TH‐E‐218‐05: Prediction of Respiratory Motion from Single Daily 3D Image Using Prior Model of Motion and Anatomic Variations

Purpose: Respiratory motion may vary significantly from the planning stage to the time of treatment, leading to challenges in motion‐based treatment intervention. The purpose of this study was to develop a computational framework allowing for accurate prediction of daily respiratory motion from single 3D daily image under drastic inter‐fractional respiratory variations and anatomic changes, by taking advantage of prior knowledge of motion and anatomic relationship. Methods: Deformable image registration (DIR) was first performed across planning 4D‐CT to obtain a set of displacement vector field (DVF), which were further modeled by Principal Component analysis (PCA) to learn a prior motion model, namely a subspace spanned by principal bases. Subsequently, DIR was employed again across vowels of planning and daily images to calculate the DVF for inter‐fractional motion, which was used to spatially map the learned prior motion subspace (principal bases) onto the grid of daily image. Finally, we estimated the component of inter‐fractional respiratory variations by linear projection of inter‐fractional DVF onto mapped motion subspace. The resultant projections were utilized to compensate the reconstruction of daily respiratory motion. The reconstructed respiratory motion will be useful for various image‐guided treatment intervention strategies. Results: We applied the proposed framework for the prediction of respiratory motion from single daily CBCT and/or free‐breathing conventional CT using patients from a proton protocol undergoing weekly 4D‐CT evaluation. The accuracy of our model was visually and quantitatively confirmed by the excellent agreement between predicted contours by our model and physician approved ones. For a case with large tumor shrinkage, a DICE score of 90.2% has been achieved; even for another case with drastic respiratory motion difference (around 2cm) from planning stage, the agreement is also close to 90%. Conclusions: We have proposed and validated a novel framework to predict respiratory motion from single 3D daily image.