An Effective Framework for Deep-Learning-Enhanced Quantitative Microwave Imaging and Its Potential for Medical Applications

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 2; p. 643
• Main Authors: Yago Ruiz, Álvaro, Cavagnaro, Marta, Crocco, Lorenzo
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 06.01.2023; MDPI
• Subjects: Algorithms; Alzheimer's disease; convolutional neural network; Deep Learning; Diagnostic Imaging; Dielectric properties; Image contrast; Image Processing, Computer-Assisted - methods; Imaging techniques; Inverse scattering; Machine learning; Medical imaging; Medical screening; Microwave Imaging; Microwaves; Neural networks; Neural Networks, Computer; Orthogonality; orthogonality sampling method; Physics; U-Net; deep learning; inverse scattering; microwave imaging; orthogonality sampling method; U-Net; convolutional neural network
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23020643

Microwave imaging is emerging as an alternative modality to conventional medical diagnostics technologies. However, its adoption is hindered by the intrinsic difficulties faced in the solution of the underlying inverse scattering problem, namely non-linearity and ill-posedness. In this paper, an innovative approach for a reliable and automated solution of the inverse scattering problem is presented, which combines a qualitative imaging technique and deep learning in a two-step framework. In the first step, the orthogonality sampling method is employed to process measurements of the scattered field into an image, which explicitly provides an estimate of the targets shapes and implicitly encodes information in their contrast values. In the second step, the images obtained in the previous step are fed into a neural network (U-Net), whose duty is retrieving the exact shape of the target and its contrast value. This task is cast as an image segmentation one, where each pixel is classified into a discrete set of permittivity values within a given range. The use of a reduced number of possible permittivities facilitates the training stage by limiting its scope. The approach was tested with synthetic data and validated with experimental data taken from the Fresnel database to allow a fair comparison with the literature. Finally, its potential for biomedical imaging is demonstrated with a numerical example related to microwave brain stroke diagnosis.