Learning-based autonomous vascular guidewire navigation without human demonstration in the venous system of a porcine liver

Purpose The navigation of endovascular guidewires is a dexterous task where physicians and patients can benefit from automation. Machine learning-based controllers are promising to help master this task. However, human-generated training data are scarce and resource-intensive to generate. We investigate if a neural network-based controller trained without human-generated data can learn human-like behaviors. Methods We trained and evaluated a neural network-based controller via deep reinforcement learning in a finite element simulation to navigate the venous system of a porcine liver without human-generated data. The behavior is compared to manual expert navigation, and real-world transferability is evaluated. Results The controller achieves a success rate of 100% in simulation. The controller applies a wiggling behavior, where the guidewire tip is continuously rotated alternately clockwise and counterclockwise like the human expert applies. In the ex vivo porcine liver, the success rate drops to 30%, because either the wrong branch is probed, or the guidewire becomes entangled. Conclusion In this work, we prove that a learning-based controller is capable of learning human-like guidewire navigation behavior without human-generated data, therefore, mitigating the requirement to produce resource-intensive human-generated training data. Limitations are the restriction to one vessel geometry, the neglected safeness of navigation, and the reduced transferability to the real world

Prognostic Value of Different CMR-Based Techniques to Assess Left Ventricular Myocardial Strain in Takotsubo Syndrome

Cardiac magnetic resonance (CMR)-derived left ventricular (LV) global longitudinal strain (GLS) provides incremental prognostic information on various cardiovascular diseases but has not yet been investigated comprehensively in patients with Takotsubo syndrome (TS). This study evaluated the prognostic value of feature tracking (FT) GLS, tissue tracking (TT) GLS, and fast manual long axis strain (LAS) in 147 patients with TS, who underwent CMR at a median of 2 days after admission. Long-term mortality was assessed 3 years after the acute event. In contrast to LV ejection fraction and tissue characteristics, impaired FT-GLS, TT-GLS and fast manual LAS were associated with adverse outcome. The best cutoff points for the prediction of long-term mortality were similar with all three approaches: FT-GLS −11.28%, TT-GLS −11.45%, and fast manual LAS −10.86%. Long-term mortality rates were significantly higher in patients with FT-GLS > −11.28% (25.0% versus 9.8%; p = 0.029), TT-GLS > −11.45% (20.0% versus 5.4%; p = 0.016), and LAS > −10.86% (23.3% versus 6.6%; p = 0.014). However, in multivariable analysis, diabetes mellitus (p = 0.001), atrial fibrillation (p = 0.001), malignancy (p = 0.006), and physical triggers (p = 0.006) outperformed measures of myocardial strain and emerged as the strongest, independent predictors of long-term mortality in TS. In conclusion, CMR-based longitudinal strain provides valuable prognostic information in patients with TS, regardless of the utilized technique of assessment. Long-term mortality, however, is mainly determined by comorbidities

An image-based robotic needle guidance system for interventional radiology

Intro: We present a pre-clinical robotic guidance system with a dedicated image-based control software to support needle-based interventions in the angio suite. We also present first precision results for needle placement. Methods: The system consists of a Kuka LBR robot, equipped with a control software with OpenIGTLink interface and an image-based calibration and target planning software based on MeVisLab. The robot can hold either a calibration tool, containing 4 spheres with known geometry for coordinate system alignment of the imaging device and the robot or a needle-guide that prescribes the needle trajectory and supports multi-needle-placement. For needle placement, the robot places the needle guide on the patient skin to determine the needle entry point. The calibration and targeting software provides automatic calibration tool detection, image segmentation, registration, virtual needle placement, and ablation simulation. For the precision analysis we used a 15cm 16G needle that has been placed at n=13 target positions. For all positions, we compared planned and reached needle path in air and a gelatin phantom. Results: The robotic guidance system had an overall angular deviation between planned and reached position that is below 1° (range 0.50° to 1.37°), and an overall deviation of the shaft resp. skin entry point of 1.4mm (range 0.69mm to 2.41mm). Reproducibility of the needle path between air and gelatin was far better than overall system precision leaving potential for further system improvements. Time required for robot motion to target, needle insertion, 3D scan, needle removal, and robot motion to rest position was less than two minutes for this technical setup. Conclusion: The analyzed system has a better angle and shaft precision as reported for comparative systems. It is easy to use and fast. The system has potential to serve as a fully integrated biopsy and therapeutic needle intervention system in head and neck, pulmonary, and abdominal areas

Economy by Speed. Wie Closed‐loop‐Interventionssysteme moderne klinische Therapieprozesse ökonomisieren können

In den vergangenen Jahrzehnten wurde durch zahlreiche Forschungsaktivitäten eine breite Basis für personalisierte Tumorbehandlungen geschaffen. Vor allem die Kenntnisse in Bezug auf den molekularen Status eines Tumors münden in zahlreiche unterschiedliche Behandlungspfade und ‐optionen. Die Verbesserungen in der modernen Tumorversorgung reichen von effizienteren pharmazeutischen Wirkstoffen bis zu hochgenau geplanten und schonenderen operativen Eingriffen. Zu Beginn einer Behandlung, egal ob pharmazeutisch, operativ oder einer Kombination, steht immer die Informationsgewinnung über den erwähnten molekularen Status des Tumors. Gerade bei oligometastasierten Patienten, also Patienten mit einer lokal begrenzten Anzahl an Metastasen um den Primärtumor herum, ist eine Analyse des molekularen Status für jede einzelne Metastase ein erfolgversprechender Ansatz für eine spätere zielgerichtete Therapie. Dafür muss von jeder Metastase eine Gewebeprobe für nachfolgende Analysen genommen werden. Dieser Prozess ist aufwendig, ressourcenintensiv und damit sehr teuer in der Durchführung. Wir zeigen mit unserem Ansatz, wie durch die Kombination moderner bildgebender Verfahren mit assistierender Robotik ein enormer Zeitgewinn ermöglicht wird und damit moderne medizinische Prozeduren wirtschaftlich und nachhaltig nutzbar werden

ALICE - Artificial Intelligence Catheter. Towards Autonomous Closed Loop Control of Passive Endovascular Catheters Based on Deep Reinforcement Learning: Poster presented at Emerging Learning Techniques for Robotics, Workshop at the Hamlyn Symposium on Medical Robotics, 26th June 2019, London

Endovascular catheters are used for state of the art therapies of many widespread diseases. Navigating them can be very laborious and so far no robotic assistance exists for passive catheters. Steer-able catheters exist, but due to their large diameter they are not suitable for many interventions. We propose a closed loop control system where a deep reinforcement learning based control algorithm steers the catheter. The algorithm is provided with live data by a tracking system. Prior to the intervention the control algorithm is trained on the simulation model and by expert demonstration. Here we present the results of our experiments, where a control algorithm learns to steer a guidewire through a simplified vascular tree. Learning is performed in the simulation model and the result transferred to the test bench. Our results show that the algorithm is able to learn catheter steering. However the simulation results cannot be transferred to the test bench directly without facing a reduced accuracy due to the test bench not having perfect states like the simulation

Robotic Assistance System for Cone-Beam Computed Tomography-Guided Percutaneous Needle Placement

Purpose!#!The study aimed to evaluate a new robotic assistance system (RAS) for needle placement in combination with a multi-axis C-arm angiography system for cone-beam computed tomography (CBCT) in a phantom setting.!##!Materials and methods!#!The RAS consisted of a tool holder, dedicated planning software, and a mobile platform with a lightweight robotic arm to enable image-guided needle placement in conjunction with CBCT imaging. A CBCT scan of the phantom was performed to calibrate the robotic arm in the scan volume and to plan the different needle trajectories. The trajectory data were sent to the robot, which then positioned the tool holder along the trajectory. A 19G needle was then manually inserted into the phantom. During the control CBCT scan, the exact needle position was evaluated and any possible deviation from the target lesion measured.!##!Results!#!In total, 16 needle insertions targeting eight in- and out-of-plane sites were performed. Mean angular deviation from planned trajectory to actual needle trajectory was 1.12°. Mean deviation from target point and actual needle tip position was 2.74 mm, and mean deviation depth from the target lesion to the actual needle tip position was 2.14 mm. Mean time for needle placement was 361 s. Only differences in time required for needle placement between in- and out-of-plane trajectories (337 s vs. 380 s) were statistically significant (p = 0.0214).!##!Conclusion!#!Using this RAS for image-guided percutaneous needle placement with CBCT was precise and efficient in the phantom setting

Evaluation of a numerical simulation for cryoablation – comparison with bench data, clinical kidney and lung cases

Purpose The accuracy of a numerical simulation of cryoablation ice balls was evaluated in gel phantom data as well as clinical kidney and lung cases. Materials and methods To evaluate the accuracy, 64 experimental single-needle cryoablations and 12 multi-needle cryoablations in gel phantoms were re-simulated with the corresponding freeze-thaw-freeze cycles. The simulated temperatures were compared over time with the measurements of thermocouples. For single needles, temperature values were compared at each thermocouple location. For multiple needles, Euclidean distances between simulated and measured isotherms (10 °C, 0 °C, −20 °C, −40 °C) were computed. Furthermore, surface and volume of simulated 0 °C isotherms were compared to cryoablation-induced ice balls in 14 kidney and 13 lung patients. For this purpose, needle positions and relevant anatomical structures defining material parameters (kidney/lung, tumor) were reconstructed from pre-ablation CT images and fused with postablation CT images (from which ice balls were extracted by manual delineation). Results The single-needle gel phantom cases showed less than 5 °C prediction error on average. Over all multiple needle experiments in gel, the mean and maximum isotherm distance were less than 2.3 mm and 4.1 mm, respectively. Average Dice coefficients of 0.82/0.63 (kidney/lung) and mean surface distances of 2.59/3.12 mm quantify the prediction performance of the numerical simulation. However, maximum surface distances of 10.57/10.8 mm indicate that locally larger errors have to be expected. Conclusion A very good agreement of the numerical simulations for gel experiments was measured and a satisfactory agreement of the numerical simulations with measured ice balls in patient data was shown