Physics-based shape matching for intraoperative image guidance
Purpose: Soft-tissue deformations can severely degrade the validity of preoperative planning data during computer assisted interventions. Intraoperative imaging such as stereo endoscopic, time-of-flight or, laser range scanner data can be used to compensate these movements. In this context, the intr...
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| Other Authors: | , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
2014
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| In: |
Medical physics
Year: 2014, Volume: 41, Issue: 11, Pages: 111901-1-111901-12 |
| ISSN: | 2473-4209 |
| DOI: | 10.1118/1.4896021 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1118/1.4896021 Verlag, kostenfrei, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1118/1.4896021 
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| Author Notes: | Stefan Suwelack, Sebastian Röhl, Sebastian Bodenstedt, Daniel Reichard, Rüdiger Dillmann, Thiago dos Santos, Lena Maier-Hein, Martin Wagner, Josephine Wünscher, Hannes Kenngott, Beat P. Müller and Stefanie Speidel |
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| 245 | 0 | 0 | |a Physics-based shape matching for intraoperative image guidance |c Stefan Suwelack, Sebastian Röhl, Sebastian Bodenstedt, Daniel Reichard, Rüdiger Dillmann, Thiago dos Santos, Lena Maier-Hein, Martin Wagner, Josephine Wünscher, Hannes Kenngott, Beat P. Müller and Stefanie Speidel |
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| 520 | |a Purpose: Soft-tissue deformations can severely degrade the validity of preoperative planning data during computer assisted interventions. Intraoperative imaging such as stereo endoscopic, time-of-flight or, laser range scanner data can be used to compensate these movements. In this context, the intraoperative surface has to be matched to the preoperative model. The shape matching is especially challenging in the intraoperative setting due to noisy sensor data, only partially visible surfaces, ambiguous shape descriptors, and real-time requirements. Methods: A novel physics-based shape matching (PBSM) approach to register intraoperatively acquired surface meshes to preoperative planning data is proposed. The key idea of the method is to describe the nonrigid registration process as an electrostatic-elastic problem, where an elastic body (preoperative model) that is electrically charged slides into an oppositely charged rigid shape (intraoperative surface). It is shown that the corresponding energy functional can be efficiently solved using the finite element (FE) method. It is also demonstrated how PBSM can be combined with rigid registration schemes for robust nonrigid registration of arbitrarily aligned surfaces. Furthermore, it is shown how the approach can be combined with landmark based methods and outline its application to image guidance in laparoscopic interventions. Results: A profound analysis of the PBSM scheme based on in silico and phantom data is presented. Simulation studies on several liver models show that the approach is robust to the initial rigid registration and to parameter variations. The studies also reveal that the method achieves submillimeter registration accuracy (mean error between 0.32 and 0.46 mm). An unoptimized, single core implementation of the approach achieves near real-time performance (2 TPS, 7-19 s total registration time). It outperforms established methods in terms of speed and accuracy. Furthermore, it is shown that the method is able to accurately match partial surfaces. Finally, a phantom experiment demonstrates how the method can be combined with stereo endoscopic imaging to provide nonrigid registration during laparoscopic interventions. Conclusions: The PBSM approach for surface matching is fast, robust, and accurate. As the technique is based on a preoperative volumetric FE model, it naturally recovers the position of volumetric structures (e.g., tumors and vessels). It cannot only be used to recover soft-tissue deformations from intraoperative surface models but can also be combined with landmark data from volumetric imaging. In addition to applications in laparoscopic surgery, the method might prove useful in other areas that require soft-tissue registration from sparse intraoperative sensor data (e.g., radiation therapy). | ||
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