A Tripartite Data-Driven Approach for Wide-Band Electromagnetic Imaging

Electromagnetic medical imaging in the microwave regime is a hard problem notorious for 1) instability 2) under-determinism. This two-pronged problem is tackled with a two-pronged solution that uses double compression to maximally utilizing the cheap unlabelled data to a) provide a priori information required to ease under-determinism and b) reduce sensitivity of inference to the input. The result is a stable solver with a high resolution output. This work introduces a model named ‘DeepHead’; a fully data-driven implementation of the paradigm proposed in the context of microwave brain imaging. It infers the dielectric distribution of the brain at a desired single frequency while making use of an input that spreads over a wide band of frequencies. The performance of the model is evaluated with both simulations and human volunteers experiments. The inference made is juxtaposed with ground-truth dielectric distribution in simulation case, and the golden MRI / CT imaging modalities of the volunteers in real-world case.