Can coffee improve image guidance?

Anecdotally, surgeons sometimes observe large errors when using image guidance in endonasal surgery. We hypothesize that one contributing factor is the possibility that operating room personnel might accidentally bump the optically tracked rigid body attached to the patient after registration has been performed. In this paper we explore the registration error at the skull base that can be induced by simulated bumping of the rigid body, and find that large errors can occur when simulated bumps are applied to the rigid body. To address this, we propose a new fixation method for the rigid body based on granular jamming (i.e. using particles like ground coffee). Our results show that our granular jamming fixation prototype reduces registration error by 28%-68% (depending on bump direction) in comparison to a standard Brainlab reference headband. © 2015 SPIE

Soft Robotics. Bio-inspired Antagonistic Stiffening

Soft robotic structures might play a major role in the 4th industrial revolution. Researchers have demonstrated advantages of soft robotics over traditional robots made of rigid links and joints in several application areas including manufacturing, healthcare, and surgical interventions. However, soft robots have limited ability to exert larger forces and change their stiffness on demand over a wide range. Stiffness can be achieved as a result of the equilibrium of an active and a passive reaction force or of two active forces antagonistically collaborating. This paper presents a novel design paradigm for a fabric-based Variable Stiffness System including potential applications

modeling the laser ablation process

This chapter focuses on the problem of modeling the laser ablation process from a geometrical point of view. The objective is to create a model capable of describing the laser incision depth based on the knowledge of the laser parameters and inputs. The discussion starts with a statement of the problem, which is defined in terms of a supervised regression. Our approach is compared with existing heuristic models for the prediction of ablation depth

An Analytical Formulation for the Geometrico-static Problem of Continuum Planar Parallel Robots

International audienceIn this paper, we provide an analytical formulation for the geometrico-static problem of continuum planar parallel robots. This formulation provides to an analytical computation of a set of equations governing the equilibrium configurations. We also introduce a stability criterion of the computed configurations. This formulation is based on the use of Kirchhoff's rod deformation theory and finite-difference approximations. Their combination leads to a quadratic expression of the rod's deformation energy. Equilibrium configurations of a planar parallel robot composed of two hinged flexible rods are computed according to this new formulation and compared with the ones obtained with state-of-the-art approaches. By assessing equilibrium stability with the proposed technique, new unstable configurations are determined

Preoperative Planning for Guidewires Employing Shape-Regularized Segmentation and Optimized Trajectories

Upcoming robotic interventions for endovascular procedures can significantly reduce the high radiation exposure currently endured by surgeons. Robotically driven guidewires replace manual insertion and leave the surgeon the task of planning optimal trajectories based on segmentation of associated risk structures. However, such a pipeline brings new challenges. While Deep learning based segmentation such as U-Net can achieve outstanding Dice scores, it fails to provide suitable results for trajectory planning in annotation scarce environments. We propose a preoperative pipeline featuring a shape regularized U-Net that extracts coherent anatomies from pixelwise predictions. It uses Rapidly-exploring Random Trees together with convex optimization for locally optimal planning. Our experiments on two publicly available data sets evaluate the complete pipeline. We show the benefits of our approach in a functional evaluation including both segmentation and planning metrics: While we achieve comparable Dice, Hausdorff distances and planning metrics such as success rate of motion planning algorithms are significantly better than U-Net