Robotic ultrasound (US) imaging aims at overcoming some of the limitations of
free-hand US examinations, e.g. difficulty in guaranteeing intra- and
inter-operator repeatability. However, due to anatomical and physiological
variations between patients and relative movement of anatomical substructures,
it is challenging to robustly generate optimal trajectories to examine the
anatomies of interest, in particular, when they comprise articulated joints. To
address this challenge, this paper proposes a vision-based approach allowing
autonomous robotic US limb scanning. To this end, an atlas MRI template of a
human arm with annotated vascular structures is used to generate trajectories
and register and project them onto patients' skin surfaces for robotic US
acquisition. To effectively segment and accurately reconstruct the targeted 3D
vessel, we make use of spatial continuity in consecutive US frames by
incorporating channel attention modules into a U-Net-type neural network. The
automatic trajectory generation method is evaluated on six volunteers with
various articulated joint angles. In all cases, the system can successfully
acquire the planned vascular structure on volunteers' limbs. For one volunteer
the MRI scan was also available, which allows the evaluation of the average
radius of the scanned artery from US images, resulting in a radius estimation
($1.2\pm0.05~mm$) comparable to the MRI ground truth ($1.2\pm0.04~mm$).

该研究提出了一种基于视觉的方法，允许自主的机器人超声波肢体扫描，使用 MRI 图像的基准分析血管结构生成扫描轨迹，并利用神经网络对连续的超声帧进行空间连续性的分析以实现精确的血管结构重建及测量。

基于自主分割的超声波图像采集技术在关节运动情况下的应用

Towards Autonomous Atlas-based Ultrasound Acquisitions in Presence of Articulated Motion

To carry out autonomous 3D scanning and online reconstruction of unknown
indoor scenes, one has to find a balance between global exploration of the
entire scene and local scanning of the objects within it. In this work, we
propose a novel approach, which provides object-aware guidance for
autoscanning, for exploring, reconstructing, and understanding an unknown scene
within one navigation pass. Our approach interleaves between object analysis to
identify the next best object (NBO) for global exploration, and object-aware
information gain analysis to plan the next best view (NBV) for local scanning.
First, an objectness-based segmentation method is introduced to extract
semantic objects from the current scene surface via a multi-class graph cuts
minimization. Then, an object of interest (OOI) is identified as the NBO which
the robot aims to visit and scan. The robot then conducts fine scanning on the
OOI with views determined by the NBV strategy. When the OOI is recognized as a
full object, it can be replaced by its most similar 3D model in a shape
database. The algorithm iterates until all of the objects are recognized and
reconstructed in the scene. Various experiments and comparisons have shown the
feasibility of our proposed approach.

提出了一种自主三维扫描的新方法，它通过对象分析确定全局探测的下一个最佳对象（NBO），并通过对象感知信息分析来规划局部扫描的下一个最佳视图（NBV），从而实现了对未知场景的探索、重构和理解。