Ultrasound is a vital imaging modality utilized for a variety of diagnostic and interventional procedures. However, an expert sonographer is required to make accurate maneuvers of the probe over the human body while making sense of the ultrasound images for diagnostic purposes. This procedure requires a substantial amount of training and up to a few years of experience. In this paper, we propose an autonomous robotic ultrasound system that uses Bayesian Optimization (BO) in combination with the domain expertise to predict and effectively scan the regions where diagnostic quality ultrasound images can be acquired. The quality map, which is a distribution of image quality in a scanning region, is estimated using Gaussian process in BO. This relies on a prior quality map modeled using expert's demonstration of the high-quality probing maneuvers. The ultrasound image quality feedback is provided to BO, which is estimated using a deep convolution neural network model. This model was previously trained on database of images labelled for diagnostic quality by expert radiologists. Experiments on three different urinary bladder phantoms validated that the proposed autonomous ultrasound system can acquire ultrasound images for diagnostic purposes with a probing position and force accuracy of 98.7% and 97.8%, respectively.

翻译：超声是一种重要的成像模态，广泛应用于各类诊断和介入操作。然而，执行诊断操作需要经验丰富的超声技师在解读超声图像的同时，精确操控探头在人体表面的移动。这一过程需要大量训练和数年经验积累。本文提出了一种自主机器人超声系统，该系统将贝叶斯优化与领域知识相结合，用于预测并有效扫描可获取诊断质量超声图像的区域。通过贝叶斯优化中的高斯过程，对扫描区域内的图像质量分布（即质量图谱）进行估计。该过程依赖于由专家演示的高质量探头操作所建模的先验质量图谱。系统采用深度卷积神经网络模型估计超声图像质量反馈，该模型先前已在由放射科专家标注诊断质量标记的图像数据库上完成训练。在三种不同膀胱体模上的实验验证表明，所提出的自主超声系统能够以98.7%的探头定位精度和97.8%的施力精度获取可用于诊断目的的超声图像。