Respiratory motion is a major source of error in radiation treatment of thoracic and abdominal tumors. State-of-the-art motion-adaptive radiation therapy techniques are usually guided by external breathing signals acting as surrogates for the internal motion of organs and tumors. Assuming a relationship between the surrogate measurements and the internal motion patterns, which are usually described by non-linear transformations, correspondence models can be defined and used for surrogate-based motion estimation. In this contribution, a diffeomorphic motion estimation framework based on standard multivariate linear regression is extended by subspace-based approaches like principal component analysis, partial least squares, and canonical correlation analysis. These methods aim at exploiting the hidden structure of the training data to improve the use of the information provided by high-dimensional surrogate and internal motion representations. A quantitative evaluation carried out on 4D CT data sets of 10 lung tumor patients shows that subspace-based approaches are able to significantly improve the mean estimation accuracy when compared to standard multivariate linear regression.

Date Published: 13 March 2013
PDF: 8 pages
Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 866915 (13 March 2013); doi: 10.1117/12.2002428

Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)