Passive magnetic-based localization for precise untethered medical instrument tracking. (March 2018)

• Record Type: Journal Article
• Authors: Sun, Zhenglong; Maréchal, Luc; Foong, Shaohui
• Is Part Of: Computer methods and programs in biomedicine. Volume 156(2018)
• Page Start: 151
• Page End: 161
• Publication Date: 2018-03
• Subjects: Medical intervention -- Artificial neural networks -- Magnetic-field based localization -- Instrument tracking; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.12.018
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Passive magnetic tracking technology to enable untethered medical instrument tracking with minimal modifications to the existing routine. Investigating the efficacy in tracking using two practical medical interventions as motivating examples. Two different methods were compared and modelling selection strategy was discussed. Abstract: Background and objective: Motion tracking and navigation systems are paramount for both safety and efficacy in a variety of surgical insertions, interventions and procedures. Among the state-of-art tracking technology, passive magnetic tracking using permanent magnets or passive magnetic sources for localization is an effective technology to provide untethered medical instrument tracking without cumbersome wires needed for signal or power transmission. Motivated by practical needs in two medical insertion procedures: Nasogastric intubation and Ventriculostomy, we propose a unified method based on passive magnetic-field localization, for enhanced efficacy and safety. Methods: Traditional approaches to passive magnetic tracking involve solving the inverse localization problem. Limited by the idealistic magnetic field dipole model and computationally intense nonlinear optimization algorithm, the overall accuracy and computational cost are greatly compromised. The method introduced here features direct localization with artificial neural network (ANN) models that bypasses the need to resolve the inverse problem and is adaptable for a variety of real-time localization and tracking applications. Results: The efficiency of the two methods, the inverse optimization method and the direct ANN method are experimentally evaluated by comparing the estimated position of reference trajectories for typical nasogastric and ventriculostomy insertion paths performed by a dexterous robotic arm which provides ground truth measurement. It was found that within the region of interest (ROI), the direct ANN technique could significantly improve the localization accuracy, with an average experimental localization error of less than 2 mm, while that of the traditional inverse optimization method using a dipole-based mathematical model at greater than 5 mm. Ex-vivo experiments were performed to validate the localization methods in clinical settings. Conclusions: While the proposed method for passive magnetic tracking requires a procedure-specific pre-procedural calibration, it is able to provide real-time tracking with high accuracy, robustness and diversity. It could be the missing piece to the puzzle to bring passive magnetic tracking technology into practice, therefore leading to untethered medical instrument tracking.