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.
|Title of host publication||Medical Imaging 2013: Image Processing|
|Editors||David R. Haynor, Sebastien Ourselin|
|Number of pages||9|
|Publication status||Published - 13.03.2013|
|Event||Image Processing, SPIE Medical Imaging 2013|
- Lake Buena Vista (Orlando Area), United States
Duration: 09.02.2013 → 14.02.2013