arXiv:2607.00955v1 Announce Type: 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
This research addresses the current computational limitations of Implicit Neural Representations (INRs), a promising AI technique, by exploring learning cardiac motion priors for faster and more reliable deployment.
Improving the efficiency and robustness of INRs for medical imaging, particularly cardiac motion analysis, can accelerate diagnostics, enhance treatment planning, and reduce computational overhead in healthcare AI.
The ability to integrate learned priors into INRs makes these continuous representations more practical for real-world, time-sensitive applications without extensive retraining, shifting how dynamic medical imaging data is processed.
- · Medical AI developers
- · Cardiologists
- · Healthcare technology providers
- · Patients needing cardiac diagnostics
- · Traditional motion estimation methods
- · Software requiring extensive, manual optimization
Implicit neural representations become more computationally viable for real-time medical imaging and analysis.
Faster and more accurate cardiac motion estimation could lead to earlier disease detection and more personalized treatment strategies.
This efficiency gain might generalize to other dynamic biological systems, expanding the application of INRs beyond cardiology to broader medical diagnostics.
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Read at arXiv cs.AI