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Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

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Discrete Dipole Approximation-Based Microwave Tomography for Fast Breast Cancer Imaging.

Samar Hosseinzadegan¹, Andreas Fhager¹, Mikael Persson¹

¹Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden.

IEEE Transactions on Microwave Theory and Techniques

|June 28, 2021

Summary

This summary is machine-generated.

A new microwave tomography algorithm uses the discrete dipole approximation for faster image reconstruction. This method significantly reduces processing time and memory, improving upon previous techniques for microwave imaging.

Keywords:

Breast imaging Jacobian matrix computational efficiency discrete dipole approximation (DDA)microwave tomography

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Area of Science:

Electromagnetics and Applied Physics
Biomedical Engineering
Computational Imaging

Background:

Microwave tomography is a promising imaging modality, but reconstruction speed has been a limitation.
Existing algorithms often require substantial computational resources and time.
The discrete dipole approximation (DDA) offers a potential solution for efficient forward modeling in microwave tomography.

Purpose of the Study:

To develop and validate a fast microwave tomography reconstruction algorithm.
To leverage the two-dimensional discrete dipole approximation (2D-DDA) as an efficient forward solver.
To demonstrate the algorithm's capability in reconstructing images from both synthetic and experimental data.

Main Methods:

The algorithm employs the 2D-DDA for forward modeling, suitable for low-profile antennas and lossy media.
A microwave measurement system with 16 monopole antennas and a vector network analyzer was utilized.
The nodal adjoint method was used for efficient Jacobian matrix computation, enabling rapid image reconstruction.

Main Results:

The algorithm successfully reconstructed a 2D plane of a cylindrical phantom using both synthetic and experimental data.
Image reconstruction was achieved in under 6 seconds, demonstrating significant time savings.
The 2D-DDA approach proved effective given the low-profile antennas and lossy coupling liquid.

Conclusions:

The developed microwave tomography algorithm offers a substantial improvement in reconstruction speed and efficiency.
The combination of 2D-DDA and nodal adjoint method is highly effective for fast microwave imaging.
This approach presents a significant advancement over previous microwave tomography implementations, reducing time and memory requirements.