用神经网络提升心脏核磁灌注定量精度，更抗噪声更稳定。
Physics-Informed Implicit Neural Representations for Improved Myocardial Perfusion MRI Quantification
- 用时空隐式神经表示建模核磁信号，实现连续动态重建
- 在模拟数据上参数估计误差降低18%-29%，抗噪能力更强
- 适合医学影像定量分析、需高精度血流评估的研究者
从心脏磁共振（CMR）中量化心肌灌注可通过将示踪剂动力学模型拟合到动态增强MR数据实现。然而，使用多室交换模型拟合观测数据以估计灌注参数是一个困难的反问题，对噪声和采集变异性敏感。此前，物理信息神经网络（PINNs）已被提出作为传统非线性最小二乘法的替代方案，在定量灌注CMR中表现良好。本文扩展了先前的PINN框架，引入时空隐式神经表示（INRs），将MR信号表示为连续时空函数，从而提升模型的准确性、平滑性和物理一致性。在真实模拟的CMR数据集上，所提出的带有INRs的PINN方法在鲁棒性和参数估计精度方面均优于已有方法。代码已开源：https://github.com/q-cardIA/pinn-inr。
原文摘要 · Abstract (English)
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.
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