Intraoperative localized constrained registration in navigated bronchoscopy

Purpose: One of the major challenges in electromagnetic navigated bronchoscopy is the navigation accuracy. An initial rigid image‐to‐patient registration may not be optimal for the entire lung volume, as the lung tissue anatomy is likely to have shifted since the time of computer tomography (CT) acquisition. The accuracy of the initial rigid registration will also be affected throughout the procedure by breathing, coughing, patient movement and tissue displacements due to pressure from bronchoscopy tools. A method to minimize the negative impact from these factors by updating the registration locally during the procedure is needed and suggested in this paper. Methods: The intraoperative local registration method updates the initial registration by optimization in an area of special interest, for example, close to a biopsy position. The local registration was developed through an adaptation of a previously published registration method used for the initial registration of CT to the patient anatomy. The method was tested in an experimental breathing phantom setup, where respiratory movements were induced by a robotic arm. Deformations were also applied to the phantom to see if the local registration could compensate for these. Results: The local registration was successfully applied in all 15 repetitions, five in each of the three parts of the airway phantom. The mean registration accuracy was improved from 11.8–19.4 mm to 4.0–6.7 mm, varying to some degree in the different segments of the airway model. Conclusions: A local registration method, to update and improve the initial image‐to patient registration during navigated bronchoscopy, was developed. The method was successfully tested in a breathing phantom setup. Further development is needed to make the method more automatic. It must also be verified in human studies.

[1]  Daisuke Deguchi,et al.  A method for bronchoscope tracking using position sensor without fiducial markers , 2007, SPIE Medical Imaging.

[2]  Douglas C McCrory,et al.  Performance characteristics of different modalities for diagnosis of suspected lung cancer: summary of published evidence. , 2003, Chest.

[3]  Hans-Peter Meinzer,et al.  Evaluation and extension of a navigation system for bronchoscopy inside human lungs , 2007, SPIE Medical Imaging.

[4]  Takayuki Kitasaka,et al.  Externally Navigated Bronchoscopy Using 2-D Motion Sensors: Dynamic Phantom Validation , 2013, IEEE Transactions on Medical Imaging.

[5]  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.

[6]  Hiroshi Murase,et al.  Real-time marker-free patient registration for electromagnetic navigated bronchoscopy: a phantom study , 2011, International Journal of Computer Assisted Radiology and Surgery.

[7]  Berthold K. P. Horn,et al.  Closed-form solution of absolute orientation using unit quaternions , 1987 .

[8]  Lei Dong,et al.  Dynamic lung modeling and tumor tracking using deformable image registration and geometric smoothing. , 2012 .

[9]  Erlend Fagertun Hofstad,et al.  Automatic registration of CT images to patient during the initial phase of bronchoscopy: a clinical pilot study. , 2014, Medical physics.

[10]  Xiangjian He,et al.  Adaptive marker-free registration using a multiple point strategy for real-time and robust endoscope electromagnetic navigation , 2015, Comput. Methods Programs Biomed..

[11]  Daisuke Deguchi,et al.  Marker-Free Registration for Electromagnetic Navigation Bronchoscopy under Respiratory Motion , 2010, MIAR.

[12]  R. Yung Tissue diagnosis of suspected lung cancer: selecting between bronchoscopy, transthoracic needle aspiration, and resectional biopsy. , 2003, Respiratory care clinics of North America.

[13]  Lei Dong,et al.  Dynamic lung modeling and tumor tracking using deformable image registration and geometric smoothing , 2012, CompIMAGE.

[14]  William E. Higgins,et al.  Interactive CT-Video Registration for the Continuous Guidance of Bronchoscopy , 2013, IEEE Transactions on Medical Imaging.

[15]  T. Langø,et al.  Bronchoscope-induced Displacement of Lung Targets: First In Vivo Demonstration of Effect From Wedging Maneuver in Navigated Bronchoscopy , 2013, Journal of bronchology & interventional pulmonology.

[16]  Kensaku Mori,et al.  Robust Endoscope Motion Estimation Via an Animated Particle Filter for Electromagnetically Navigated Endoscopy , 2014, IEEE Transactions on Biomedical Engineering.

[17]  Erlend Fagertun Hofstad,et al.  Liver deformation in an animal model due to pneumoperitoneum assessed by a vessel-based deformable registration , 2014, Minimally invasive therapy & allied technologies : MITAT : official journal of the Society for Minimally Invasive Therapy.

[18]  F. Lindseth,et al.  Navigated Bronchoscopy: A Technical Review , 2014, Journal of bronchology & interventional pulmonology.

[19]  F. Asano Application and Limitations of Virtual Bronchoscopic Navigation , 2012 .

[20]  Erlend Fagertun Hofstad,et al.  CustusX: an open-source research platform for image-guided therapy , 2015, International Journal of Computer Assisted Radiology and Surgery.

[21]  Daisuke Deguchi,et al.  Improvement of accuracy of marker-free bronchoscope tracking using electromagnetic tracker based on bronchial branch information. , 2008, Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention.

[22]  Frank Lindseth,et al.  GPU-Based Airway Segmentation and Centerline Extraction for Image Guided Bronchoscopy , 2012 .