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Terahertz Imaging and Characterization Protocol for Freshly Excised Breast Cancer Tumors

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Bio-optics based sensation imaging for breast tumor detection using tissue characterization.

Jong-Ha Lee¹, Yoon Nyun Kim², Hee-Jun Park³

¹Department of Biomedical Engineering, School of Medicine, Keimyung University, 1095, Dalgubeol-daero, Daegu 704-701, Korea. segeberg@gmail.com.

Sensors (Basel, Switzerland)

|March 19, 2015

Summary

This study introduces a novel method using tactile imaging and artificial neural networks to accurately estimate tissue stiffness and size. This technique shows promise for early breast cancer screening and diagnosis.

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Area of Science:

Biomedical Engineering
Medical Imaging
Computational Mechanics

Background:

Accurate characterization of tissue mechanical properties is crucial for disease diagnosis.
Existing methods for tissue parameter estimation can be invasive or lack precision.
Optical tactile sensation imaging systems offer a non-invasive approach to gather surface data.

Purpose of the Study:

To develop and validate a method for estimating the stiffness and geometric parameters of tissue inclusions.
To leverage tactile data from an optical tactile sensation imaging system (TSIS) for parameter estimation.
To assess the potential of this method for breast cancer screening and diagnosis.

Main Methods:

Utilized finite element modeling (FEM) to create a forward algorithm predicting tactile data based on tissue mechanical properties.
Developed an inversion algorithm, employing artificial neural networks (ANN), to extract inclusion size, depth, and Young's modulus from tactile data.
Validated the method using a realistic tissue phantom containing stiff inclusions.

Main Results:

The proposed method accurately estimated tissue inclusion parameters.
Relative errors for size, depth, and Young's modulus were 0.58%, 3.82%, and 2.51%, respectively.
Experimental validation demonstrated the method's precision and reliability.

Conclusions:

The developed tissue inclusion parameter estimation method is effective and accurate.
The technique shows significant potential as a non-invasive screening and diagnostic tool.
This approach could enhance early detection capabilities, particularly for breast cancer.