Robust bronchoscope motion tracking using sequential Monte Carlo methods in navigated bronchoscopy: dynamic phantom and patient validation
- 242 Downloads
Accurate and robust estimates of camera position and orientation in a bronchoscope are required for navigation. Fusion of pre-interventional information (e.g., CT, MRI, or US) and intra-interventional information (e.g., bronchoscopic video) were incorporated into a navigation system to provide physicians with an augmented reality environment for bronchoscopic interventions.
Two approaches were used to predict bronchoscope movements by incorporating sequential Monte Carlo (SMC) simulation including (1) image-based tracking techniques and (2) electromagnetic tracking (EMT) methods. SMC simulation was introduced to model ambiguities or uncertainties that occurred in image- and EMT-based bronchoscope tracking. Scale invariant feature transform (SIFT) features were employed to overcome the limitations of image-based motion tracking methods. Validation was performed on five phantom and ten human case datasets acquired in the supine position.
For dynamic phantom validation, the EMT–SMC simulation method improved the tracking performance of the successfully registered bronchoscopic video frames by 12.7% compared with a hybrid-based method. In comparisons between tracking results and ground truth, the accuracy of the EMT–SMC simulation method was 1.51 mm (positional error) and 5.44° (orientation error). During patient assessment, the SIFT–SMC simulation scheme was more stable or robust than a previous image-based approach for bronchoscope motion estimation, showing 23.6% improvement of successfully tracked frames. Comparing the estimates of our method to ground truth, the position and orientation errors are 3.72 mm and 10.2°, while those of our previous image-based method were at least 7.77 mm and 19.3°. The computational times of our EMT– and SIFT–SMC simulation methods were 0.9 and 1.2 s per frame, respectively.
The SMC simulation method was developed to model ambiguities that occur in bronchoscope tracking. This method more stably and accurately predicts the bronchoscope camera position and orientation parameters, reducing uncertainties due to problematic bronchoscopic video frames and airway deformation during intra-bronchoscopy navigation.
KeywordsSequential Monte Carlo methods Bronchoscope motion tracking Virtual bronchoscopy Navigated bronchoscopy
Unable to display preview. Download preview PDF.
- 1.American Cancer Society (2010) Global cancer facts & figures 2010, American Cancer Society, Atlanta. http://www.cancer.org/Research/CancerFactsFigures
- 2.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 E 79-D(6): 809–819Google Scholar
- 13.Muller SA, Maier-Hein L, Tekbas A, Seitel A, Ramsauer S, Radeleff B, Franz AM, Tetzlaff R, Mehrabi A, Wolf I, Kauczor H-U, Meinzer H-P, Schmied BM (2010) Navigated liver biopsy using a novel soft tissue navigation system versus CT-guided liver biopsy in a porcine model: a prospective randomized trial. Acad Radiol 17: 1282–1287PubMedCrossRefGoogle Scholar
- 14.Mori K, Deguchi D, Akiyama K, Kitasaka T, Maurer CR Jr, Suenaga Y, Takabatake H, Mori M, Natori H (2005) Hybrid bronchoscope tracking using a magnetic tracking sensor and image registration. In: Proceedings of MICCAI 2005, vol LNCS 3750. pp 543–550Google Scholar
- 18.Luo X, Reichl T, Feuerstein M, Kitasaka T, Mori K (2010) Modified hybrid bronchoscope tracking based on sequential monte carlo sampler: dynamic phantom validation. In: Proceedings of ACCV 2010, vol 3. pp 1722–1733Google Scholar
- 20.Doucet A, de Freitas N, Gordon N (2001) Sequential Monte Carlo methods in practice. Springer, BerlinGoogle Scholar
- 21.Liu JS, Chen R (1998) Sequential Monte Carlo methods for dynamic systems. J Am Stat Assoc 93(443): 1032–1044Google Scholar
- 25.Hanson AJ (2006) Visualizing quaternions. Morgan-Kaufmann/ Elsevier, Los AltosGoogle Scholar
- 27.Luo X, Feuerstein M, Deguchi D, Kitasaka T, Takabatake H, Mori K Development and comparison of new hybrid motion tracking for bronchoscopic navigation. Med Image Anal (in press)Google Scholar
- 28.Mori K, Suenaga Y, Toriwaki J (2003) Fast software-based volume rendering using multimedia instructions on PC platforms and its application to virtual endoscopy. In: Proceedings of SPIE, vol 5031. pp 111–122Google Scholar
- 29.Luo X, Feuerstein M, Sugiura T, Kitasaka T, Imaizumi K, Hasegawa Y, Mori K (2010) Towards hybrid bronchoscope tracking under respiratory motion: evaluation on a dynamic motion phantom. In: Proceedings of SPIE, vol 7625. p 76251BGoogle Scholar
- 30.Schneider M, Stevens C (2007) Development and testing of a new magnetic-tracking device for image guidance. In: Proceedings of SPIE, vol 6509. p 65090IGoogle Scholar