arXiv Artificial Intelligence

Learning Cardiac Motion Priors for Implicit Neural Representations

2 hours ago 11.08.2026 07:00 👁 1 views
Learning Cardiac Motion Priors for Implicit Neural Representations

Quick summary

arXiv:2607.00955v3 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 lea

Key takeaways

  • arXiv:2607.00955v3 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.

Why it matters

“Learning Cardiac Motion Priors for Implicit Neural Representations” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗