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TY - JOUR

T1 - Advances in machine learning applications for cardiovascular 4D flow MRI

AU - Peper, Eva S.

AU - van Ooij, Pim

AU - Jung, Bernd

AU - Huber, Adrian

AU - Gräni, Christoph

AU - Bastiaansen, Jessica A. M.

N1 - Funding Information: This study was supported by funding received from the Swiss National Science Foundation (grant #PCEFP2_194296) and the Swiss Heart Foundation (grant #FF18054). Publisher Copyright: Copyright © 2022 Peper, van Ooij, Jung, Huber, Gräni and Bastiaansen.

PY - 2022/12/9

Y1 - 2022/12/9

N2 - 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.

AB - 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.

KW - 4D flow

KW - 4D flow cardiovascular magnetic resonance

KW - artificial intelligence

KW - four-dimensional flow imaging

KW - machine learning (ML)

UR - http://www.scopus.com/inward/record.url?scp=85144998267&partnerID=8YFLogxK

U2 - 10.3389/fcvm.2022.1052068

DO - 10.3389/fcvm.2022.1052068

M3 - Review article

C2 - 36568555

VL - 9

SP - 1052068

JO - Frontiers in cardiovascular medicine

JF - Frontiers in cardiovascular medicine

SN - 2297-055X

M1 - 1052068

ER -