Tele-ultrasound has the potential greatly to improve health equity for countless remote communities. However, practical scenarios involve potentially large time delays which cause current implementations of telerobotic ultrasound (US) to fail. Using a local model of the remote environment to provide haptics to the expert operator can decrease teleoperation instability, but the delayed visual feedback remains problematic. This paper introduces a robotic tele-US system in which the local model is not only haptic, but also visual, by re-slicing and rendering a pre-acquired US sweep in real time to provide the operator a preview of what the delayed image will resemble. A prototype system is presented and tested with 15 volunteer operators. It is found that visual-haptic model-mediated teleoperation (MMT) compensates completely for time delays up to 1000 ms round trip in terms of operator effort and completion time while conventional MMT does not. Visual-haptic MMT also significantly outperforms MMT for longer time delays in terms of motion accuracy and force control. This proof-of-concept study suggests that visual-haptic MMT may facilitate remote robotic tele-US.

In brain tumor surgery, the quality and safety of the procedure can be impacted by intra-operative tissue deformation, called brain shift. Brain shift can move the surgical targets and other vital structures such as blood vessels, thus invalidating the pre-surgical plan. Intra-operative ultrasound (iUS) is a convenient and cost-effective imaging tool to track brain shift and tumor resection. Accurate image registration techniques that update pre-surgical MRI based on iUS are crucial but challenging. The MICCAI Challenge 2018 for Correction of Brain shift with Intra-Operative UltraSound (CuRIOUS2018) provided a public platform to benchmark MRI-iUS registration algorithms on newly released clinical datasets. In this work, we present the data, setup, evaluation, and results of CuRIOUS 2018, which received 6 fully automated algorithms from leading academic and industrial research groups. All algorithms were first trained with the public RESECT database, and then ranked based on a test dataset of 10 additional cases with identical data curation and annotation protocols as the RESECT database. The article compares the results of all participating teams and discusses the insights gained from the challenge, as well as future work.

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.

Capturing an enclosing volume of moving subjects and organs using fast individual image slice acquisition has shown promise in dealing with motion artefacts. Motion between slice acquisitions results in spatial inconsistencies that can be resolved by slice-to-volume reconstruction (SVR) methods to provide high quality 3D image data. Existing algorithms are, however, typically very slow, specialised to specific applications and rely on approximations, which impedes their potential clinical use. In this paper, we present a fast multi-GPU accelerated framework for slice-to-volume reconstruction. It is based on optimised 2D/3D registration, super-resolution with automatic outlier rejection and an additional (optional) intensity bias correction. We introduce a novel and fully automatic procedure for selecting the image stack with least motion to serve as an initial registration target. We evaluate the proposed method using artificial motion corrupted phantom data as well as clinical data, including tracked freehand ultrasound of the liver and fetal Magnetic Resonance Imaging. We achieve speed-up factors greater than 30 compared to a single CPU system and greater than 10 compared to currently available state-of-the-art multi-core CPU methods. We ensure high reconstruction accuracy by exact computation of the point-spread function for every input data point, which has not previously been possible due to computational limitations. Our framework and its implementation is scalable for available computational infrastructures and tests show a speed-up factor of 1.70 for each additional GPU. This paves the way for the online application of image based reconstruction methods during clinical examinations. The source code for the proposed approach is publicly available.

Dense clinical data like 3D Computed Tomography (CT) scans can be visualized together with real-time imaging for a number of medical intervention applications. However, it is difficult to provide a fused visualization that allows sufficient spatial perception of the anatomy of interest, as derived from the rich pre-operative scan, while not occluding the real-time image displayed embedded within the volume. We propose an importance-driven approach that presents the embedded data such that it is clearly visible along with its spatial relation to the surrounding volumetric material. To support this, we present and integrate novel techniques for importance specification, feature emphasis, and contextual cutaway generation. We show results in a clinical context where a pre-operative CT scan is visualized alongside a tracked ultrasound image, such that the important vasculature is depicted between the viewpoint and the ultrasound image, while a more opaque representation of the anatomy is exposed in the surrounding area.

This paper presents methods and a clinical procedure for integrating B-mode ultrasound images tagged with position information with a planning computed tomography (CT) scan for radiotherapy. A workflow is described that allows the integration of these modalities into the clinic. A surface mapping approach provides a preregistration of the ultrasound image borders onto the patient's skin. Successively, a set of individual ultrasound images from a freehand sweep is chosen by the physician. These images are automatically registered with the planning CT scan using novel intensity-based methods. We put a particular focus on deriving an appropriate similarity measure based on the physical properties and artifacts of ultrasound. A combination of a weighted mutual information term, edge correlation, clamping to the skin surface, and occlusion detection is able to assess the alignment of structures in ultrasound images and information reconstructed from the CT data. We demonstrate the practicality of our methods on five patients with head and neck tumors and cervical lymph node metastases and provide a detailed report on the conducted experiments, including the setup, calibration, acquisition, and verification of our algorithms. The mean target registration error on nine data sets is 3.9 mm. Thus, the additional information about intranodal architecture and fulfillment of malignancy criteria derived from a high-resolution ultrasonography of lymph nodes can be localized and visualized in the CT scan coordinate space and is made available for further radiation treatment planning.