Microwave breast imaging reconstruction

In this thesis I analyzed the microwave tomography method to recognize breast can- cer. I study how identify the dielectric permittivity, the Helmoltz equation parameter used to model the real physic problem. Through a non linear least squares method I solve a problem of parameters identification; I show the theoric approach and the devel- opment to reach the results. I use the Levenberg-Marquardt algorithm, applied on COMSOL software to multiphysic models; so I do numerical proofs on semplified test problems compared to the specific real problem to solve.

Performance analysis of reconstruction algorithms for breast microwave imaging

The problem of localizing a scatterer, which represents a tumor, in a homogeneous circular domain, which represents a breast, is addressed. A breast imaging method based on microwaves is considered. The microwave imaging involves to several techniques for detecting, localizing and characterizing tumors in breast tissues. In all such methods an electromagnetic inverse scattering problem exists. For the scattering detection method, an algorithm based on a linear procedure solution, inspired by MUltiple SIgnal Classification algorithm (MUSIC) and Time Reversal method (TR), is implemented. The algorithm returns a reconstructed image of the investigation domain in which it is detected the scatterer position. This image is called pseudospectrum. A preliminary performance analysis of the algorithm vying the working frequency is performed: the resolution and the signal-to-noise ratio of the pseudospectra are improved if a multi-frequency approach is considered. The Geometrical Mean-MUSIC algorithm (GM- MUSIC) is proposed as multi-frequency method. The performance of the GMMUSIC is tested in different real life computer simulations. The performed analysis shows that the algorithm detects the scatterer until the electrical parameters of the breast are known. This is an evident limit, since, in a real life situation, the anatomy of the breast is unknown. An improvement in GM-MUSIC is proposed: the Eye-GMMUSIC algorithm. Eye-GMMUSIC algorithm needs no a priori information on the electrical parameters of the breast. It is an optimizing algorithm based on the pattern search algorithm: it searches the breast parameters which minimize the Signal-to-Clutter Mean Ratio (SCMR) in the signal. Finally, the GM-MUSIC and the Eye-GMMUSIC algorithms are tested on a microwave breast cancer detection system consisting of an dipole antenna, a Vector Network Analyzer and a novel breast phantom built at University of Bologna. The reconstruction of the experimental data confirm the GM-MUSIC ability to localize a scatterer in a homogeneous medium.

Tomographic 3D reconstruction through linear inversion for breast microwave imaging

In the present thesis we address the problem of detecting and localizing a small spherical target with characteristic electrical properties inside a volume of cylindrical shape, representing female breast, with MWI. One of the main works of this project is to properly extend the existing linear inversion algorithm from planar slice to volume reconstruction; results obtained, under the same conditions and experimental setup are reported for the two different approaches. Preliminar comparison and performance analysis of the reconstruction algorithms is performed via numerical simulations in a software-created environment: a single dipole antenna is used for illuminating the virtual breast phantom from different positions and, for each position, the corresponding scattered field value is registered. Collected data are then exploited in order to reconstruct the investigation domain, along with the scatterer position, in the form of image called pseudospectrum. During this process the tumor is modeled as a dielectric sphere of small radius and, for electromagnetic scattering purposes, it's treated as a point-like source. To improve the performance of reconstruction technique, we repeat the acquisition for a number of frequencies in a given range: the different pseudospectra, reconstructed from single frequency data, are incoherently combined with MUltiple SIgnal Classification (MUSIC) method which returns an overall enhanced image. We exploit multi-frequency approach to test the performance of 3D linear inversion reconstruction algorithm while varying the source position inside the phantom and the height of antenna plane. Analysis results and reconstructed images are then reported. Finally, we perform 3D reconstruction from experimental data gathered with the acquisition system in the microwave laboratory at DIFA, University of Bologna for a recently developed breast-phantom prototype; obtained pseudospectrum and performance analysis for the real model are reported.

Experimental set up and characterization of a prototype for breast microwave imaging

Nella presente tesi è stato sviluppato un sistema di acquisizione automatico finalizzato allo studio del breast microwave imaging. Le misure sono state eseguite in configurazione monostatica, in cui viene acquisito un segnale da diverse posizioni lungo il perimetro dell’area di indagine. A questo scopo, è stato installato un motore ad alta precisione che permette la rotazione del fantoccio e l’esecuzione automatica delle misure da un numero di posizioni fissato. Per automatizzare il processo di acquisizione, è stato inoltre sviluppato appositamente un software in ambiente LabView. Successivamente, è stata eseguita una intensa sessione di misure finalizzate alla caratterizzazione del sistema sviluppato al variare delle condizioni di misura. Abbiamo quindi utilizzato dei fantocci di tumore di diverse dimensioni e permittività elettrica per studiare la sensibilità della strumentazione in condizione di mezzo omogeneo. Dall’analisi delle ricostruzioni multifrequenza effettuate tramite diversi algoritmi di tipo TR-MUSIC sul range di frequenze selezionato, abbiamo notato che il tumore è ricostruito correttamente in tutti gli scenari testati. Inoltre, abbiamo creato un ulteriore fantoccio per simulare la presenza di una disomogeneità nel dominio di imaging. In questo caso, abbiamo studiato le performances del sistema di acquisizione al variare della posizione del tumore, le cui caratteristiche sono state fissate, e della permittività associata al fantoccio. Dall’analisi dei risultati appare che le performances di ricostruzione sono condizionate dalla presenza della disomogeneità, in modo particolare se il tumore è posizionato all’interno di essa. Infine, abbiamo studiato delle performance di due algoritmi di ricostruzione 3D: uno di essi è basato sulla sovrappo- sizione tomografica e sfrutta metodi di interpolazione, l’altro si basa sull’utilizzo di un propagatore 3D per il dipolo Hertziano in approssimazione scalare.