Personalized Respiratory Motion Model Using Conditional Generative Networks for MR-Guided Radiotherapy

MRI-guided radiotherapy systems enable real-time 2D cine acquisitions for target monitoring, but cannot provide volumetric information due to spatio-temporal constraints. Hence, respiratory motion models coupled with a temporal predictive mechanism are a suitable solution to enable ahead-of-time 3D tumor and anatomy tracking in combination with real-time online plan adaptation. We propose a novel subject-specific probabilistic model to enable 3D+t predictions from image-based surrogates during radiotherapy treatments. The model is trained end-to-end to simultaneously capture and learn a distribution of realistic motion fields over a population dataset. Furthermore, the distribution is conditioned on a sequence of partial observations, which can be extrapolated in time using a \( {seq2seq}\)-inspired mechanism allowing for scalable predictive horizon. Based on the generative properties of conditional variational autoencoders, it integrates anatomical features and temporal information to construct an interpretable latent space with respiratory phase discrimination. The choice of a probabilistic framework allows improving uncertainty estimation during the volume generation phase. Experimental validation on 25 subjects demonstrates the potential of the proposed model, which achieves a mean landmark error of \(1.4 \pm 1.1\) mm, yielding statistically significant improvements over state-of-the-art methods.