Physics-Informed Implicit Neural Representations for Improved Myocardial Perfusion MRI Quantification
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 sp...
| Authors | Christos Tsepas, Chang Yan, Maximilian Fuetterer et al. |
|---|---|
| Primary category | eess.IV |
| Published | 11 August 2026 |
| Last updated at source | 11 August 2026 |
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