3D microwave tomography of the breast using prior anatomical information

Authors

  • Golnabi Amir H.,

    1. Department of Mathematical Sciences, Montclair State University, Montclair, New Jersey 07043
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  • Meaney Paul M.,

    1. Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire 03755
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  • Paulsen Keith D.

    1. Thayer 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
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Abstract

Purpose:

The 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.

Methods:

Previous 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.

Results:

Image 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.

Conclusions:

The 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.

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