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Publication Detail
Building surrogate-driven motion models from cone-beam CT via surrogate-correlated optical flow
  • Publication Type:
    Conference
  • Authors:
    Martin J, McClelland J, Champion B, Hawkes DJ
  • Publication date:
    01/01/2014
  • Pagination:
    61, 67
  • Published proceedings:
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
  • Volume:
    8498 LNCS
  • ISBN-13:
    9783319075204
  • Status:
    Published
  • Print ISSN:
    0302-9743
Abstract
An iterative approach to building a surrogate-driven motion model exclusively from cone-beam CT projections is presented. At each iteration the motion model is updated via an analytical expression derived from an optical flow-based approach, with corresponding improvements in the motion compensated reconstruction. The differences between the actual and estimated motion, as seen in the projections, are incorporated into a modified CBCT reconstruction. The correlations between these differences and the surrogate signals used in the motion model are also taken into account in determining the motion model updates. The updates are then composed with the previous estimate of the motion model and set as the new estimate of the motion model. New updates to this new estimate can then be calculated. The motion model could be used to better understand respiratory motion immediately prior to a fraction of radiotherapy treatment, or to monitor key regions of interest during tracked treatments. This method would also be a promising candidate to adapt an older model built during planning to the day of treatment. The local, voxel-wise updates to the model can account for large inter-fraction changes, specific to the day of treatment. Results on a simulated case are presented, derived from an actual patient dataset undergoing radiotherapy treatment for lung cancer. With the fitted motion, simulated projections of the animated patient volume were seen to be more similar to the actual projections than projections of the static patient volume. When compared with the actual motion, the mean L2-error over the entire patient was reduced to 0.46 mm. © 2014 Springer International Publishing Switzerland.
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