectional images of the electrical resistivity distribution within the body, made from voltage or current measurements through electrodes attached around the body. The authors describe a gated EIT system to image the cardiogenic electrical resistivity variations and the results of in vivo studies on human subjects. It is shown that the sensitivity of EIT to tissue resistivity variations due to blood perfusion is good enough to image blood flow to the lungs; hence, abnormalities in pulmonary perfusion, such as pulmonary embolism, should appear in EIT images. In addition, more valuable information related to the cardiac activity can be gained from EIT images than from impedance cardiography. It is thus likely that a cardiac output index may be calculable from the average resistivity variations over the ventricles, but considerable research is required before the images can be understood in detail.

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.

The existence of variations of normal human thoracic impedance, during the cardiac cycle to high frequency electrical current is well known. Since the impedance variations within the thorax are synchronous with the electrocardiogram (ECG), they are attributed to cardiac activity. They can arise from the change of either the rate of blood flow or the blood volume in the heart chambers, the great blood vessels and the lungs. However, their relative contribution is not known. Many investigators have worked on the non-invasive determination of some cardiac parameters using surface electrode impedance measurements on the thorax. Since the relationships between the measurement results and the pulsatile circulation of blood in various organs inside the chest are not well known, the information determined by surface impedance measurements is not as accurate as the results of invasive techniques. Recent advances in the clinical use of applied potential tomography (APT), or electrical impedance imaging, showed that the APT system gives a good soft-tissue contrast and has good sensitivity to resistivity changes. It is therefore concluded that the origin of thoracic impedance changes related to cardiac activity can be deduced from APT images. Our initial studies of ECG gated dynamic APT images of the thorax show that cardiac related thoracic impedance variations originating from different organs can be separated. Sequential APT images of the thorax during the cardiac cycle are presented. The movement of blood from the ventricles to the lungs and vascular system and back to the ventricles is observable in these images.(ABSTRACT TRUNCATED AT 250 WORDS)