Sparse Active Triangulation Grids for Respiratory Motion Management
- 1Lehrstuhl für Mustererkennung (Informatik 5), Friedrich-Alexander-Universität Erlangen-Nürnberg
- 2Institut für Optik, Information und Photonik, Friedrich-Alexander-Universität Erlangen-Nürnberg
sebastian [dot] bauer [at] informatik [dot] uni-erlangen [dot] de
The management of respiratory motion poses a challenge to a variety of medical applications. In radiation therapy, studies have shown that external body motion correlates with the internal tumor position. Based on a patient-specific model learnt prior to the first fraction, the tumor location can be inferred from an external surrogate during treatment, superseding intra-fractional radiographic imaging. Existing solutions rely on a single 1D respiratory surrogate and exhibit a limited level of accuracy due to inter-cycle variation. Recent work indicated that considering multiple regions would yield an enhanced model. Hence, we propose a novel solution based on a single-shot active triangulation sensor (Flying Triangulation, Ettl et al., Appl Opt 51 (2012) 281-289) that acquires a sparse grid of 3D measurement lines in real time, using two perpendicular laser line pattern projection systems. Building on non-rigid point cloud registration, the elastic displacement field representing the torso deformation with respect to a reference is recovered. This displacement field can then be used for model-based tumor tracking. We present measurement results and non-rigid displacement fields.
@inproceedings{bauer2012p23,
author = {Bauer, Sebastian and Ettl, Svenja and Wasza, Jakob and Willomitzer, Florian and Huber, Florian and Hornegger, Joachim and Häusler, Gerd},
title = {{Sparse Active Triangulation Grids for Respiratory Motion Management}},
booktitle = {DGaO-Proceedings 2012},
series = {DGaO-Proceedings},
eventtitle = {113. Jahrestagung der DGaO},
eventdate = {2012-05-30/2012-06-02},
venue = {Eindhoven},
publisher = {Deutsche Gesellschaft für angewandte Optik e.V.},
year = {2012},
pages = {P23},
langid = {english},
url = {https://dgao-proceedings.de/paper/113-p23}
}