Potential benefits of using a priori data in reconstruction algorithms for microwave tomography
Conference poster, 2006

Microwave tomography is rapidly developing into a promising imaging technique that could be useful in many different applications where a non-invasive detection of internal dielectric properties is required. Such a technique could for instance be useful in mammographical imaging for screening and follow up examinations of breast cancer tumours. Compared to x-ray mammography there is a potential significant benefit in using microwaves in the diagnosis of breast cancer tumours due to a significant contrast in the dielectric properties between tumour and surrounding tissue. Development of an efficient imaging reconstruction algorithm is a big challenge in the construction of an operational microwave tomograph. The algorithm has to be fast, accurate and robust. In this work results are presented showing how a priori dielectric data of the objects being reconstructed can be exploited to improve the convergence rate and the resolution of the reconstructed image. The work is based on a conjugate-gradient reconstruction algorithm and FDTD modelling of the electromagnetic wave propagation. For this work it has been modified to search for circular objects with given permittivity and conductivity. The results are promising and indicate that significantly improved reconstructions can be obtained. However to be practically viable further development of the algorithm need to be made in order to make it more robust and adaptive. Once that is made we plan to use this technique in a prototype that will be evaluated for clinical breast cancer imaging.

Author

Andreas Fhager

Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering

Parham Hashemzadeh

Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering

Mikael Persson

Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering

Medicinteknikdagarna, 3-4 October 2006, Uppsala, Sweden

Subject Categories

Computational Mathematics

Biophysics

More information

Created

10/6/2017