Fast deformable image registration uncertainty estimation for contour propagation in daily adaptive proton therapy

Andreas Smolders, Florian Amstutz, Ye Zhang, Damien Charles Weber, Tony Lomax, Francesca Albertini

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In daily adaptive proton therapy, deformable image registration (DIR) can be used to propagate manually delineated contours from a reference CT to the daily CT for plan reoptimization. However, the ill-posedness of DIR implies uncertainty on the DIR hyperparameters, which results in uncertainty in the displacement field. In this work, a fast deep learning method is developed to predict the uncertainty associated with a DIR result without the need for Monte-Carlo (MC) sampling. It is shown that this results in a significant time reduction compared to MC whilst leading to similar probabilistic contours.
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Friday 8th July
Poster Session 3.1 - onsite 15:20 - 16:20, virtual 11:00 - 12:00 (UTC+2)
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