Robotic ultrasound-guided facet joint insertion.

PURPOSE: Facet joint insertion is a common treatment of chronic pain in the back and spine. This procedure is often performed under fluoroscopic guidance, where the staff's repetitive radiation exposure remains an unsolved problem. Robotic ultrasound (rUS) has the potential to reduce or even eliminate the use of radiation by using ultrasound with a robotic-guided needle insertion. This work presents first clinical data of rUS-based needle insertions extending previous work of our group. METHODS: Our system implements an automatic US acquisition protocol combined with a calibrated needle targeting system. This approach assists the physician by positioning the needle holder on a trajectory selected in a 3D US volume of the spine. RESULTS: By the time of submission, nine facets were treated with our approach as first data from an ongoing clinical study. The insertion success rate was shown to be comparable to current clinical practice. Furthermore, US imaging offers additional anatomical context for needle trajectory planning. CONCLUSION: This work shows first clinical data for robotic ultrasound-assisted facet joint insertion as a promising solution that can easily be incorporated into the clinical workflow. Presented results show the clinical value of such a system.

Use the force: deformation correction in robotic 3D ultrasound.

PURPOSE: Ultrasound acquisitions are typically affected by deformations due to the pressure applied onto the contact surface. While a certain amount of pressure is necessary to ensure good acoustic coupling and visibility of the anatomy under examination, the caused deformations hinder accurate localization and geometric analysis of anatomical structures. These complications have even greater impact in case of 3D ultrasound scans as they limit the correct reconstruction of acquired volumes. METHODS: In this work, we propose a method to estimate and correct the induced deformation based solely on the tracked ultrasound images and information about the applied force. This is achieved by modeling estimated displacement fields of individual image sequences using the measured force information. By representing the computed displacement fields using a graph-based approach, we are able to recover a deformation-less 3D volume. RESULTS: Validation is performed on 30 in vivo human datasets acquired using a robotic ultrasound framework. Compared to ground truth, the presented deformation correction shows errors of [Formula: see text] for an applied force of 5 N at a penetration depth of 55 mm. CONCLUSION: The proposed technique allows for the correction of deformations induced by the transducer pressure in entire 3D ultrasound volumes. Our technique does not require biomechanical models, patient-specific assumptions or information about the tissue properties; it can be employed based on the information from readily available robotic ultrasound platforms.

Acoustic window planning for ultrasound acquisition.

Autonomous robotic ultrasound has recently gained considerable interest, especially for collaborative applications. Existing methods for acquisition trajectory planning are solely based on geometrical considerations, such as the pose of the transducer with respect to the patient surface. PURPOSE: This work aims at establishing acoustic window planning to enable autonomous ultrasound acquisitions of anatomies with restricted acoustic windows, such as the liver or the heart. METHODS: We propose a fully automatic approach for the planning of acquisition trajectories, which only requires information about the target region as well as existing tomographic imaging data, such as X-ray computed tomography. The framework integrates both geometrical and physics-based constraints to estimate the best ultrasound acquisition trajectories with respect to the available acoustic windows. We evaluate the developed method using virtual planning scenarios based on real patient data as well as for real robotic ultrasound acquisitions on a tissue-mimicking phantom. RESULTS: The proposed method yields superior image quality in comparison with a naive planning approach, while maintaining the necessary coverage of the target. CONCLUSION: We demonstrate that by taking image formation properties into account acquisition planning methods can outperform naive plannings. Furthermore, we show the need for such planning techniques, since naive approaches are not sufficient as they do not take the expected image quality into account.

3D ultrasound registration-based visual servoing for neurosurgical navigation.

PURPOSE: We present a fully image-based visual servoing framework for neurosurgical navigation and needle guidance. The proposed servo-control scheme allows for compensation of target anatomy movements, maintaining high navigational accuracy over time, and automatic needle guide alignment for accurate manual insertions. METHOD: Our system comprises a motorized 3D ultrasound (US) transducer mounted on a robotic arm and equipped with a needle guide. It continuously registers US sweeps in real time with a pre-interventional plan based on CT or MR images and annotations. While a visual control law maintains anatomy visibility and alignment of the needle guide, a force controller is employed for acoustic coupling and tissue pressure. We validate the servoing capabilities of our method on a geometric gel phantom and real human anatomy, and the needle targeting accuracy using CT images on a lumbar spine gel phantom under neurosurgery conditions. RESULTS: Despite the varying resolution of the acquired 3D sweeps, we achieved direction-independent positioning errors of [Formula: see text] mm and [Formula: see text], respectively. Our method is capable of compensating movements of around 25 mm/s and works reliably on human anatomy with errors of [Formula: see text] mm. In all four manual insertions by an expert surgeon, a needle could be successfully inserted into the facet joint, with an estimated targeting accuracy of [Formula: see text] mm, superior to the gold standard. CONCLUSION: The experiments demonstrated the feasibility of robotic ultrasound-based navigation and needle guidance for neurosurgical applications such as lumbar spine injections.

Towards MRI-Based Autonomous Robotic US Acquisitions: A First Feasibility Study.

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.