学习心脏运动先验用于隐式神经表示
Learning Cardiac Motion Priors for Implicit Neural Representations
- William Harvey Research Institute, Queen Mary University of London（伦敦玛丽女王大学威廉·哈维研究所）
- Cardio-Oncology Service, Royal Brompton and Harefield Hospitals（皇家布朗普顿和哈雷菲尔德医院心脏肿瘤科）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
针对隐式神经表示在心脏运动估计中优化耗时且敏感的问题，比较了四种学习先验策略，其中元学习在早期适应和长期轨迹上表现最佳。
中文摘要 AI 辅助
隐式神经表示（INR）非常适合心脏运动估计，提供连续、紧凑的运动场表示。然而，将INR拟合到每个图像序列耗时且对优化轨迹敏感。学习先验有助于引导优化朝向合理的运动场并实现更快的适应，但学习心脏运动INR的先验仍未被充分探索。在这项工作中，我们比较了四种学习心脏运动先验的策略，包括通过联合优化学习的群体先验、通过权重平均获得的一致性先验、自动解码器和元学习。使用来自英国生物银行的短轴标记心脏磁共振图像，我们评估了它们对跟踪精度、运动行为和适应轨迹的影响。与随机初始化相比，所有学习先验都显著提高了早期适应性能。虽然简单的一致性先验有效，但自动解码器在早期适应期间更快地恢复大变形。元学习实现了强大的早期性能，并在50次迭代中保持了最佳的适应轨迹。
英文摘要
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. The code can be found at https://github.com/andrewjackbell/nvf_priors .
