AI Insight
This study compares four methods for learning motion priors in implicit neural representations (INRs) to improve cardiac motion estimation from tagged MRI images. Using UK Biobank data, researchers found that all learned priors significantly improved early adaptation performance compared to random initialization, with meta-learning achieving the best overall adaptation trajectory over 50 iterations. Auto-decoders showed particular strength in recovering large deformations quickly during early adaptation stages.
Why it matters
This work could accelerate cardiac imaging analysis by reducing the time and computational resources needed to estimate heart motion from medical scans. Improved cardiac motion tracking has direct applications in diagnosing heart disease and monitoring cardiac function in clinical settings.
Understand the Science
arXiv:2607.00955v2 Announce Type: replace-cross
Abstract: Implicit neural representations (INRs) are well suited to cardiac motion estimation, providing continuous, compact representations of motion fields. However, fitting an INR to each image sequence is time-consuming and sensitive to the optimisation trajectory. Learned priors can help guide optimisation towards plausible motion fields and enable faster adaptation, but learning priors for cardiac motion INRs remains under-explored. In this work, we compare four strategies for learning cardiac motion priors, including a population prior learned by joint optimisation, a consensus prior obtained by weight averaging, auto-decoders, and meta-learning. Using short-axis tagged cardiac magnetic resonance images from the UK Biobank, we evaluate their impact on tracking accuracy, motion behaviour, and adaptation trajectory.
All learned priors substantially improved early adaptation performance compared with random initialisation. While the simple consensus prior was effective, auto-decoders recovered large deformations faster during early adaptation. Meta-learning achieved strong early performance and maintained the best adaptation trajectory over 50 iterations.
Source: Learning Cardiac Motion Priors for Implicit Neural Representations