Abstract
Purpose
Accurate and real-time prediction of the lung and lung tumor deformation during respiration are important considerations when performing a peripheral biopsy procedure. However, most existing work focused on offline whole lung simulation using 4D image data, which is not applicable in real-time image-guided biopsy with limited image resources. In this paper, we propose a patient-specific biomechanical model based on the boundary element method (BEM) computed from CT images to estimate the respiration motion of local target lesion region, vessel tree and lung surface for the real-time biopsy guidance.
Methods
This approach applies pre-computation of various BEM parameters to facilitate the requirement for real-time lung motion simulation. The resulting boundary condition at end inspiratory phase is obtained using a nonparametric discrete registration with convex optimization, and the simulation of the internal tissue is achieved by applying a tetrahedron-based interpolation method depend on expert-determined feature points on the vessel tree model. A reference needle is tracked to update the simulated lung motion during biopsy guidance.
Results
We evaluate the model by applying it for respiratory motion estimations of ten patients. The average symmetric surface distance (ASSD) and the mean target registration error (TRE) are employed to evaluate the proposed model. Results reveal that it is possible to predict the lung motion with ASSD of \(1.9\pm 0.8\) mm and a mean TRE of \(2.5\pm 2.1\) mm at largest over the entire respiratory cycle. In the CT-/electromagnetic-guided biopsy experiment, the whole process was assisted by our BEM model and final puncture errors in two studies were 3.1 and 2.0 mm, respectively.
Conclusion
The experiment results reveal that both the accuracy of simulation and real-time performance meet the demands of clinical biopsy guidance.
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Acknowledgements
This research is partially supported by the National Key research and development program (2016YFC0106200), 863 national research fund (2015AA043203), the Chinese NSFC research fund (61190120, 61190124 and 61271318) as well as Fujian Provincial Department of Science and Technology (2016Y0069). The authors thank the Léon Bérard Cancer Center & CREATIS laboratory for providing the 4DCT data.
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All procedures performed in studies involving human participants were in accordance with the 8 ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
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Chen, D., Chen, W., Huang, L. et al. BEM-based simulation of lung respiratory deformation for CT-guided biopsy. Int J CARS 12, 1585–1597 (2017). https://doi.org/10.1007/s11548-017-1603-8
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DOI: https://doi.org/10.1007/s11548-017-1603-8