Real-Time Model-Based Quantitative Ultrasound and Radar
This addresses the need for fast and accurate quantitative imaging in medical applications like cancer detection and stroke diagnosis, representing a novel method for a known bottleneck.
The paper tackles the problem of slow and unreliable quantitative medical imaging from ultrasound and radar signals by proposing a neural network based on wave propagation physics, achieving reconstruction of multiple physical properties in under one second for complex scenarios with only eight elements.
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.