Real-time marker-free patient registration for electromagnetic navigated bronchoscopy: a phantom study
To perform bronchoscopy safely and smoothly, it is very important to develop a bronchoscopic guidance system. Transbronchial lung biopsy (TBLB) with a bronchoscopic guidance system especially should permit safe image-guided procedures. Recently, electromagnetic tracking (EMT) is utilized to track the tip of the bronchoscope camera in real time. For most tracking methods using position sensors, registration between tracking data and previously acquired reference image data, such as CT image, is performed using natural landmarks of the patient or fiducial markers attached to the patient, whose positions need to be measured manually by the physician before the actual bronchoscopy. Therefore, this paper proposes a marker-free CT-to-patient registration method utilizing bronchoscope’s position and orientation obtained by the EMT.
We developed a guidance system that is able to track the tip of the bronchoscope camera in real time. In the case of a guidance system that uses position sensors, natural landmarks of the patient or fiducial markers attached to the patient are needed to obtain the correspondence between EMT outputs and previously acquired reference image data, such as CT image. This paper proposes a registration method without landmarks or fiducials by estimating the transformation matrix between the patient and the CT image taken prior to the bronchoscopic examination. This estimation is performed by computing correspondences between the outputs of the EMT sensor and airways extracted from the CT image. As ambiguities between EMT measurements and their corresponding airway branches may arise at airway bifurcations, we introduce a stable airway branch selection mechanism for improving the robustness of the estimation of the transformation matrix. To evaluate the performance of the proposed method, we applied the method to a rubber bronchial phantom and added virtual breathing motion to the sensor output.
Experimental results show that the accuracy of our proposed method is within 2.0−3.0 mm (without breathing motion) and 2.5−3.5 mm (with breathing motion). The proposed method could also track a bronchoscope camera in real time.
We developed a method for CT-to-patient registration using a position sensor without fiducial markers and natural landmarks. Endoscopic guided biopsy of lung lesions is feasible using a marker-free CT-to-patient registration method.
KeywordsBronchoscopy Virtual bronchoscopy Position sensor Marker-free registration Motion recovery Tracking Camera tracking
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- 1.Vining DJ, Shitrin RY, Haponik EF, Liu K, Choplin RH (1994) Virtual bronchoscopy. Radiology, vol 193(P), Supplement to radiology (RSNA Scientific Program), p 261Google Scholar
- 2.Geiger B, Kikinis R (1995) Simulation of endoscopy, Computer vision, virtual reality and robotics in medicine, LNCS 905. Springer, pp 277–281Google Scholar
- 4.Mori K, Urano A, Hasegawa J, Toriwaki J, Anno H, Katada K (1996) Virtualized endoscope system—an application of virtual reality technology to diagnostic aid. IEICE Trans Inf Syst E79–D(6): 809–819Google Scholar
- 5.Hong L, Muraki S, Kaufman A, Bartz D, He T (1997) Virtual voyage: interactive navigation in the human colon. In: Proceedings of the 24th annual conference on computer graphics and interactive techniques (SIGGRAPH’97), pp 27–34Google Scholar
- 6.Rogalla, P, Terwisschavan Scheltinga, J, Hamm, B (eds) (2001) Virtual endoscopy and related 3D techniques. Springer, BerlinGoogle Scholar
- 7.Caramella, D, Bartolozzi, C (eds) (2002) 3D image processing—techniques and clinical application. Springer, BerlinGoogle Scholar
- 9.Helferty JP, Higgins WE (2001) Technique for registering 3D virtual CT images to endoscopic video. In: Proceedings of international conference on image processing (ICIP2001), pp 893–896Google Scholar
- 10.Merritt SA, Rai L, Higgins WE (2006) Real-time CT-video registration for continuous endoscopic guidance. In: Proceedings of SPIE medical imaging, vol 6143, pp 614313-1–614313-15Google Scholar
- 12.Schneider A, Hautmann H, Barfuss H, Pinkau T, Peltz F, Feussner H, Wichert A (2004) Real-time image tracking of a flexible bronchoscope. In: Proceedings of CARS2004, vol 1268, pp 753–757Google Scholar
- 14.Wegner I, Biederer J, Tetzlaff R, Wolf I, Meinzer H (2007) Evaluation and extension of a navigation system for bronchoscopy inside human lungs. In: Proceedings of SPIE medical imaging 2007, vol 6509, pp 65091H-1–65091H-12Google Scholar
- 15.Klein T, Traub J, Hautmann H, Ahmadian A, Navab N (2007) Fiducial-free registration procedure for navigated bronchoscopy. In: Proceedings of the 10th international conference on medical image computing and computer assisted intervention (MICCAI2007), Part I, LNCS, vol 4791, pp 475–482Google Scholar
- 16.Deguchi D, Ishitani K, Kitasaka T, Mori K, Suenaga Y, Takabatake H, Mori M, Natori H (2007) A method for bronchoscope tracking using position sensor without fiducial markers. In: Proceedings of SPIE medical imaging 2007, vol 6511, pp 65110N-1–65110N-12Google Scholar
- 17.Mori K, Deguchi D, Ishitani K, Kitasaka T, Suenaga Y, Hasegawa Y, Imaizumi K, Takabatake H (2007) Bronchoscope tracking without fiducial markers using ultra-tiny electromagnetic tracking system and its evaluation in different environments. In: Proceedings of the 10th international conference on medical image computing and computer assisted intervention (MICCAI2007), Part II, LNCS vol 4792, pp 644–651Google Scholar
- 18.Mori K, Deguchi D, Kitasaka T, Suenaga Y, Hasegawa Y, Imaizumi K, Takabatake H (2008) Improvement of accuracy of marker-free bronchoscope tracking using electromagnetic tracker based on bronchial branch information. In: Proceedings of the 11th international conference on medical image computing and computer assisted intervention (MICCAI2008), Part II, LNCS, vol 5242, pp 535–542Google Scholar
- 20.Tsai RY, Lenz RK (1988) Real time versatile robotics hand/eye calibration using 3D machine vision. In: Proceedings of 1988 IEEE international conference on robotics and automation, pp 554–561Google Scholar
- 22.Kitasaka T, Mori K, Hasegawa J, Toriwaki J (2002) A method for extraction of bronchus regions from 3D chest X-ray CT images by analyzing structural features of the bronchus. FORMA 17(4): 321–338Google Scholar
- 23.Feuerstein M, Kitasaka T, Mori K (2009) Adaptive branch tracing and image sharpening for airway tree extraction in 3-D chest CT. In: Proceedings of second international workshop on pulmonary image analysis, pp 273–284Google Scholar
- 24.Press WH, Teukolsky SA, Vetterling WT, Flannery BP (1999) Numerical recipes in C, the art of scientific computing second edition. Cambridge University Press, Cambridge, pp 321–336Google Scholar
- 25.Soper TD, Haynora DR, Glenny RW, Seibela EJ (2007) A model of respiratory airway motion for real-time tracking of an ultrathin bronchoscope. In: Proceedings of SPIE medical imaging 2007, vol 6511, pp 65110M-1–65110M-12Google Scholar