Literature DB >> 33380765

The D-bar method for electrical impedance tomography-demystified.

J L Mueller1,2, S Siltanen1,2.   

Abstract

Electrical impedance tomography (EIT) is an imaging modality where a patient or object is probed using harmless electric currents. The currents are fed through electrodes placed on the surface of the target, and the data consists of voltages measured at the electrodes resulting from a linearly independent set of current injection patterns. EIT aims to recover the internal distribution of electrical conductivity inside the target. The inverse problem underlying the EIT image formation task is nonlinear and severely ill-posed, and hence sensitive to modeling errors and measurement noise. Therefore, the inversion process needs to be regularized. However, traditional variational regularization methods, based on optimization, often suffer from local minima because of nonlinearity. This is what makes regularized direct (non-iterative) methods attractive for EIT. The most developed direct EIT algorithm is the D-bar method, based on Complex Geometric Optics solutions and a nonlinear Fourier transform. Variants and recent developments of D-bar methods are reviewed, and their practical numerical implementation is explained.

Entities:  

Year:  2020        PMID: 33380765      PMCID: PMC7771826          DOI: 10.1088/1361-6420/aba2f5

Source DB:  PubMed          Journal:  Inverse Probl        ISSN: 0266-5611            Impact factor:   2.407


  17 in total

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

Authors:  David Isaacson; Jennifer L Mueller; Jonathan C Newell; Samuli Siltanen
Journal:  IEEE Trans Med Imaging       Date:  2004-07       Impact factor: 10.048

2.  An implementation of CalderOn's method for 3-D limited-view EIT.

Authors:  Gregory Boverman; Tzu-Jen Kao; David Isaacson; Gary J Saulnier
Journal:  IEEE Trans Med Imaging       Date:  2009-01-19       Impact factor: 10.048

3.  Deep D-Bar: Real-Time Electrical Impedance Tomography Imaging With Deep Neural Networks.

Authors:  Sarah Jane Hamilton; A Hauptmann
Journal:  IEEE Trans Med Imaging       Date:  2018-04-27       Impact factor: 10.048

4.  Comparing D-bar and common regularization-based methods for electrical impedance tomography.

Authors:  S J Hamilton; W R B Lionheart; A Adler
Journal:  Physiol Meas       Date:  2019-04-26       Impact factor: 2.833

5.  Direct EIT reconstructions of complex admittivities on a chest-shaped domain in 2-D.

Authors:  Sarah J Hamilton; Jennifer L Mueller
Journal:  IEEE Trans Med Imaging       Date:  2013-01-09       Impact factor: 10.048

6.  Estimating regions of air trapping from electrical impedance tomography data.

Authors:  Jennifer L Mueller; Peter Muller; Michelle Mellenthin; Rashmi Murthy; Michael Capps; Melody Alsaker; Robin Deterding; Scott D Sagel; Emily DeBoer
Journal:  Physiol Meas       Date:  2018-05-31       Impact factor: 2.833

7.  DYNAMIC OPTIMIZED PRIORS FOR D-BAR RECONSTRUCTIONS OF HUMAN VENTILATION USING ELECTRICAL IMPEDANCE TOMOGRAPHY.

Authors:  Melody Alsaker; Jennifer L Mueller; Rashmi Murthy
Journal:  J Comput Appl Math       Date:  2018-08-13       Impact factor: 2.621

8.  Direct 2-D reconstructions of conductivity and permittivity from EIT data on a human chest.

Authors:  Claudia N L Herrera; Miguel F M Vallejo; Jennifer L Mueller; Raul G Lima
Journal:  IEEE Trans Med Imaging       Date:  2014-09-04       Impact factor: 10.048

9.  A direct D-bar reconstruction algorithm for recovering a complex conductivity in 2-D.

Authors:  S J Hamilton; C N L Herrera; J L Mueller; A Von Herrmann
Journal:  Inverse Probl       Date:  2012-07-31       Impact factor: 2.407

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  5 in total

1.  Graph Convolutional Networks for Model-Based Learning in Nonlinear Inverse Problems.

Authors:  William Herzberg; Daniel B Rowe; Andreas Hauptmann; Sarah J Hamilton
Journal:  IEEE Trans Comput Imaging       Date:  2021-12-02

2.  Hybrid method for improving Tikhonov-based reconstruction quality in electrical impedance tomography.

Authors:  Meng Wang; Shuo Zheng; Yanyan Shi; Yajun Lou
Journal:  J Med Imaging (Bellingham)       Date:  2022-10-17

Review 3.  Robust imaging using electrical impedance tomography: review of current tools.

Authors:  Benoit Brazey; Yassine Haddab; Nabil Zemiti
Journal:  Proc Math Phys Eng Sci       Date:  2022-02-02       Impact factor: 2.704

4.  Improved resolution of D-bar images of ventilation using a Schur complement property and an anatomical atlas.

Authors:  Talles Batista Rattis Santos; Rafael Mikio Nakanishi; Erick Dario León Bueno de Camargo; Marcelo Brito Passos Amato; Jari P Kaipio; Raul Gonzalez Lima; Jennifer L Mueller
Journal:  Med Phys       Date:  2022-05-05       Impact factor: 4.506

5.  A Point-Matching Method of Moment with Sparse Bayesian Learning Applied and Evaluated in Dynamic Lung Electrical Impedance Tomography.

Authors:  Christos Dimas; Vassilis Alimisis; Nikolaos Uzunoglu; Paul P Sotiriadis
Journal:  Bioengineering (Basel)       Date:  2021-11-25
  5 in total

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