Introducing: DeepHead, Wide-band Electromagnetic Imaging Paradigm
This addresses a critical problem in medical imaging for brain diagnostics, offering a novel paradigm that could improve accuracy and reliability, though it appears incremental in its specific application to microwave imaging.
The paper tackles the instability and under-determinism in microwave electromagnetic medical imaging by introducing DeepHead, a data-driven method that uses double compression to leverage unlabelled data for stable, high-resolution inference of brain dielectric distributions, validated through simulations and human experiments.
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. DeepHead is 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.