Enhancing Hemodynamic Parameter Estimations: Nonlinear Blood Behavior in 4D Flow MRI

Hemodynamic parameters have been estimated assuming a Newtonian constant viscosity, even when blood exhibits shear-thinning behavior. This article investigates the influence of blood rheology and hematocrit percentage on estimating Wall Shear Stress (WSS) and Energy Loss ($E_L$) at different time instants of the cardiac cycle, as well as the Oscillatory Shear Index (OSI). We specifically focus on a hematocrit-dependent power-law non-Newtonian model, considering a wide range of hematocrit values. The rheological parameters are obtained from experimentally fitted data reported previously. This study contributes to understanding the impact of blood rheology on hemodynamic parameter estimations using both in-silico and in-vivo aortic 4D Flow magnetic resonance images. Across all cases, we systematically compared WSS, $E_L$, and OSI parameters using Newtonian and power-law models, highlighting the crucial role of blood rheology in accurately assessing cardiovascular diseases.

WarpPINN: Cine-MR image registration with physics-informed neural networks

Heart failure is typically diagnosed with a global function assessment, such as ejection fraction. However, these metrics have low discriminate power, failing to distinguish different types of this disease. Quantifying local deformations in the form of cardiac strain can provide helpful information, but it remains a challenge. In this work, we introduce WarpPINN, a physics-informed neural network to perform image registration to obtain local metrics of the heart deformation. We apply this method to cine magnetic resonance images to estimate the motion during the cardiac cycle. We inform our neural network of near-incompressibility of cardiac tissue by penalizing the jacobian of the deformation field. The loss function has two components: an intensity-based similarity term between the reference and the warped template images, and a regularizer that represents the hyperelastic behavior of the tissue. The architecture of the neural network allows us to easily compute the strain via automatic differentiation to assess cardiac activity. We use Fourier feature mappings to overcome the spectral bias of neural networks, allowing us to capture discontinuities in the strain field. We test our algorithm on a synthetic example and on a cine-MRI benchmark of 15 healthy volunteers. We outperform current methodologies both landmark tracking and strain estimation. We expect that WarpPINN will enable more precise diagnostics of heart failure based on local deformation information. Source code is available at https://github.com/fsahli/WarpPINN.