Joint tof image denoising and registration with a CT surface in radiation therapy

Authors:
Sebastian Bauer;Benjamin Berkels;Joachim Hornegger;Martin Rumpf
Affiliations:
Pattern Recognition Lab, Dept. of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany;Interdisciplinary Mathematics Inst., University of South Carolina, Columbia, SC;Pattern Recognition Lab, Dept. of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany;Inst. for Numerical Simulation, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
Venue:
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
Year:
2011

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Abstract

The management of intra-fractional respiratory motion is becoming increasingly important in radiation therapy. Based on in advance acquired accurate 3D CT data and intra-fractionally recorded noisy time-of-flight (ToF) range data an improved treatment can be achieved. In this paper, a variational approach for the joint registration of the thorax surface extracted from a CT and a ToF image and the denoising of the ToF image is proposed. This enables a robust intra-fractional full torso surface acquisition and deformation tracking to cope with variations in patient pose and respiratory motion. Thereby, the aim is to improve radiotherapy for patients with thoracic, abdominal and pelvic tumors. The approach combines a Huber norm type regularization of the ToF data and a geometrically consistent treatment of the shape mismatch. The algorithm is tested and validated on synthetic and real ToF/CT data and then evaluated on real ToF data and 4D CT phantom experiments.