发表机构
Eindhoven University of Technology; University and ETH Zurich（埃因霍温理工大学; 苏黎世大学与苏黎世联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究将物理信息神经网络（PINN）框架扩展加入时空隐式神经表示（INRs），在真实模拟CMR数据集上提升了心肌灌注参数估计的鲁棒性与准确性。
AI 中文摘要
从心脏磁共振（CMR）定量心肌灌注可通过将示踪剂动力学模型拟合到动态对比增强MR数据实现。但用描述组织内对比剂演化的多室交换模型拟合观测数据以估计灌注参数，是对噪声和采集变异性敏感的挑战性逆问题。此前，物理信息神经网络（PINNs）被提出作为传统非线性最小二乘拟合方法的替代方案，在定量灌注CMR中展现出良好效果。本研究将此前提出的PINN框架扩展，加入时空隐式神经表示（INRs），将MR信号表示为连续时空函数，以提升PINN模型的准确性、平滑性和物理一致性。在真实模拟CMR数据集上，所提出的带INRs的PINN相比现有方法展现出更优的鲁棒性和参数估计准确性，代码可在指定URL获取。
英文摘要
Quantifying myocardial perfusion from cardiac magnetic resonance (CMR) can be achieved by fitting tracer-kinetic models to the dynamic contrast-enhanced MR data. However, fitting the observed data with multi-compartment exchange models, which describe the evolution of the contrast agent in the tissue, to estimate perfusion parameters is a challenging inverse problem that is sensitive to noise and acquisition variability. Previously, physics-informed neural networks (PINNs) have been proposed as an alternative to conventional non-linear least squares fitting methods with promising results for quantitative perfusion CMR. In this work, we extend the previously proposed PINN framework with spatiotemporal implicit neural representations (INRs) to represent the MR signal as a continuous spatiotemporal function and to improve the accuracy, smoothness, and physical consistency of the PINN model. In realistic simulated CMR datasets, our proposed PINN with INRs demonstrates improved robustness and parameter estimation accuracy over the previously established methods. The code is available at https://github.com/q-cardIA/pinn-inr.
CommentsAccepted at the STACOM workshop at MICCAI, Strasbourg 2026
