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A new data acquisition system has been developed. This system measures the external magnetic fields due to induced currents in the body at a relatively low operation frequency of 50 kHz. Data is obtained by scanning a 2-D area on the body surface. For each transmitter position, a single sample (averaged) of the field distribution is used for image reconstruction. The Steepest Descent Algorithm is used to solve the inverse problem related to the field profiles. High-resolution images of agar blocks and an anesthetized leech are presented. The system sensitivity is measured as 13.2 mV/(S/m) using saline solution phantoms and as 155 V/S using resistors. The signal to noise ratio in the measurements is calculated to be 35.44 dB. The linearity in the measurements is explored using saline solutions in the biological conductivity range. The nonlinearity is measured to be 3.96% of the full scale. The nonlinearity is found to be 0.12% when resistor phantoms are used. The spatial resolution in the conductivity images is measured as 9.36 mm for a 7.5-mm-diameter cylindrical agar object. The results show that it is possible to distinguish two bars separated 14.4 mm from each other. Index Terms--Conductivity measurements, electrical impedance imaging, magnetic induction imaging, medical imaging.

A data-acquisition system has been developed to image electrical conductivity of biological tissues via contactless measurements. This system uses magnetic excitation to induce currents inside the body and measures the resulting magnetic fields. The data-acquisition system is constructed using a PC-controlled lock-in amplifier instrument. A magnetically coupled differential coil is used to scan conducting phantoms by a computer controlled scanning system. A 10000-turn differential coil system with circular receiver coils of radii 15 mm is used as a magnetic sensor. The transmitter coil is a 100-turn circular coil of radius 15 mm and is driven by a sinusoidal current of 200 mA (peak). The linearity of the system is 7.2% full scale. The sensitivity of the system to conducting tubes when the sensor-body distance is 0.3 cm is 21.47 mV/(S/m). It is observed that it is possible to detect a conducting tube of average conductivity (0.2 S/m) when the body is 6 cm from the sensor. The system has a signal-to-noise ratio of 34 dB and thermal stability of 33.4 mV/[degrees]C. Conductivity images are reconstructed using the steepest-descent algorithm. Images obtained from isolated conducting tubes show that it is possible to distinguish two tubes separated 17 mm from each other. The images of different phantoms are found to be a good representation of the actual conductivity distribution. The field profiles obtained by scanning a biological tissue show the potential of this methodology for clinical applications. Index Terms--Contactless conductivity measurement, electrical impedance imaging, magnetic induction, medical imaging.

A new imaging modality is introduced to image electrical conductivity of biological tissues via contactless measurements. This modality uses magnetic excitation to induce currents inside the body and measures the magnetic fields of the induced currents. In this study, the mathematical basis of the methodology is analyzed and numerical models are developed to simulate the imaging system. The induced currents are expressed using the (mathematical expression not reproducible in ASCII)(phi)formulation of the electric field where (mathematical expression not reproducible in ASCII) is the magnetic vector potential and (phi) is the scalar potential function. It is assumed that (mathematical expression not reproducible in ASCII) describes the primary magnetic vector potential that exists in the absence of the body. This assumption considerably simplifies the solution of the secondary magnetic fields caused by induced currents. In order to solve (phi) for objects of arbitrary conductivity distribution a three-dimensional (3-D) finite-element method (FEM) formulation is employed. A specific 7 x 7-coil system is assumed nearby the upper surface of a 10 x 10 x 5-cm conductive body. A sensitivity matrix, which relates the perturbation in measurements to the conductivity perturbations, is calculated. Singular-value decomposition of the sensitivity matrix shows various characteristics of the imaging system. Images are reconstructed using 500 voxels in the image domain, with truncated pseudoinverse. The noise level is assumed to produce a representative signal-to-noise ratio (SNR) of 80 dB. It is observed that it is possible to identify voxel perturbations (of volume 1 cm(super 3)) at 2 cm depth. However, resolution gradually decreases for deeper conductivity perturbations. Index Terrns: Electrical conductivity, finite-element method (FEM), magnetic induction, medical imaging.

A method for obtaining a practical inverse for the distribution of neural activity in the human cerebral cortex is developed for electric, magnetic, and bimodal data to exploit their complementary aspects. Intracellular current is represented by current dipoles uniformly distributed on two parallel sulci joined by a gyrus. Linear systems of equations relate electric, magnetic, and bimodal data to unknown dipole moments. The corresponding lead-field matrices are characterized by singular value decomposition (SVD). The optimal reference electrode location for electric data is chosen on the basis of the decay behavior of the singular values. The singular values of these matrices show better decay behavior with increasing number of measurements, however, that property is useful depending on the noise in the measurements. The truncated SVD pseudo-inverse is used to control noise artifacts in the reconstructed images. Simulations for single-dipole sources at different depths reveal the relative contributions of electric and magnetic measures. For realistic noise levels the performance of both unimodal and bimodal systems do not improve with an increase in the number of measurements beyond [approximately]100. Bimodal image reconstructions are generally superior to unimodal ones in finding the center of activity. Index Terms - Electroencephalography (EEG), magnetic source imaging, magnetoencaphalography (MEG), singular value decomposition (SVD), truncated SVD pseudo-inversion.