Phase Aberration Robust Beamformer for Planewave US Using Self-Supervised Learning
This addresses phase aberration artifacts in ultrasound imaging for clinical applications, representing an incremental improvement over conventional methods.
The paper tackles the problem of phase aberration artifacts in ultrasound imaging, which limit lesion detectability, by proposing a self-supervised 3D CNN that robustly generates high-quality images from aberrated inputs, with experimental results showing significant artifact reduction and improved visual quality in deep scans.
Ultrasound (US) is widely used for clinical imaging applications thanks to its real-time and non-invasive nature. However, its lesion detectability is often limited in many applications due to the phase aberration artefact caused by variations in the speed of sound (SoS) within body parts. To address this, here we propose a novel self-supervised 3D CNN that enables phase aberration robust plane-wave imaging. Instead of aiming at estimating the SoS distribution as in conventional methods, our approach is unique in that the network is trained in a self-supervised manner to robustly generate a high-quality image from various phase aberrated images by modeling the variation in the speed of sound as stochastic. Experimental results using real measurements from tissue-mimicking phantom and \textit{in vivo} scans confirmed that the proposed method can significantly reduce the phase aberration artifacts and improve the visual quality of deep scans.