Data Assimilation by Stochastic Ensemble Kalman Filtering to Enhance Turbulent Cardiovascular Flow Data From Under-Resolved Observations

Abstract

We propose a data assimilation methodology that can be used to enhance the spatial and temporal resolution of voxel-based data as it may be obtained from biomedical imaging modalities. It can be used to improve the assessment of turbulent blood flow in large vessels by combining observed data with a computational fluid dynamics solver. The methodology is based on a Stochastic Ensemble Kalman Filter (SEnKF) approach and geared toward pulsatile and turbulent flow configurations. We describe the observed flow fields by a mean value and its covariance. These flow fields are combined with forecasts obtained from a direct numerical simulation of the flow field. The method is validated against canonical pulsatile and turbulent flows. Finally, it is applied to a clinically relevant configuration, namely the flow downstream of a bioprosthetic valve in an aorta phantom. It is demonstrated how the 4D flow field obtained from experimental observations can be enhanced by the data assimilation algorithm. Results show that the presented method is promising for future use with in vivo data from 4D Flow Magnetic Resonance Imaging (4D Flow MRI). 4D Flow MRI returns spatially and temporally averaged flow fields that are limited by the spatial and the temporal resolution of the tool. These averaged flow fields and the associated uncertainty might be used as observation data in the context of the proposed methodology.

HPC+ in the medical field: Overview and current examples

“…The use of such accelerated codes allows us to go beyond classical forward simulations and to employ, e.g., adjoint-based methods of automatic design optimization for heart valves [ 23 ] or data assimilation to enhance clinical diagnostic imaging [ 25 ]. In particular, the second example presents an application which can connect daily clinical work in a hospital to HPC.…”

“…This was explored by the authors in the HPC-PREDICT project in which a computational workflow for the analysis of blood flow in the ascending aorta was developed. This end-to-end workflow includes elements for fast data acquisition with 4D-Flow-MRI [ 24 ] automatic segmentation algorithms a Kalman filter for data assimilation of 4D-Flow-MRI images [ 25 ], and a deep learning network for anomaly detection in the enhanced MRI data [ 26 ]. The vision of HPC-PREDICT is to enable hospitals to improve their diagnostic imaging by providing fast, high-resolution, low-noise and automatically annotated 4D-Flow-MRI images using HPC.…”