Incorporating a Spatial Prior into Nonlinear D-Bar EIT Imaging for Complex Admittivities

• Published in: IEEE transactions on medical imaging Vol. 36; no. 2; pp. 457 - 466
• Main Authors: Hamilton, Sarah J., Mueller, J. L., Alsaker, M.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-02-2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; biomedical imaging; Computer simulation; Conductivity; Electric Impedance; Electrical impedance; electrical impedance tomography; Electrical measurement; Electrical resistivity; Electrodes; Fourier transforms; Ill posed problems; Image reconstruction; Inverse problems; Low pass filters; Lungs; Mathematical model; Medical imaging; Noise; Noise measurement; Noise sensitivity; Pathology; Permittivity; Phantoms, Imaging; Pleural effusion; Pneumothorax; Reconstruction; Regularization; Scattering; Tomography
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2016.2613511

Electrical Impedance Tomography (EIT) aims to recover the internal conductivity and permittivity distributions of a body from electrical measurements taken on electrodes on the surface of the body. The reconstruction task is a severely ill-posed nonlinear inverse problem that is highly sensitive to measurement noise and modeling errors. Regularized D-bar methods have shown great promise in producing noise-robust algorithms by employing a low-pass filtering of nonlinear (nonphysical) Fourier transform data specific to the EIT problem. Including prior data with the approximate locations of major organ boundaries in the scattering transform provides a means of extending the radius of the low-pass filter to include higher frequency components in the reconstruction, in particular, features that are known with high confidence. This information is additionally included in the system of D-bar equations with an independent regularization parameter from that of the extended scattering transform. In this paper, this approach is used in the 2-D D-bar method for admittivity (conductivity as well as permittivity) EIT imaging. Noise-robust reconstructions are presented for simulated EIT data on chest-shaped phantoms with a simulated pneumothorax and pleural effusion. No assumption of the pathology is used in the construction of the prior, yet the method still produces significant enhancements of the underlying pathology (pneumothorax or pleural effusion) even in the presence of strong noise.