Respiratory motion introduces uncertainties when planning and delivering radiotherapy for lung cancer patients. Cone-beam projections acquired in the treatment room could provide valuable information for building motion models, useful for gated treatment delivery or motion compensated reconstruction. We propose a method for estimating 3D+T respiratory motion from the 2D+T cone-beam projection sequence by including prior knowledge about the patient's breathing motion. Motion estimation is accomplished by maximizing the similarity of the projected view of a patient specific model to observed projections of the cone-beam sequence. This is done semi-globally, considering entire breathing cycles. Using realistic patient data, we show that the method is capable of good prediction of the internal patient motion from cone-beam data, even when confronted with interfractional changes in the breathing motion. © 2009 Springer-Verlag.
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Vandemeulebroucke, J., Kybic, J., Clarysse, P., & Sarrut, D. (2009). Respiratory motion estimation from cone-beam projections using a prior model. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5762 LNCS, pp. 365–372). https://doi.org/10.1007/978-3-642-04271-3_45