Deformable Image Registration Uncertainty Quantification Using Deep Learning for Dose Accumulation in Adaptive Proton Therapy
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Date
2022
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
Deformable image registration (DIR) is a key element in adaptive radiotherapy (AR) to include anatomical modifications in the adaptive planning. In AR, daily 3D images are acquired and DIR can be used for structure propagation and to deform the daily dose to a reference anatomy. Quantifying the uncertainty associated with DIR is essential. Here, a probabilistic unsupervised deep learning method is presented to predict the variance of a given deformable vector field (DVF). It is shown that the proposed method can predict the uncertainty associated with various conventional DIR algorithms for breathing deformation in the lung. In addition, we show that the uncertainty prediction is accurate also for DIR algorithms not used during the training. Finally, we demonstrate how the resulting DVFs can be used to estimate the dosimetric uncertainty arising from dose deformation.
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Publication status
published
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Book title
Biomedical Image Registration
Journal / series
Volume
13386
Pages / Article No.
57 - 66
Publisher
Springer
Event
10th International Workshop on Biomedical Image Registration (WBIR 2022)
Edition / version
Methods
Software
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Date collected
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Subject
Deformable image registration; Proton therapy; Adaptive planning; Uncertainty; Deep learning