用物理模型指导的自适应注册，解决心脏多参数MRI图像对齐难题
Groupwise Registration with Physics-Informed Test-Time Adaptation on Multi-parametric Cardiac MRI
- 基于物理模型生成参考图，实现测试时自适应图像配准
- 在多种对比度差异大的序列中实现精准跨模态配准
- 适用于健康志愿者多序列数据，提升组织特征分析可靠性
多参数映射磁共振已成为心肌组织表征的有效工具。然而，多参数图之间的错位使得像素级分析变得困难。为解决此问题，我们开发了一种可泛化的物理信息深度学习模型，通过测试时自适应实现不同物理模型（如T1映射与T2映射）获取的对比加权图像间的组级图像配准。该物理信息自适应利用特定物理模型生成的合成图像作为配准参考，支持多种组织对比的归纳学习。我们在健康志愿者的多种MRI序列中验证了该模型，结果表明其在广泛图像对比度变化下显著提升了多模态配准性能。
原文摘要 · Abstract (English)
Multiparametric mapping MRI has become a viable tool for myocardial tissue characterization. However, misalignment between multiparametric maps makes pixel-wise analysis challenging. To address this challenge, we developed a generalizable physics-informed deep-learning model using test-time adaptation to enable group image registration across contrast weighted images acquired from multiple physical models (e.g., a T1 mapping model and T2 mapping model). The physics-informed adaptation utilized the synthetic images from specific physics model as registration reference, allows for transductive learning for various tissue contrast. We validated the model in healthy volunteers with various MRI sequences, demonstrating its improvement for multi-modal registration with a wide range of image contrast variability.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。