Robust Endoscope Motion Estimation Via an Animated Particle Filter for Electromagnetically Navigated Endoscopy

Electromagnetically navigated endoscopy, which is increasingly applied in endoscopic interventions, utilizes an electromagnetic sensor attached at the endoscope tip to measure the endoscope movements and to navigate the endoscope in the region of interest in the body. Due to patient motion and magnetic field distortion, sensor electromagnetic tracking (EMT) measurement inaccuracy and dynamic jitter errors remain challenging for electromagnetic navigation. This paper proposes a new tracking framework of an animated particle filter that integrates adaptive particle swarm optimization into a generic particle filter to significantly boost electromagnetic trackers. We validate our method on a dynamic phantom and compare it to state-of-the-art EMT methods. Our experimental results demonstrate the effectiveness and robustness of our method, which provides position and orientation accuracy of 2.48 mm, 4.69° that significantly outperformed previous methods at least with tracking error of 4.19 mm, 7.75°. The tracking smoothness was improved from 4.09 mm, 3.37° to 1.84 mm, 2.52°. Our method successfully tackled the particle impoverishment better than standard particle filters.

[1]  Lena Maier-Hein,et al.  Particle filtering for respiratory motion compensation during navigated bronchoscopy , 2010, Medical Imaging.

[2]  Xiaodong Li,et al.  This article has been accepted for inclusion in a future issue. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 1 Locating and Tracking Multiple Dynamic Optima by a Particle Swarm Model Using Speciation , 2022 .

[3]  Nassir Navab,et al.  Magneto-Optical Tracking of Flexible Laparoscopic Ultrasound: Model-Based Online Detection and Correction of Magnetic Tracking Errors , 2009, IEEE Transactions on Medical Imaging.

[4]  Ziv Yaniv,et al.  Needle-Based Interventions With the Image-Guided Surgery Toolkit (IGSTK): From Phantoms to Clinical Trials , 2010, IEEE Transactions on Biomedical Engineering.

[5]  Daisuke Deguchi,et al.  Hybrid Bronchoscope Tracking Using a Magnetic Tracking Sensor and Image Registration , 2005, MICCAI.

[6]  Zhengrong Liang,et al.  3D virtual colonoscopy , 1995, Proceedings 1995 Biomedical Visualization.

[7]  Takayuki Kitasaka,et al.  Towards hybrid bronchoscope tracking under respiratory motion: evaluation on a dynamic motion phantom , 2010, Medical Imaging.

[8]  Eric J. Seibel,et al.  In Vivo Validation of a Hybrid Tracking System for Navigation of an Ultrathin Bronchoscope Within Peripheral Airways , 2010, IEEE Transactions on Biomedical Engineering.

[9]  Pheng-Ann Heng,et al.  Interactive Visualization for Rapid Noninvasive Cardiac Assessment , 1996, Computer.

[10]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[11]  Daisuke Deguchi,et al.  Selective image similarity measure for bronchoscope tracking based on image registration , 2009, Medical Image Anal..

[12]  Luis Serra,et al.  The brain bench planner and trainer for minimal access surgery , 1996, VRST.

[13]  Gorka Bastarrika,et al.  Diagnostic yield of electromagnetic navigation bronchoscopy is highly dependent on the presence of a Bronchus sign on CT imaging: results from a prospective study. , 2010, Chest.

[14]  Takayuki Kitasaka,et al.  Robust bronchoscope motion tracking using sequential Monte Carlo methods in navigated bronchoscopy: dynamic phantom and patient validation , 2011, International Journal of Computer Assisted Radiology and Surgery.

[15]  A. Bovik,et al.  A universal image quality index , 2002, IEEE Signal Processing Letters.

[16]  G. Klein,et al.  Parallel Tracking and Mapping for Small AR Workspaces , 2007, 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality.

[17]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[18]  Tao Mei,et al.  Visual search reranking via adaptive particle swarm optimization , 2011, Pattern Recognit..

[19]  Daisuke Deguchi,et al.  Development and comparison of new hybrid motion tracking for bronchoscopic navigation , 2012, Medical Image Anal..

[20]  Andrew J. Davison,et al.  DTAM: Dense tracking and mapping in real-time , 2011, 2011 International Conference on Computer Vision.

[21]  Jackrit Suthakorn,et al.  Non-radiological colonoscope tracking image guided colonoscopy using commercially available electromagnetic tracking system , 2010, 2010 IEEE Conference on Robotics, Automation and Mechatronics.

[22]  Kensaku Mori,et al.  Fast software-based volume rendering using multimedia instructions on PC platforms and its application to virtual endoscopy , 2003, SPIE Medical Imaging.

[23]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[24]  Armin Ernst,et al.  Lesions Using Overlaid CT Images : The First Human Study Real-Time Electromagnetic Navigation Bronchoscopy to Peripheral Lung , 2006 .

[25]  Kevin Cleary,et al.  Implementation of an electromagnetic tracking system for accurate intrahepatic puncture needle guidance: accuracy results in an in vitro model. , 2007, Academic radiology.

[26]  William E. Higgins,et al.  Computer-based system for the virtual-endoscopic guidance of bronchoscopy , 2007, Comput. Vis. Image Underst..

[27]  Takayuki Kitasaka,et al.  Endoscope 3-D motion tracking using an aggressive particle filtering for boosting electromagnetic guidance endoscopy , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[28]  Amit Konar,et al.  Particle Swarm Optimization and Differential Evolution Algorithms: Technical Analysis, Applications and Hybridization Perspectives , 2008, Advances of Computational Intelligence in Industrial Systems.

[29]  Caroline Hayhurst,et al.  Application of electromagnetic technology to neuronavigation: a revolution in image-guided neurosurgery. , 2009, Journal of neurosurgery.

[30]  Volodymyr V. Kindratenko,et al.  A survey of electromagnetic position tracker calibration techniques , 2005, Virtual Reality.

[31]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[32]  Guang-Zhong Yang,et al.  Nonrigid 2-D/3-D Registration for Patient Specific Bronchoscopy Simulation With Statistical Shape Modeling: Phantom Validation , 2006, IEEE Transactions on Medical Imaging.