Screen-based Augmented Reality (AR) systems can be built as a window into the real world as often done in mobile AR applications or using the Magic Mirror metaphor, where users can see themselves with augmented graphics on a large display. The term Magic Mirror implies that the display shows the users enantiomorph, i.e. the mirror image, such that the system mimics a real-world physical mirror. However, the question arises whether one should design a traditional mirror, or instead display the true mirror image by means of a non-reversing mirror? We discuss the perceptual differences between these two mirror visualization concepts and present a first comparative study in the context of Magic Mirror anatomy teaching.

Robotic ultrasound has the potential to assist and guide physicians during interventions. In this work, we present a set of methods and a workflow to enable autonomous MRI-guided ultrasound acquisitions. Our approach uses a structured-light 3D scanner for patient-to-robot and image-to-patient calibration, which in turn is used to plan 3D ultrasound trajectories. These MRI-based trajectories are followed autonomously by the robot and are further refined online using automatic MRI/US registration. Despite the low spatial resolution of structured light scanners, the initial planned acquisition path can be followed with an accuracy of 2.46 ± 0.96 mm. This leads to a good initialization of the MRI/US registration: the 3D-scan-based alignment for planning and acquisition shows an accuracy (distance between planned ultrasound and MRI) of 4.47 mm, and 0.97 mm after an online-update of the calibration based on a closed loop registration.

Medical robotic ultrasound offers potential to assist interventions, ease long-term monitoring and reduce operator dependency. Various techniques for remote control of ultrasound probes through telemanipulation systems have been presented in the past, however not exploiting the potential of fully autonomous acquisitions directly performed by robotic systems. In this paper, a trajectory planning algorithm for automatic robotic ultrasound acquisition under expert supervision is introduced. The objective is to compute a suitable path for covering a volume of interest selected in diagnostic images, for example by prior segmentation. A 3D patient surface point cloud is acquired using a depth camera, which is the sole prerequisite besides the volume delineation. An easily parameterizable path function generates single or multiple parallel scan trajectories capable of dealing with large target volumes. A spline is generated through the preliminary path points and is transferred to a lightweight robot to perform the ultrasound scan using an impedance control mode. The proposed approach is validated via simulation as well as on phantoms and on animal viscera.

While intraoperative imaging is commonly used to guide surgical interventions, automatic robotic support for image-guided navigation has not yet been established in clinical routine. In this paper, we propose a novel visual servoing framework that combines, for the first time, full image-based 3D ultrasound registration with a real-time servo-control scheme. Paired with multi-modal fusion to a pre-interventional plan such as an annotated needle insertion path, it thus allows tracking a target anatomy, continuously updating the plan as the target moves, and keeping a needle guide aligned for accurate manual insertion. The presented system includes a motorized 3D ultrasound transducer mounted on a force-controlled robot and a GPU-based image processing toolkit. The tracking accuracy of our framework is validated on a geometric agar/gelatin phantom using a second robot, achieving positioning errors of on average 0.42-0.44 mm. With compounding and registration runtimes of up to total around 550 ms, real-time performance comes into reach. We also present initial results on a spine phantom, demonstrating the feasibility of our system for lumbar spine injections.

In this paper we present the usage of a drop-in gamma probe for intra-operative Single-Photon Emission Computed Tomography (SPECT) imaging in the scope of minimally invasive robot-assisted interventions. The probe is designed to be inserted and reside inside the abdominal cavity during the intervention. It is grasped during the procedure using a robotic laparoscopic gripper enabling full six degrees of freedom handling by the surgeon. We demonstrate the first deployment of the tracked probe for intra-operative in-patient robotic SPECT enabling augmented-reality image guidance. The hybrid mechanical- and image-based in-patient probe tracking is shown to have an accuracy of 0.2 mm. The overall system performance is evaluated and tested with a phantom for gynecological sentinel lymph node interventions and compared to ground-truth data yielding a mean reconstruction accuracy of 0.67 mm.

Image-guided medical interventions more frequently rely on Augmented Reality (AR) visualization to enable surgical navigation. Current systems use 2-D monitors to present the view from external cameras, which does not provide an ideal perception of the 3-D position of the region of interest. Despite this problem, most research targets the direct overlay of diagnostic imaging data, and only few studies attempt to improve the perception of occluded structures in external camera views. The focus of this paper lies on improving the 3-D perception of an augmented external camera view by combining both auditory and visual stimuli in a dynamic multi-sensory AR environment for medical applications. Our approach is based on Temporal Distance Coding (TDC) and an active surgical tool to interact with occluded virtual objects of interest in the scene in order to gain an improved perception of their 3-D location. Users performed a simulated needle biopsy by targeting virtual lesions rendered inside a patient phantom. Experimental results demonstrate that our TDC-based visualization technique significantly improves the localization accuracy, while the addition of auditory feedback results in increased intuitiveness and faster completion of the task.

This paper presents an approach to predict the deformation of the lungs and surrounding organs during respiration. The framework incorporates a computational model of the respiratory system, which comprises an anatomical model extracted from computed tomography (CT) images at end-expiration (EE), and a biomechanical model of the respiratory physiology, including the material behavior and interactions between organs. A personalization step is performed to automatically estimate patient-specific thoracic pressure, which drives the biomechanical model. The zone-wise pressure values are obtained by using a trust-region optimizer, where the estimated motion is compared to CT images at end-inspiration (EI). A detailed convergence analysis in terms of mesh resolution, time stepping and number of pressure zones on the surface of the thoracic cavity is carried out. The method is then tested on five public datasets. Results show that the model is able to predict the respiratory motion with an average landmark error of 3.40 ±1.0 mm over the entire respiratory cycle. The estimated 3-D lung motion may constitute as an advanced 3-D surrogate for more accurate medical image reconstruction and patient respiratory analysis.