论文标题
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Phase Aberration Robust Beamformer for Planewave US Using Self-Supervised Learning
论文作者
论文摘要
由于其实时和非侵入性，超声（US）被广泛用于临床成像应用。然而，由于体系内声速（SOS）的变化引起的相差伪像，其病变的可检测性通常在许多应用中受到限制。为了解决这个问题，在这里，我们提出了一种新型的自我监督的3D CNN，该CNN可以实现相差稳健的平面波成像。我们的方法不是像常规方法那样旨在估算SOS分布，而是以自我监督的方式对网络进行训练，从而通过对声音速度的变化建模为随机的速度来稳健地产生来自各个阶段畸变图像的高质量图像。实验性的结果是使用模仿组织的幻影和\ textit {in Vivo}扫描的实际测量结果证实，所提出的方法可以显着减少相差伪像并提高深扫描的视觉质量。
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.
