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Clinical Imaging of Microwave Mammography

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Optimum receiver array design for magnetic induction tomography.

Doga Gürsoy¹, Hermann Scharfetter

¹Institute of Medical Engineering, Graz University of Technology, Graz 8010, Austria. guersoy@tugraz.at

IEEE Transactions on Bio-Medical Engineering

|February 11, 2009

Summary

This study introduces an algorithm for optimizing receiver array designs in magnetic induction tomography (MIT). The method improves image resolution by iteratively selecting optimal receiver locations for better conductivity mapping.

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Area of Science:

Biomedical Engineering
Medical Imaging
Electrical Engineering

Background:

Magnetic induction tomography (MIT) is an imaging technique for mapping internal electrical conductivity.
The arrangement of receiver coils significantly impacts MIT image reconstruction quality.
Current MIT designs may not achieve optimal resolution or information content.

Purpose of the Study:

To develop and evaluate a fast deterministic algorithm for optimizing receiver array designs in MIT.
To enhance image resolution, particularly in specific regions of interest.
To compare the performance of optimized designs against conventional geometries.

Main Methods:

A deterministic algorithm iteratively excludes suboptimal receiver locations to identify optimal array designs.
The algorithm was applied to design regionally focused MIT arrays for improved resolution.
Eigenvalue analysis of the Hessian matrix was used to assess information content.

Main Results:

The developed algorithm successfully generated optimized receiver array designs for MIT.
Regionally focused designs demonstrated increased image resolution in targeted areas.
Optimized designs yielded improved reconstructed images compared to conventional geometries.
The algorithm maintained consistent conductivity information content across receivers.

Conclusions:

The fast deterministic algorithm is practically usable for designing optimal receiver arrays in MIT.
Optimized designs offer enhanced image resolution and quality.
The method provides a viable approach for improving MIT experimental setups.