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

## TL;DR

This paper introduces a probabilistic motion model for medical image sequences, enabling realistic motion prediction, interpolation, and transfer, with improved accuracy in cardiac cine-MRI compared to existing methods.

## Contribution

It presents a novel probabilistic latent space and temporal dropout training scheme for motion modeling, allowing motion simulation and transfer without inter-subject registration.

## Key findings

- Improved registration accuracy over state-of-the-art algorithms.
- Effective motion interpolation from incomplete sequences.
- Successful motion transfer demonstrating pathology simulation.

## 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.