An experimental system for detection and localization of hemorrhage using ultra-wideband microwaves with deep learning

TL;DR

This paper presents an experimental system using ultra-wideband microwaves and deep learning to detect and localize brain hemorrhages, demonstrating high accuracy and potential for rapid, low-cost stroke diagnosis.

Contribution

It introduces a novel experimental framework with a robotic system, UWB antenna array, and deep neural network for microwave-based hemorrhage detection and localization.

Findings

Detection sensitivity and specificity >0.99

Localization error of 1.65 mm

Feasibility of microwave stroke detection demonstrated

Abstract

Stroke is a leading cause of mortality and disability. Emergent diagnosis and intervention are critical, and predicated upon initial brain imaging; however, existing clinical imaging modalities are generally costly, immobile, and demand highly specialized operation and interpretation. Low-energy microwaves have been explored as low-cost, small form factor, fast, and safe probes of tissue dielectric properties, with both imaging and diagnostic potential. Nevertheless, challenges inherent to microwave reconstruction have impeded progress, hence microwave imaging (MWI) remains an elusive scientific aim. Herein, we introduce a dedicated experimental framework comprising a robotic navigation system to translate blood-mimicking phantoms within an anatomically realistic human head model. An 8-element ultra-wideband (UWB) array of modified antipodal Vivaldi antennas was developed and driven by a two-port vector network analyzer spanning 0.6-9.0 GHz at an operating power of 1 mw. Complex scattering parameters were measured, and dielectric signatures of hemorrhage were learned using a dedicated deep neural network for prediction of hemorrhage classes and localization. An overall sensitivity and specificity for detection >0.99 was observed, with Rayliegh mean localization error of 1.65 mm. The study establishes the feasibility of a robust experimental model and deep learning solution for UWB microwave stroke detection.