Collaborative robot assisted smart needle placement

Needles are key tools to realize minimally invasive interventions. Physicians commonly rely on subjectively perceived insertion forces at the distal end of the needle when advancing the needle tip to the desired target. However, detecting tissue transitions at the distal end of the needle is difficult since the sensed forces are dominated by shaft forces. Disentangling insertion forces has the potential to substantially improve needle placement accuracy.We propose a collaborative system for robotic needle insertion, relaying haptic information sensed directly at the needle tip to the physician by haptic feedback through a light weight robot. We integrate optical fibers into medical needles and use optical coherence tomography to image a moving surface at the tip of the needle. Using a convolutional neural network, we estimate forces acting on the needle tip from the optical coherence tomography data. We feed back forces estimated at the needle tip for real time haptic feedback and robot control. When inserting the needle at constant velocity, the force change estimated at the tip when penetrating tissue layers is up to 94% between deep tissue layers compared to the force change at the needle handle of 2.36 %. Collaborative needle insertion results in more sensible force change at tissue transitions with haptic feedback from the tip (49.79 ± 25.51)% compared to the conventional shaft feedback (15.17 ± 15.92) %. Tissue transitions are more prominent when utilizing forces estimated at the needle tip compared to the forces at the needle shaft, indicating that a more informed advancement of the needle is possible with our system.

Gas System for the ATLAS NSW Micromegas Detectors: Design Aspects and Advanced Validation Methods for their QA/QC

In this work we present the design aspects of the Gas Distribution System of NSW Micromegas detectors, simulation results and gas flow / pressure uniformity. We also describe the appropriate gas leak test methods, a conventional and an alternative one, being used in the Quality Assurance and Quality Control of the detectors. For the performance studies we used emulated leak branches based on medical needles. We also describe proposed upgrade stages combining the proposed competitive Flow Rate Loss method with the Lock-in Amplifier technique. Further, we describe the baseline setup for the Gas Tightness Station at BB5/CERN.

A new method for evaluating the normal rake angle and inclination angle on medical needles

Hollow needles are the most frequently used medical equipment. The design of a hollow needle that best enables medical procedures requires a better understanding of needle tip geometry. Calculating the cutting angles of a needle for a complex surface topology is difficult. This article proposes a new method based on non-Euclidean geometry for the analysis of biopsy needle tip. The method can be used to calculate the cutting angles on any pipe needle. To verify the validity of this method, the normal rake angle and inclination angle on four types of needles (bias bevel needle, cylinder surface needle, curved surface needle and Cournand-type needle) were investigated. It was found that calculation of the cutting angles was simple and convenient using this method, especially for the curved surface needles. Images of the cutting angles from the Cournand-type needles revealed that the smaller bevel angle [Formula: see text] resulted in a higher normal rake angle [Formula: see text] and inclination angle [Formula: see text]. As [Formula: see text] increased, the range of the normal rake angle [Formula: see text] became larger at first and then became smaller.

Generation of Probabilistically Optimal Paths for Nonholonomic Needle Insertion

Rapidly-exploring Random Tree (RRT) is a sampling-based algorithm which is designed for path planning problems. It is efficient to handle high-dimensional configuration space (C-space) and nonholonomic constraints. Under the nonholonomic constraints, the RRT can generate paths between an initial state and a goal state while avoiding obstacles. Since this framework assumes that a system is deterministic, more improvement should be added when the method is applied to a system with uncertainty. In robotic systems with motion uncertainty, probability for successful targeting and obstacle avoidance are more suitable measurement than the deterministic distance between the robot system and the target position. In this paper, the probabilistic targeting error is defined as a root-mean-square (RMS) distance between the system to the desired target. The proximity of the obstacle to the system is also defined as an averaged distance of obstacles to the robotic system. Then, we consider a cost function that is a sum of the targeting error and the obstacle proximity. By numerically minimizing the cost, we can obtain the optimal path. In this paper, a method for efficient evaluation and minimization of this cost function is proposed and the proposed method is applied to nonholonomic flexible medical needles for performance tests.