Advances in machine learning applications for cardiovascular 4D flow MRI

Abstract

Four-dimensional flow magnetic resonance imaging (MRI) has evolved as a non-invasive imaging technique to visualize and quantify blood flow in the heart and vessels. Hemodynamic parameters derived from 4D flow MRI, such as net flow and peak velocities, but also kinetic energy, turbulent kinetic energy, viscous energy loss, and wall shear stress have shown to be of diagnostic relevance for cardiovascular diseases. 4D flow MRI, however, has several limitations. Its long acquisition times and its limited spatio-temporal resolutions lead to inaccuracies in velocity measurements in small and low-flow vessels and near the vessel wall. Additionally, 4D flow MRI requires long post-processing times, since inaccuracies due to the measurement process need to be corrected for and parameter quantification requires 2D and 3D contour drawing. Several machine learning (ML) techniques have been proposed to overcome these limitations. Existing scan acceleration methods have been extended using ML for image reconstruction and ML based super-resolution methods have been used to assimilate high-resolution computational fluid dynamic simulations and 4D flow MRI, which leads to more realistic velocity results. ML efforts have also focused on the automation of other post-processing steps, by learning phase corrections and anti-aliasing. To automate contour drawing and 3D segmentation, networks such as the U-Net have been widely applied. This review summarizes the latest ML advances in 4D flow MRI with a focus on technical aspects and applications. It is divided into the current status of fast and accurate 4D flow MRI data generation, ML based post-processing tools for phase correction and vessel delineation and the statistical evaluation of blood flow.

Exploring the Intersection of Geophysics and Diagnostic Imaging in the Health Sciences

“…Tomographic techniques are frequently utilized in geophysics to build 3D images of subsurface objects. Examples include X-ray CT and PET scans, where cross-sectional images are produced using information from various angles [ 120 , 121 , 122 , 123 ]. Tomographic Imaging: Medical imaging uses tomographic techniques to recreate precise images of organs and tissues.…”

Mechanism Analysis of Vascular Calcification Based on Fluid Dynamics

“…A prominent emerging technology, 4D Flow MRI, introduces an innovative in vivo approach to assess fluid mechanics parameters. Technically, 4D Flow MRI builds upon phase-contrast magnetic resonance imaging (PC MRI) by incorporating three-directional flow velocity encoding and time-resolved capabilities [ 120 ]. It empowers the comprehensive measurement of fluid mechanics parameters within the heart and major vessels, providing full volumetric coverage throughout the entire cardiac cycle.…”

A clinician’s guide to understanding aortic 4D flow MRI

“…Post-processing of 4D flow MRI is more complex than 2D flow imaging, although post-processing time as well as user-dependency can be reduced by automating segmentation and quantification processes and deep-learning [ 21 , 22 ]. Quantitative 4D flow analysis is further complicated by the fact that data on normal or reference values are currently limited, and values may vary according to vendor systems and centers [ 23 , 24 ].…”