Ultrasound is a commonly used medical imaging modality that requires expert sonographers to manually maneuver the ultrasound probe based on the acquired image. Autonomous Robotic Ultrasound (A-RUS) is an appealing alternative to this manual procedure in order to reduce sonographers' workload. The key challenge to A-RUS is optimizing the ultrasound image quality for the region of interest across different patients. This requires knowledge of anatomy, recognition of error sources and precise probe position, orientation and pressure. Sample efficiency is important while optimizing these parameters associated with the robotized probe controller. Bayesian Optimization (BO), a sample-efficient optimization framework, has recently been applied to optimize the 2D motion of the probe. Nevertheless, further improvements are needed to improve the sample efficiency for high-dimensional control of the probe. We aim to overcome this problem by using a neural network to learn a low-dimensional kernel in BO, termed as Deep Kernel (DK). The neural network of DK is trained using probe and image data acquired during the procedure. The two image quality estimators are proposed that use a deep convolution neural network and provide real-time feedback to the BO. We validated our framework using these two feedback functions on three urinary bladder phantoms. We obtained over 50% increase in sample efficiency for 6D control of the robotized probe. Furthermore, our results indicate that this performance enhancement in BO is independent of the specific training dataset, demonstrating inter-patient adaptability.

翻译：超声是一种常用的医学成像模态，需要经验丰富的超声医师根据获取的图像手动操作超声探头。自主机器人超声（A-RUS）是替代这一手动流程的可行方案，可减轻超声医师的工作量。A-RUS的关键挑战在于为不同患者的感兴趣区域优化超声图像质量，这需要解剖学知识、误差源识别能力以及精确的探头位置、朝向和按压力度。在优化与机器人探头控制器相关的参数时，样本效率尤为重要。贝叶斯优化（BO）作为一种样本高效的优化框架，近期已被应用于优化探头的二维运动。然而，为提升高维探头控制的样本效率，仍需进一步改进。我们旨在通过引入神经网络学习BO中的低维核函数（称为深度核函数DK）来解决该问题。DK的神经网络利用操作过程中采集的探头数据和图像数据进行训练。本文提出了两种基于深度卷积神经网络的图像质量估计器，可为BO提供实时反馈。我们在三个膀胱体模上使用这两种反馈函数验证了框架。对于机器人探头的六维控制，样本效率提升了超过50%。此外，我们的结果表明，BO中的这一性能提升与特定训练数据集无关，展现了跨患者的适应性。