Evidence map›Paper›PMID 27036589›Full record

ArticleMedical physics2016

3D microwave tomography of the breast using prior anatomical information.

Amir H Golnabi, Paul M Meaney, Keith D Paulsen

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Article in Medical physics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

Amir H GolnabiDepartment of Mathematical Sciences, Montclair State University, Montclair, New Jersey 07043.
Paul M MeaneyThayer School of Engineering, Dartmouth College, Hanover, New Hampshire 03755.
Keith D PaulsenThayer School of Engineering, Dartmouth College, Hanover, New Hampshire 03755; Department of Radiology, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire 03755; Norris Cotton Cancer Center, Dartmouth Hitchcock Medical Center, Lebanon, New Hampshire 03756; and Advanced Surgical Center, Dartmouth Hitchcock Medical Center, Lebanon, New Hampshire 03756.

Funding

Microwave Imaging for Neoadjuvant Chemotherapy MonitoringR01CA191227 · NCI · DARTMOUTH COLLEGE · PI MEANEY, PAUL M · 2015 to 2019
$3.6M
NCI NIH HHS R01 CA191227NCI NIH HHS R01-CA191227-01
6 · The paper itself

Abstract

purposeThe authors have developed a new 3D breast image reconstruction technique that utilizes the soft tissue spatial resolution of magnetic resonance imaging (MRI) and integrates the dielectric property differentiation from microwave imaging to produce a dual modality approach with the goal of augmenting the specificity of MR imaging, possibly without the need for nonspecific contrast agents. The integration is performed through the application of a soft prior regularization which imports segmented geometric meshes generated from MR exams and uses it to constrain the microwave tomography algorithm to recover nearly uniform property distributions within segmented regions with sharp delineation between these internal subzones.

methodsPrevious investigations have demonstrated that this approach is effective in 2D simulation and phantom experiments and also in clinical exams. The current study extends the algorithm to 3D and provides a thorough analysis of the sensitivity and robustness to misalignment errors in size and location between the spatial prior information and the actual data.

resultsImage results in 3D were not strongly dependent on reconstruction mesh density, and the changes of less than 30% in recovered property values arose from variations of more than 125% in target region size-an outcome which was more robust than in 2D. Similarly, changes of less than 13% occurred in the 3D image results from variations in target location of nearly 90% of the inclusion size. Permittivity and conductivity errors were about 5 times and 2 times smaller, respectively, with the 3D spatial prior algorithm in actual phantom experiments than those which occurred without priors.

conclusionsThe presented study confirms that the incorporation of structural information in the form of a soft constraint can considerably improve the accuracy of the property estimates in predefined regions of interest. These findings are encouraging and establish a strong foundation for using the soft prior technique in clinical studies, where their microwave imaging system and MRI can simultaneously collect breast exam data in patients.

Indexed as

MicrowavesAlgorithmsBreastImaging, Three-DimensionalMagnetic Resonance ImagingPhantoms, ImagingTomography

Identifiers

PMID27036589
PMCPMC4808070

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.