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Purpose: To quickly and automatically propagate organ contours from pretreatment to fraction images in magnetic resonance (MR)‐guided prostate external‐beam radiotherapy. Methods: Five prostate cancer patients underwent 20 fractions of image‐guided external‐beam radiotherapy on a 1.5 T MR‐Linac system. For each patient, a pretreatment T2‐weighted three‐dimensional (3D) MR imaging (MRI) scan was used to delineate the clinical target volume (CTV) contours. The same scan was repeated during each fraction, with the CTV contour being manually adapted if necessary. A convolutional neural network (CNN) was trained for combined image registration and contour propagation. The network estimated the propagated contour and a deformation field between the two input images. The training set consisted of a synthetically generated ground truth of randomly deformed images and prostate segmentations. We performed a leave‐one‐out cross‐validation on the five patients and propagated the prostate segmentations from the pretreatment to the fraction scans. Three variants of the CNN, aimed at investigating supervision based on optimizing segmentation overlap, optimizing the registration, and a combination of the two were compared to results of the open‐source deformable registration software package Elastix. Results: The neural networks trained on segmentation overlap or the combined objective achieved significantly better Hausdorff distances between predicted and ground truth contours than Elastix, at the much faster registration speed of 0.5 s. The CNN variant trained to optimize both the prostate overlap and deformation field, and the variant trained to only maximize the prostate overlap, produced the best propagation results. Conclusions: A CNN trained on maximizing prostate overlap and minimizing registration errors provides a fast and accurate method for deformable contour propagation for prostate MR‐guided radiotherapy. [ABSTRACT FROM AUTHOR]

To evaluate prostate intrafraction motion using MRI during the full course of online adaptive MR-Linac radiotherapy (RT) fractions, in preparation of MR-guided extremely hypofractionated RT. Five low and intermediate risk prostate cancer patients were treated with 20 × 3.1 Gy fractions on a 1.5T MR-Linac. Each fraction, initial MRI (Pre) scans were obtained at the start of every treatment session. Pre-treatment planning MRI contours were propagated and adapted to this Pre scan after which plan re-optimization was started in the treatment planning system followed by dose delivery. 3D Cine-MR imaging was started simultaneously with beam-on and acquired over the full beam-on period. Prostate intrafraction motion in this cine-MR was determined with a previously validated soft-tissue contrast based tracking algorithm. In addition, absolute accuracy of the method was determined using a 4D phantom. Prostate motion was completely automatically determined over the full on-couch period (approx. 45 min) with no identified mis-registrations. The translation 95% confidence intervals are within clinically applied margins of 5 mm, and plan adaption for intrafraction motion was required in only 4 out of 100 fractions. This is the first study to investigate prostate intrafraction motions during entire MR-guided RT sessions on an MR-Linac. We have shown that high quality 3D cine-MR imaging and prostate tracking during RT is feasible with beam-on. The clinically applied margins of 5 mm have proven to be sufficient for these treatments and may potentially be further reduced using intrafraction plan adaptation guided by cine-MR imaging. [ABSTRACT FROM AUTHOR]

Proffered Papers: Proffered papers 18: Intra-fractional motion management OC-0341: Seminal vesicle motion tracking in 3D cine-MR during MR-Linac prostate treatments D. De Muinck Keizer, T. Willigenburg, M.D. Den Hartogh, J.R.N. Van der Voort van Zyp, B.W. Raaymakers, J.J.W. Lagendijk, H.C.J. De Boer. [Extracted from the article]

Poster Highlights: Poster highlights 17 CL: Palliation PH-0529: Prostate intrafraction motion during the full course of MR-guided radiotherapy sessions D. De Muinck Keizer, L.G.W. Kerkmeijer, T. Willigenburg, M.D. Den Hartogh, J.R.N. Van der Voort van Zyp, E.N. De Groot-van Breugel, B.W. Raaymakers, J.J.W. Lagendijk, H.C.J. De Boer. [Extracted from the article]