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

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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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Dates and versions

hal-02239318 , version 1 (13-08-2019)
hal-02239318 , version 2 (23-09-2019)

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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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