Ultrasound and radar signals are highly beneficial for medical imaging as
they are non-invasive and non-ionizing. Traditional imaging techniques have
limitations in terms of contrast and physical interpretation. Quantitative
medical imaging can display various physical properties such as speed of sound,
density, conductivity, and relative permittivity. This makes it useful for a
wider range of applications, including improving cancer detection, diagnosing
fatty liver, and fast stroke imaging. However, current quantitative imaging
techniques that estimate physical properties from received signals, such as
Full Waveform Inversion, are time-consuming and tend to converge to local
minima, making them unsuitable for medical imaging. To address these
challenges, we propose a neural network based on the physical model of wave
propagation, which defines the relationship between the received signals and
physical properties. Our network can reconstruct multiple physical properties
in less than one second for complex and realistic scenarios, using data from
only eight elements. We demonstrate the effectiveness of our approach for both
radar and ultrasound signals.

基于波传播物理模型的神经网络可以在不到一秒钟的时间内针对复杂和现实场景，只使用八个元素的数据，重构多种物理性质，并对雷达和超声信号的有效性进行了验证。