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.
翻译:微波系统中的电磁医学成像是一个非常棘手的问题,其臭名昭著的原因是:(1) 不稳定(2) 不确定;(2) 不确定;这一双管齐下的问题通过双管齐下的解决办法来解决,使用双重压缩来最大限度地利用廉价的无标签数据达到a) 提供一种必要的先验信息,以缓解不确定状态,和(b) 降低对输入的推断的敏感度。结果是一个具有高分辨率输出的稳定解析器。深头是一个完全以数据为驱动的微波脑成像中拟议范例的实施过程。它推导出脑部在理想的单一频率上的电流分布,同时利用在宽频谱上传播的投入。模型的性能通过模拟和人类志愿者实验加以评估。所作的推断与模拟案例的地面-断电分布和真实世界案件中的志愿者的黄金 MRI/CT成像模式并列。