Biomedical subjects

Tzu-Jen Kao

Publications and source records attributed to Tzu-Jen Kao.

4 recordsLinked to original sources

PubMed2006

A 3D reconstruction algorithm for EIT using a handheld probe for breast cancer detection.

A 3D reconstruction algorithm for electrical impedance tomography is presented for determining the distribution of electrical properties inside the body, given electrical measurements made on the surface. A linearized reconstruction algorithm using planar electrode arrays in a handheld probe geometry developed by Mueller et al (1999 IEEE Trans. Biomed. Eng. 46 1379-86) has been refined and extended in this paper. This algorithm is based on linearizing the conductivity about a constant value. We have extended the distance below the electrodes at which a target can be imaged by using a combination of two regularization schemes and a weighted mesh. An appropriate combination of Tikhonov and NOSER regularization produces satisfactory static images of a 2 cm cube placed 2 cm below the array, and difference images of a 1 cm cube 4 cm away from the array. The weighted mesh allows use of fixed regularization parameters for all depths of the target.

Breast Neoplasms↗

PubMed2006

Reducing boundary effects in static EIT imaging.

Electrical impedance tomography (EIT) is a non-invasive technique used to image the electrical conductivity and permittivity within a body from measurements taken on the body's surface. High-quality static images are required for many medical imaging applications. Forming such images usually requires an accurate way to calculate the expected voltages on the surface resulting from the application of known currents to that surface. This is described as the forward problem. This paper introduces a new method to improve static images by using an improved forward solution which estimates a different conductivity value for each applied current pattern. This method, creating an automatically adjusting forward solution, can improve the sensitivity of static images under many EIT imaging applications. It does so by reducing the boundary effects caused by electrodes and any layered structures near them such as skin. The drawback of this method is that circularly symmetric structures of interest may be suppressed or eliminated from the images. The performance of this method is illustrated in a 2D circular phantom with simulation data from both a FEM model and experimental data.

Algorithms↗

PubMed2006

Dynamic electrical impedance imaging of a chest phantom using the Kalman filter.

A dynamic complex impedance imaging technique is developed with the aid of the linearized Kalman filter (LKF) for real-time reconstruction of the human chest. The forward problem is solved by an analytical method based on the separation of variables and Fourier series. The inverse problem is treated as a state estimation problem. The nonlinear measurement equation is linearized about the best homogeneous impedivity value as an initial guess, and the impedivity distribution is estimated with the aid of the Kalman estimator. The Kalman gain matrix is pre-computed and stored off-line to minimize the on-line computational time. Simulation and phantom experiment are reported to illustrate the reconstruction performances in the sense of spatio-temporal resolution in a simplified geometry of the human chest.

Algorithms↗

PubMed2003

Distinguishability of inhomogeneities using planar electrode arrays and different patterns of applied excitation.

Electrical impedance tomography (EIT) is a non-invasive technique used to image the electrical conductivity and permittivity within a body from measurements taken on the body surface. Four methods are being investigated for breast cancer diagnosis by EIT today: Single voltage source, single current source and multiple current sources with a fixed pre-determined 'canonical' pattern of currents and an adaptively determined 'optimal' pattern of currents. To determine which of these four methods might yield the best distinguishability using planar electrode arrays for breast cancer detection, we placed electrode arrays on a saline tank and used each excitation pattern to detect a conducting target placed at the centre of a flat electrode array in two geometries: mammography geometry and single probe geometry. The result was that the multiple current sources method had higher distinguishability than either the SCS or the SVS method. In both these electrode geometries, the optimal current pattern had higher distinguishability than the other patterns at all distances.

Breast↗