MammoWave is a microwave imaging device for breast lesions detection, which operates using two (azimuthally rotating) antennas without any matching liquid. Images, subsequently obtained by resorting to Huygens Principle, are intensity maps, representing the homogeneity of tissues’ dielectric properties. In this paper, we propose to generate, for each breast, a set of conductivity weighted microwave images by using different values of conductivity in the Huygens Principle imaging algorithm. Next, microwave images’ parameters, i.e. features, are introduced to quantify the non-homogenous behaviour of the image. We empirically verify on 103 breasts that a selection of these features may allow distinction between breasts with no radiological finding (NF) and breasts with radiological findings (WF), i.e. with lesions which may be benign or malignant. Statistical significance was set at p<0.05. We obtained single features Area Under the receiver operating characteristic Curves (AUCs) spanning from 0.65 to 0.69. In addition, an empirical rule-of-thumb allowing breast assessment is introduced using a binary score S operating on an appropriate combination of features. Performances of such rule-of-thumb are evaluated empirically, obtaining a sensitivity of 74%, which increases to 82% when considering dense breasts only.

Breast lesion detection employing state of the art microwave systems provide a safe, non-ionizing technique that can differentiate healthy and non-healthy tissues by exploiting their dielectric properties. In this paper, a microwave apparatus for breast lesion detection is used to accumulate clinical data from subjects undergoing breast examinations at the Department of Diagnostic Imaging, Perugia Hospital, Perugia, Italy. This paper presents the first ever clinical demonstration and comparison of a microwave ultra-wideband (UWB) device augmented by machine learning with subjects who are simultaneously undergoing conventional breast examinations. Non-ionizing microwave signals are transmitted through the breast tissue and the scattering parameters (S-parameter) are received via a dedicated moving transmitting and receiving antenna set-up. The output of a parallel radiologist study for the same subjects, performed using conventional techniques, is taken to pre-process microwave data and create suitable data for the machine intelligence system. These data are used to train and investigate several suitable supervised machine learning algorithms nearest neighbour (NN), multi-layer perceptron (MLP) neural network, and support vector machine (SVM) to create an intelligent classification system towards supporting clinicians to recognise breasts with lesions. The results are rigorously analysed, validated through statistical measurements, and found the quadratic kernel of SVM can classify the breast data with 98% accuracy.

A novel technique for imaging based on ultrawideband (UWB) microwave signals is introduced. Specifically, the procedure deals with the Huygens Principle (HP); using HP to forward-propagate the waves removes the need to solve inverse problems and, consequently, no matrix generation/inversion is required. Together with its simplicity, the methodology permits the capture of contrast-the extent to which different media can be discriminated in the final image. Moreover, UWB allows all the information in the frequency domain to be utilized by combining the information from the individual frequencies to construct a consistent image. It follows that the methodology can identify the presence and location of significant scatterers inside a volume. Validation of the technique through both simulations and measurements on cylinder and spheres with inclusions has been performed. Application of the proposed technique to medical imaging is envisaged.

A fundamental step in ultrawideband (UWB) communication system design involves the characterization of the indoor propagation channel. In this paper, we show that the UWB propagation channel parameters can be accurately predicted by employing ray tracing (RT) simulation carried out at various frequencies over the signal bandwidth. It is important to note that the determination of the rays reaching a given location is made only once, as the RT algorithm is independent of frequency. A parallel ray approximation (PRA) is used to significantly improve the computational efficiency of the RT based method. Moreover the accuracy of the approximation is verified through a measurement campaign.