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Probabilistic Motion Modeling from Medical Image Sequences: Application to Cardiac Cine-MRI

Abstract : We propose to learn a probabilistic motion model from a sequence of images. Besides spatio-temporal registration, our method offers to predict motion from a limited number of frames, useful for temporal super-resolution. The model is based on a probabilistic latent space and a novel temporal dropout training scheme. This enables simulation and interpolation of realistic motion patterns given only one or any subset of frames of a sequence. The encoded motion also allows to be transported from one subject to another without the need of inter-subject registration. An unsupervised generative deformation model is applied within a temporal convolutional network which leads to a diffeomorphic motion model, encoded as a low-dimensional motion matrix. Applied to cardiac cine-MRI sequences, we show improved registration accuracy and spatio-temporally smoother deformations compared to three state-of-the-art registration algorithms. Besides, we demonstrate the model's applicability to motion transport by simulating a pathology in a healthy case. Furthermore, we show an improved motion reconstruction from incomplete sequences compared to linear and cubic interpolation.
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Contributor : Julian Krebs Connect in order to contact the contributor
Submitted on : Monday, September 23, 2019 - 3:01:13 PM
Last modification on : Friday, November 18, 2022 - 9:26:50 AM
Long-term archiving on: : Sunday, February 9, 2020 - 7:24:28 AM


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  • HAL Id : hal-02239318, version 2
  • ARXIV : 1907.13524


Julian Krebs, Tommaso Mansi, Nicholas Ayache, Hervé Delingette. Probabilistic Motion Modeling from Medical Image Sequences: Application to Cardiac Cine-MRI. STACOM 2019 - 10th Workshop on Statistical Atlases and Computational Modelling of the Heart, Oct 2019, Shenzhen, China. ⟨hal-02239318v2⟩



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