Microwave Imaging and Scattering Analysis
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Microwave breast imaging has strong potential as an adjunct or alternative to current breast imaging modalities, especially for dense breast tissue and in resource-limited settings, and the growing role of machine learning in enhancing image resolution, segmentation, and lesion classification is highlighted.
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Breast cancer is a leading cause of morbidity and mortality among women worldwide, and the limitations of conventional imaging, especially in women with dense breast tissue have sparked interest in safer, more accessible diagnostic alternatives. Microwave breast imaging (MBI) is a promising, non-ionizing, and potentially cost-effective modality that leverages dielectric contrasts between malignant and healthy tissues. This review presents MBI technologies, focusing on microwave tomography and radar-based techniques such as confocal microwave imaging and tissue-sensing adaptive radar. We evaluate system designs, antenna configurations, image reconstruction algorithms, and clinical performance from leading research groups. Additionally, we highlight the growing role of machine learning in enhancing image resolution, segmentation, and lesion classification. Despite encouraging results from early-phase clinical trials, challenges such as limited spatial resolution, anatomical variability, and high computational demands continue to hinder broader adoption. We outline future directions, including real-time motion correction, improved antenna sensitivity, and stronger clinical validation. With continued innovation and regulatory support, MBI has strong potential as an adjunct or alternative to current breast imaging modalities, especially for dense breast tissue and in resource-limited settings.
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@article{Akesson2026Microwave,
title = {Microwave breast imaging: A review of clinical potential and technological advances},
author = {Isabella Akesson and Richard Kovac and HyunA Son and Ana Claudia Teixeira de Castro Gonçalves Ortega and Daniil Fedorov and Arosh S Perera Molligoda Arachchige},
journal = {World Journal of Methodology},
year = {2026},
doi = {10.5662/wjm.v16.i3.110991},
url = {https://doi.org/10.5662/wjm.v16.i3.110991}
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