Improving Conductivity Image Quality Using Block Matrix-based Multiple Regularization (BMMR) Technique in EIT: A Simulation Study

Abstract A Block Matrix based Multiple Regularization (BMMR) technique is proposed for improving conductivity image quality in Electrical Impedance Tomography (EIT). The response matrix (JTJ) has been partitioned into several sub-block matrices and the largest element of each sub-block matrix has been chosen as regularization parameter for the nodes contained by that sub-block. Simulated boundary data are generated for circular domains with circular inhomogeneities of different geometry and the conductivity images are reconstructed in a Model Based Iterative Image Reconstruction (MoBIIR) algorithm. Conductivity images are reconstructed with BMMR technique and the results are compared with the Single-step Tikhonov Regularization (STR) and modified Levenberg-Marquardt Regularization (LMR) methods. Results show that the BMMR technique improves the impedance image and its spatial resolution for single and multiple inhomogeneity phantoms of different geometries. It is observed that the BMMR technique reduces the projection error as well as the solution error and improves the conductivity reconstruction in EIT. Results also show that the BMMR method improves the image contrast and inhomogeneity conductivity profile by reducing background noise for all the phantom configurations.

[1]  Philippe Renaud,et al.  Cell Culture Imaging Using Microimpedance Tomography , 2008, IEEE Transactions on Biomedical Engineering.

[2]  S. K. Biswas,et al.  Regional sub-block matrices based multiple regularization and biomedical image reconstruction , 2010, 2010 International Conference on Systems in Medicine and Biology.

[3]  T. Hope,et al.  Technology review: The use of electrical impedance scanning in the detection of breast cancer , 2003, Breast Cancer Research.

[4]  Tushar Kanti Bera,et al.  A FEM-Based Forward Solver for Studying the Forward Problem of Electrical Impedance Tomography (EIT) with A Practical Biological Phantom , 2009, 2009 IEEE International Advance Computing Conference.

[5]  Tushar Kanti Bera,et al.  A Simple instrumentation calibration technique for Electrical Impedance Tomography (EIT) using a 16-electrode phantom , 2009, 2009 IEEE International Conference on Automation Science and Engineering.

[6]  Tushar Kanti Bera,et al.  Resistivity imaging of a reconfigurable phantom with circular inhomogeneities in 2D-electrical impedance tomography , 2011 .

[7]  J P Kaipio,et al.  Assessment of errors in static electrical impedance tomography with adjacent and trigonometric current patterns. , 1997, Physiological measurement.

[8]  B. M. Graham,et al.  Enhancements in Electrical Impedance Tomography (EIT) image reconstruction for three-dimensional lung imaging , 2007 .

[9]  C. J. Grootveld.  Measuring & modeling of concentrated settling suspensions using electrical impedance tomography , 1996 .

[10]  Tianzi Jiang,et al.  Improving image quality of diffuse optical tomography with a projection-error-based adaptive regularization method. , 2008, Optics express.

[11]  D S Holder,et al.  Some practical biological phantoms for calibrating multifrequency electrical impedance tomography. , 1996, Physiological measurement.

[12]  Emery N. Brown,et al.  Assessment of Autonomic Control and Respiratory Sinus Arrhythmia Using Point Process Models of Human Heart Beat Dynamics , 2009, IEEE Transactions on Biomedical Engineering.

[13]  S. Arridge.  Optical tomography in medical imaging , 1999 .

[14]  Min Chan Kim,et al.  Regularization methods in electrical impedance tomography technique for the two-phase flow visualization , 2001 .

[15]  J. Strzelczyk.  The Essential Physics of Medical Imaging , 2003 .

[16]  Tushar Kanti Bera,et al.  Studying the Boundary Data Profile of a Practical Phantom for Medical Electrical Impedance Tomography with Different Electrode Geometries , 2009 .

[17]  L Tarassenko,et al.  Impedance imaging in the newborn. , 1987, Clinical physics and physiological measurement : an official journal of the Hospital Physicists' Association, Deutsche Gesellschaft fur Medizinische Physik and the European Federation of Organisations for Medical Physics.

[18]  S. K. Biswas,et al.  Diffuse optical tomographic imager using a single light source , 2009 .

[19]  Tushar Kanti Bera,et al.  Improving Image Quality in Electrical Impedance Tomography (EIT) Using Projection Error Propagation-Based Regularization (PEPR) Technique: A Simulation Study , 2011 .

[20]  Hung-Yuan Chung,et al.  The Scanning Data Collection Strategy for Enhancing the Quality of Electrical Impedance Tomography , 2008, IEEE Transactions on Instrumentation and Measurement.

[21]  William R B Lionheart.  EIT reconstruction algorithms: pitfalls, challenges and recent developments. , 2004, Physiological measurement.

[22]  K D Paulsen,et al.  Contrast-detail analysis for detection and characterization with near-infrared diffuse tomography. , 2000, Medical physics.

[23]  D. Djajaputra.  Electrical Impedance Tomography: Methods, History and Applications , 2005 .

[24]  Thomas J. Yorkey,et al.  Comparing reconstruction methods for electrical impedance tomography , 1987, Annals of Biomedical Engineering.

[25]  Nathan Ida,et al.  Introduction to the Finite Element Method , 1997 .

[26]  Tushar Kanti Bera,et al.  A study of practical biological phantoms with simple instrumentation for Electrical Impedance Tomography (EIT) , 2009, 2009 IEEE Instrumentation and Measurement Technology Conference.

[27]  R M Heethaar,et al.  Improvement of cardiac imaging in electrical impedance tomography by means of a new electrode configuration. , 1996, Physiological measurement.

[28]  José Hinz,et al.  Regional ventilation by electrical impedance tomography: a comparison with ventilation scintigraphy in pigs. , 2003, Chest.

[29]  D S Holder,et al.  An electrode addressing protocol for imaging brain function with electrical impedance tomography using a 16-channel semi-parallel system , 2009, Physiological measurement.

[30]  Brian H. Brown,et al.  Medical impedance tomography and process impedance tomography: a brief review , 2001 .

[31]  David Barber,et al.  Three dimensional electrical impedance tomography of the human thorax , 1996, Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[32]  C. J. Kotre,et al.  Subsurface electrical impedance imaging: measurement strategy, image reconstruction and in vivo results. , 1996, Physiological measurement.

[33]  Manuchehr Soleimani,et al.  Nonlinear image reconstruction for electrical capacitance tomography using experimental data , 2005 .

[34]  R H Bayford,et al.  Bioimpedance tomography (electrical impedance tomography). , 2006, Annual review of biomedical engineering.

[35]  Richard H. Bayford,et al.  Electrical impedance tomography of human brain function using reconstruction algorithms based on the finite element method , 2003, NeuroImage.

[36]  J. Rosell,et al.  Multi-frequency static imaging in electrical impedance tomography: Part 1 instrumentation requirements , 1995, Medical and Biological Engineering and Computing.

[37]  John G. Webster,et al.  Electrical Impedance Tomography , 1991 .

[38]  F. J. Dickin,et al.  Low common mode error data collection strategy for electrical resistance tomography , 1998 .

[39]  Mi Wang,et al.  Electrical resistance tomography for process applications , 1996 .

[40]  Andy Adler,et al.  Electrical impedance tomography: regularized imaging and contrast detection , 1996, IEEE Trans. Medical Imaging.

[41]  Denyer Cwl.  Electronics for real-time and three-dimensional electrical impedance tomographs. , 1996 .

[42]  Y. L. Mo,et al.  Increasing image resolution in electrical impedance tomography , 2002 .