Spatial and Temporal Super-Resolution and Denoising of Intracardiac 4D Flow MRI

Abstract

Intracardiac flow is driven by complex, and time-varying cardiac motion and interaction with the heart valves, producing complex flow features that may provide valuable information for disease diagnosis and treatment planning. 4D Flow MRI enables non-invasive, time-resolved measurement of three-dimensional cardiovascular velocity fields. However, substantial noise and limited spatial and temporal resolution limit its ability to accurately resolve flow features. We propose 4D-MRAR (4D Magnetic Resonance Augmented Reconstruction), a deep learning (DL) method to super-resolve and denoise 4D Flow MRI data. Unlike prior DL methods that train and infer on static vascular regions, our method predicts intracardiac flow within hearts undergoing large deformation. Moreover, it uniquely allows for simultaneous super-resolution in 3D space and time. The method combines a residual convolutional neural network, a recurrent neural network, and an implicit neural field to predict high-resolution velocity and pressure fields while efficiently enforcing the governing equations of fluid flow during training. Trained on a synthetic dataset obtained from computational fluid dynamics (CFD) simulations of left ventricle (LV) flow, our method generalizes to synthetic whole-heart 4D flow MRI data, and demonstrated promising performance on real 4D Flow MRI. The produced super-resolved flow fields exhibit significantly improved accuracy and greater consistency with the governing equations of blood flow compared with prior methods.