Biomedical subjects

Jennifer L Mueller

Publications and source records attributed to Jennifer L Mueller.

3 recordsLinked to original sources

PubMed2004

Reconstructions of chest phantoms by the D-bar method for electrical impedance tomography.

The problem this paper addresses is how to use the two-dimensional D-bar method for electrical impedance tomography with experimental data collected on finitely many electrodes covering a portion of the boundary of a body. This requires an approximation of the Dirichlet-to-Neumann, or voltage-to-current density map, defined on the entire boundary of the region, from a finite number of matrix elements of the current-to-voltage map. Reconstructions from experimental data collected on a saline filled tank containing agar heart and lung phantoms are presented, and the results are compared to reconstructions by the NOSER algorithm on the same data.

Agar↗

PubMed2004

EIT reconstructions and Faddeev solutions for a numerically simulated phantom chest.

Electrical impedance tomography (EIT) uses measurements of electromagnetic fields to reconstruct the conductivity distribution inside of a body. As a medical imaging technique, EIT has many applications including cardio-pulmonary imaging, cranial imaging, and breast cancer detection. The D-bar method uses inverse scattering techniques and is based on a uniqueness proof in 2-D by Nachman [Ann. of Math.143 (1996)]. In this paper, the solution of the D-bar equation is tested on a model consisting of a numerical simulation of a human chest containing simulated skin, lungs and heart. Faddeev solutions are computed for this simulation and are compared to each other and their known asymptotic behavior.

Algorithms↗

PubMed2002

A direct reconstruction algorithm for electrical impedance tomography.

A direct (noniterative) reconstruction algorithm for electrical impedance tomography in the two-dimensional (2-D), cross-sectional geometry is reviewed. New results of a reconstruction of a numerically simulated phantom chest are presented. The algorithm is based on the mathematical uniqueness proof by A. I. Nachman [1996] for the 2-D inverse conductivity problem. In this geometry, several of the clinical applications include monitoring heart and lung function, diagnosis of pulmonary embolus, diagnosis of pulmonary edema, monitoring for internal bleeding, and the early detection of breast cancer.

Algorithms↗