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Computer-aided medical systems, e.g. in the fields of medical robotics or image-based assistance, are continuously investigated to overcome human limitations concerning perception, memory or dexterity. A common requirement of such systems is the availability of a digital model describing the patient’s position and morphology during a procedure. Operational complexity and technical limitations of established 3D imaging methods leave clinical settings in need of a method for the fast acquisition of a three-dimensional body surface representation. For this purpose, we propose an unsupervised and efficient body registration pipeline based on the markerless elastic registration and completion of single-view stereo range images of the body surface with statistical parametric body shape templates. Initial results show a promising representative quality of the models generated through the registration process with submillimetric fitting accuracy and realistic surface morphology, indicating the general feasibility of our approach as an instant body registration method for automated medical and biometric applications.

Ultrasound (US) imaging, in contrast to other image guidance techniques, offers the distinct advantage of providing volumetric image data in real-time (4D) without using ionizing radiation. The goal of this study was to perform the first quantitative comparison of three different 4D US systems with fast matrix array probes and real-time data streaming regarding their target tracking accuracy and system latency. Sinusoidal motion of varying amplitudes and frequencies was used to simulate breathing motion with a robotic arm and a static US phantom. US volumes and robot positions were acquired online and stored for retrospective analysis. A template matching approach was used for target localization in the US data. Target motion measured in US was compared to the reference trajectory performed by the robot to determine localization accuracy and system latency. Using the robotic setup, all investigated 4D US systems could detect a moving target with sub-millimeter accuracy. However, especially high system latency increased tracking errors substantially and should be compensated with prediction algorithms for respiratory motion compensation.

Most kinematic structures in robot architectures for medical tasks are not optimal. Further, the workspace and payloads are often oversized which results in high product prices that are not suitable for a clinical technology transfer. To investigate optimal kinematic structures and configurations, we have developed an adaptive simulation framework with an associated workflow for requirement analyses, modelling and simulation of specific robot kinematics. The framework is used to build simple and cost effective medical robot designs and was evaluated in a tool manipulation task where medical instruments had to be positioned precisely and oriented on the patient's body. The model quality is measured based on the maximum workspace coverage according to a configurable scoring metric. The metric generalizes among different human body shapes that are based on anthropometric data from UMTRI Human Shape. This dexterity measure is used to analyze different kinematic structures in simulations using the open source simulation tool V-REP. Therefor we developed simulation and visualization procedures for medical tasks based on a patchwork of size-variant anatomical target regions that can be configured and selectively activated in a motion planning controller. In our evaluations we compared the dexterity scores of a commercial lightweight robot arm with 7 joints to optimized kinematic structures with 6, 7 and 8 joints. Compared to the commercial hardware, we achieved improvements of 59% when using an optimized 6- dimensional robot arm, 64% with the 7-dimensional arm and 96% with an 8-dimensional robot arm. Our results show that simpler robot designs can outperform the typically used commercial robot arms in medical applications where the maximum workspace coverage is essential. Our framework provides the basis for a fully automatic optimization tool of the robot parameters that can be applied to a large variety of problems.

Automation of the image acquisition process via robotic solutions offer a large leap towards resolving ultrasound’s user-dependency. This paper, as part of a larger project aimed to develop a multipurpose 4d-ultrasonic force-sensitive robot for medical applications, focuses on achieving real-time remote visualisation for 4d ultrasound image transfer. This was possible through implementing our software modification on a GE Vivid 7 Dimension workstation, which operates a matrix array probe controlled by a KUKA LBR iiwa 7 7-DOF robotic arm. With the help of robotic positioning and the matrix array probe, fast volumetric imaging of target regions was feasible. By testing ultrasound volumes, which were roughly 880 kB in size, while using gigabit Ethernet connection, a latency of ∼57 ms was achievable for volume transfer between the ultrasound station and a remote client application, which as a result allows a frame count of 17.4 fps. Our modification thus offers for the first time real-time remote visualization, recording and control of 4d ultrasound data, which can be implemented in teleoperation.

We demonstrate large-area robotically assisted optical coherence tomography (LARA-OCT), utilizing a seven-degree-of-freedom robotic arm in conjunction with a 3.3 MHz swept-source OCT to raster scan samples of arbitrary shape. By combining multiple fields of view (FOV), LARA-OCT can probe a much larger area than conventional OCT. Also, nonplanar and curved surfaces like skin on arms and legs can be probed. The lenses in the LARA-OCT scanner with their normal FOV can have fewer aberrations and less complex optics compared to a single wide field design. This may be especially critical for high resolution scans. We directly use our fast MHz-OCT for tracking and stitching, making additional machine vision systems like cameras, positioning, tracking or navigation devices obsolete. This also eliminates the need for complex coordinate system registration between OCT and the machine vision system. We implemented a real time probe-to-surface control that maintains the probe alignment orthogonal to the sample by only using surface information from the OCT images. We present OCT data sets with volume sizes of 140 × 170 × 20 mm 3 , captured in 2.5 minutes., Competing Interests: R. Huber: University of Lübeck (P), Optores GmbH (I,P,R), Optovue Inc. (R), Abott (R)., (© 2024 Optica Publishing Group.)