A Kernel-based Framework for Intra-fractional Respiratory Motion Estimation in Radiation Therapy - Geimer, Tobias; Unberath, Mathias; Birlutiu, Adriana; Taubmann, Oliver; Wölfelschneider, Jens; Bert, Christoph; Maier, Andreas (Proceedings of the 2017 IEEE International Symposium on Biomedical Imaging, 2017)
In radiation therapy, tumor tracking allows to adjust the beam such that it follows the respiration-induced tumor motion. However, most clinical approaches rely on implanted fiducial markers to locate the tumor and, thus, only provide sparse information. Motion models have been investigated to estimate dense internal displacement fields from an external surrogate signal, such as range imaging. With increasing surrogate complexity, we propose a respiratory motion estimation framework based on kernel ridge regression to cope with high-dimensional domains. This approach was validated on five patient datasets, consisting of a planning 4DCT and a follow-up 4DCT for each patient. Mean residual error was at best 2.73 ± 0.25 mm, but varied greatly between patients.<br
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.akerne_4
Keywords: Motion Models; Range Imaging; Radiation Therapy; Kernel Trick; Active Shape Models

A Kernel Ridge Regression Model for Respiratory Motion Estimation in Radiotherapy - Geimer, Tobias; Birlutiu, Adriana; Unberath, Mathias; Taubmann, Oliver; Bert, Christoph; Maier, Andreas (Bildverarbeitung für die Medizin 2017 - Algorithmen - Systeme - Anwendungen, 2017)
This paper discusses a kernel ridge regression (KRR) model for motion estimation in radiotherapy. Using KRR, dense internal motion fields are estimated from high-dimensional surrogates without the need for prior dimensionality reduction. We compare the proposed model to a related approach with dimensionality reduction in the form of principal component analysis and principle component regression. Evaluation was performed in a simulation study based on nine 4D CT patient data sets achieving a mean estimation error of 0.84 ± 0.21 mm for our approach.<br
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.akerne_3