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Clinical Imaging of Microwave Mammography

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Interferogram-based breast tumor classification using microwave-induced thermoacoustic imaging.

Hao Nan, Benyamin Allahgholizadeh Haghi, Amin Arbabian

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|January 7, 2016

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This study introduces a robust breast tumor classification method using microwave-induced thermoacoustic (TA) imaging. By analyzing pressure wave interferograms, the technique enhances diagnostic accuracy for breast cancer screening.

Area of Science:

Medical Imaging
Biomedical Engineering
Diagnostic Technology

Background:

Microwave-induced thermoacoustic (TA) imaging merges microwave-based dielectric/conductivity contrast with ultrasound resolution.
TA imaging avoids ionizing radiation, making it ideal for frequent screening, such as for breast cancer.
Current signal-based classification methods in TA imaging can be sensitive to tumor location estimation errors.

Purpose of the Study:

To demonstrate breast tumor classification using TA imaging.
To investigate the sensitivity of signal-based classification to tumor location errors.
To propose and validate a more robust classification approach using pressure wave interferograms.

Main Methods:

Development of a breast tumor classification algorithm based on TA imaging data.

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Sensitivity analysis of the algorithm to variations in estimated tumor locations.

Implementation of a classification method utilizing the interferogram of received pressure waves.

Validation of the proposed method using a finite-difference time-domain (FDTD) simulation framework.

Main Results:

The study demonstrates the feasibility of breast tumor classification using TA imaging.
Signal-based classification algorithms show sensitivity to inaccuracies in tumor localization.
The proposed interferogram-based feature basis significantly reduces sensitivity to location errors, enhancing robustness.

Conclusions:

Breast tumor classification is achievable with microwave-induced thermoacoustic imaging.
Interferogram analysis offers a more robust feature for classification compared to direct signal analysis.
This approach improves the reliability of TA imaging for breast cancer screening applications.