3-D Microwave Tomography Using the Soft Prior Regularization Technique: Evaluation in Anatomically Realistic MRI-Derived Numerical Breast Phantoms

Amir Golnabi, Paul M. Meaney, Shireen D. Geimer, Keith D. Paulsen

Research output: Contribution to journalArticle

Abstract

OBJECTIVE: Fusion of magnetic resonance imaging (MRI) breast images with microwave tomography is accomplished through a soft prior technique, which incorporates spatial information (from MRI), i.e., accurate boundary location of different regions of interest, into the regularization process of the microwave image reconstruction algorithm. METHODS: Numerical experiments were completed on a set of three-dimensional (3-D) breast geometries derived from MR breast data with different parenchymal densities, as well as a simulated tumor to evaluate the performance over a range of breast shapes, sizes, and property distributions. RESULTS: When the soft prior regularization technique was applied, both permittivity and conductivity relative root mean square error values decreased by more than 87% across all breast densities, except in two cases where the error decrease was only 55% and 78%. In addition, the incorporation of structural priors increased contrast between tumor and fibroglandular tissue by 59% in permittivity and 192% in conductivity. CONCLUSION: This study confirmed that the soft prior algorithm is robust in 3-D and can function successfully across a range of complex geometries and tissue property distributions. SIGNIFICANCE: This study demonstrates that our microwave tomography is capable of recovering accurate tissue property distributions when spatial information from MRI is incorporated through soft prior regularization.

Original languageEnglish
Pages (from-to)2566-2575
Number of pages10
JournalIEEE transactions on bio-medical engineering
Volume66
Issue number9
DOIs
StatePublished - 1 Sep 2019

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Magnetic resonance
Tomography
Microwaves
Tissue
Imaging techniques
Tumors
Permittivity
Geometry
Image reconstruction
Mean square error
Spatial distribution
Fusion reactions
Experiments

Cite this

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title = "3-D Microwave Tomography Using the Soft Prior Regularization Technique: Evaluation in Anatomically Realistic MRI-Derived Numerical Breast Phantoms",
abstract = "OBJECTIVE: Fusion of magnetic resonance imaging (MRI) breast images with microwave tomography is accomplished through a soft prior technique, which incorporates spatial information (from MRI), i.e., accurate boundary location of different regions of interest, into the regularization process of the microwave image reconstruction algorithm. METHODS: Numerical experiments were completed on a set of three-dimensional (3-D) breast geometries derived from MR breast data with different parenchymal densities, as well as a simulated tumor to evaluate the performance over a range of breast shapes, sizes, and property distributions. RESULTS: When the soft prior regularization technique was applied, both permittivity and conductivity relative root mean square error values decreased by more than 87{\%} across all breast densities, except in two cases where the error decrease was only 55{\%} and 78{\%}. In addition, the incorporation of structural priors increased contrast between tumor and fibroglandular tissue by 59{\%} in permittivity and 192{\%} in conductivity. CONCLUSION: This study confirmed that the soft prior algorithm is robust in 3-D and can function successfully across a range of complex geometries and tissue property distributions. SIGNIFICANCE: This study demonstrates that our microwave tomography is capable of recovering accurate tissue property distributions when spatial information from MRI is incorporated through soft prior regularization.",
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3-D Microwave Tomography Using the Soft Prior Regularization Technique : Evaluation in Anatomically Realistic MRI-Derived Numerical Breast Phantoms. / Golnabi, Amir; Meaney, Paul M.; Geimer, Shireen D.; Paulsen, Keith D.

In: IEEE transactions on bio-medical engineering, Vol. 66, No. 9, 01.09.2019, p. 2566-2575.

Research output: Contribution to journalArticle

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