  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.
