Manipulating a flexible ureteroscope is difficult, due to its bendable body and hand–eye coordination problems, especially when exploring the lower pole of the kidney. Though robotic interventions have been adopted in various clinical scenarios, they are rarely used in ureteroscopy. This study proposes a teleoperation system consists of a soft robotic endoscope together with a Guidance Virtual Fixture (GVF) to help users explore the kidney’s lower pole. The soft robotic arm was a cable-driven, 3D-printed design with a helicoid structure. GVF was dynamically constructed using video streams from an endoscopic camera. With a haptic controller, GVF can provide haptic feedback to guide the users in following a trajectory. In the user study, participants were asked to follow trajectories when the soft robotic arm was in a retroflex posture. The results suggest that the GVF can reduce errors in the trajectory tracking tasks when the users receive the proper training and gain more experience. Based on the NASA Task Load Index questionnaires, most participants preferred having the GVF when manipulating the robotic arm. In conclusion, the results demonstrate the benefits and potential of using a robotic arm with a GVF. More research is needed to investigate the effectiveness of the GVFs and the robotic endoscope in ureteroscopic procedures.@en

Cardiologists highlight the need for an intra-operative 3D visualization to assist interventions. The intra-operative 2D X-ray/Digital Subtraction Angiography (DSA) images in the standard clinical workflow limit cardiologists’ views significantly. Compared with image-to-image registration, model-to-image registration is an essential approach taking advantage of the reuse of pre-operative 3D models reconstructed from Computed Tomography Angiography (CTA) images. Traditional optimized-based registration methods suffer severely from high computational complexity. Moreover, the consequence of lacking ground truth for learning-based registration approaches should not be neglected. To overcome these challenges, we introduce a model-to-image registration framework via deep learning for image-guided endovascular catheterization. This work performs autonomous vessel segmentation from intra-operative fluoroscopy images via a deep residual U-net and a model-to-image matching via a convolutional neural network. For this study, image data were collected from 10 patients who performed Transcatheter Aortic Valve Implantation (TAVI) procedures. It was found that vessel segmentation of test data results in median values of Dice Similarity Coefficient, Precision, and Recall of (0.75, 0.58, 0.67) for femoral artery, and (0.71, 0.56, 0.74) for aortic root. The segmentation network behaves better than manual annotation, and it recognizes part of vessels that were not labeled manually. Image matching between the transformed moving image and the fixed image results in a median value of Recall of 0.90. The proposed approach achieves a good accuracy of vessel segmentation and a good recall value of model-to-image matching.@en

Background During Transcranial Magnetic Stimulation (TMS) experiments researchers often use a neuronavigation system to precisely and accurately maintain coil position and orientation. New method This study aimed to develop and validate an open-source software for TMS coil navigation. StimTrack uses an optical tracker and an intuitive user interface to facilitate the maintenance of position and orientation of any type of coil within and between sessions. Additionally, online access to navigation data is provided, hereby adding e.g. the ability to start or stop the magnetic stimulator depending on the distance to target or the variation of the orientation angles. Results StimTrack allows repeatable repositioning of the coil within 0.7 mm for translation and <1° for rotation. Stimulus-response (SR) curves obtained from 19 healthy volunteers were used to demonstrate that StimTrack can be effectively used in a typical experiment. An excellent intra and inter-session reliability (ICC > 0.9) was obtained on all parameters computed on SR curves acquired using StimTrack. Comparison with existing method StimTrack showed a target accuracy similar to that of a commercial neuronavigation system (BrainSight, Rogue Research Inc.). Indeed, small differences both in position (∼0.2 mm) and orientation (<1°) were found between the systems. These differences are negligible given the human error involved in landmarks registration. Conclusions StimTrack, available as supplementary material, is found to be a good alternative for commercial neuronavigation systems facilitating assessment changes in corticospinal excitability using TMS. StimTrack allows researchers to tailor its functionality to their specific needs, providing added value that benefits experimental procedures and improves data quality.

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Objective: The clinical use of Transcranial Magnetic Stimulation (TMS) as a technique to assess corticospinal excitability is limited by the time for data acquisition and the measurement variability. This study aimed at evaluating the reliability of Stimulus-Response (SR) curves acquired with a recently proposed rapid protocol on tibialis anterior muscle of healthy older adults. Methods: Twenty-four neurologically-intact adults (age:55–75 years) were recruited for this test-retest study. During each session, six SR curves, 3 at rest and 3 during isometric muscle contractions at 5% of maximum voluntary contraction (MVC), were acquired. Motor Evoked Potentials (MEPs) were normalized to the maximum peripherally evoked response; the coil position and orientation were monitored with an optical tracking system. Intra- and inter-session reliability of motor threshold (MT), area under the curve (AURC), MEP_max, stimulation intensity at which the MEP is mid-way between MEP_max and MEP_min (I50), slope in I50, MEP latency, and silent period (SP) were assessed in terms of Standard Error of Measurement (SEM), relative SEM, Minimum Detectable Change (MDC), and Intraclass Correlation Coefficient (ICC). Results: The relative SEM was ≤10% for MT, I50, latency and SP both at rest and 5%MVC, while it ranged between 11% and 37% for AURC, MEP_max, and slope. MDC values were overall quite large; e.g., MT required a change of 12%MSO at rest and 10%MSO at 5%MVC to be considered a real change. Inter-sessions ICC were >0.6 for all measures but slope at rest and MEP_max and latency at 5%MVC. Conclusions: Measures derived from SR curves acquired in <4 minutes are affected by similar measurement errors to those found with long-lasting protocols, suggesting that the rapid method is at least as reliable as the traditional methods. As specifically designed to include older adults, this study provides normative data for future studies involving older neurological patients (e.g. stroke survivors).

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