An experimental system for detection and localization of hemorrhage
  using ultra-wideband microwaves with deep learning
Eisa Hedayati Fatemeh Safari George Verghese Vito R. Ciancia Daniel K. Sodickson Seena Dehkharghani Leeor Alon
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.