Julio Sotelo1,2,3
1School of Biomedical Engineering, Universidad de Valparaíso, Valparaíso, Chile, 2Millennium Institute for Intelligent Healthcare Engineering iHEALTH, Santiago, Chile, 3Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile
Synopsis
Keywords: Cardiovascular: Hemodynamics
In this educational talk, we will
talk about the relationship between the velocity to noise ratio and acquisition
related parameters. The preprocessing of 4D flow MRI is also important, because
the acquisition could be affected by gradient-induced eddy currents,
concomitant gradient fields, gradient non-linearity and phase wraps resulting
in velocity aliasing, these problems need to be addressed to avoid additional
errors in the quantification of hemodynamic parameters. Finally we will explore
different methods used to validate 4D flow MRI advanced parameters as; wall shear stress, vorticity, helicity
density, viscous energy loss, kinetic energy, circulation, pressure gradients,
among others.
Introduction
In the last ten years, hemodynamic parameters quantified by
the 4D Flow magnetic resonance technique have emerged as essential biomarkers
in the early diagnosis of different cardiovascular diseases, providing new
insights into complex flows [1]. Parameters such as wall shear stress,
vorticity, helicity density, viscous energy loss, kinetic energy, circulation, pressure
gradients, among others, have been extensively studied [2]. But, how much do we
know about the validation of these hemodynamic parameters? In this work we
explore that to have an adequate validation, we must consider that the 4D Flow
MRI data must be correctly acquired and preprocessed. This work was divided in
three different topics; a) relationship between the velocity to noise ratio and
acquisition related parameters, b) the preprocessing steps necessary to avoid
problems like eddy current effect, maxwell term, gradient field non-linearity
and phase wraps and c) methods currently used for the validation of
hemodynamics parameters.4D Flow MRI and Velocity to Noise Ratio
The velocity-to-noise ratio (VNR)
is a measure of the quality of 4D Flow MRI data. VNR is calculated by dividing
the mean velocity (V) of the blood flow by the standard deviation of the velocity
noise (N) in the image [3]. A higher VNR indicates a better-quality image,
which is important for accurate measurement of blood flow velocity. Different
parameters related to the acquisition can affect the VNR. Three of the most
important are the velocity encoding value (Venc), spatial resolution, and temporal
resolution. An overestimated velocity encoding value can generate a reduction
in the VNR, and an underestimation can generate aliasing in the image. High spatial and temporal resolution increases
the noise in the image which decreases the VNR. There are other acquisition related
parameters that can affect the VNR, as field of view, high acceleration
parameters used and intravoxel dephasing [1]. It is important to obtain a higher
VNR, because this can lead to more accurate and reproducible measurements of
blood flow velocity, which can improve the clinical utility of 4D Flow MRI.4D Flow MRI and Preprocessing
Errors that cause bias in the
measured velocity field in 4D Flow MRI are often related to gradient-induced
eddy currents, concomitant gradient fields, and gradient non-linearity [4]. While
clinical MRI systems can correct and calibrate the latter two sources of error
with sufficient accuracy, eddy currents cause a background phase error that
must be corrected. Post-processing methods, such as static-tissue interpolation
offset correction, can be used to address this error with equivalent
performance [5]. When the maximum blood flow velocity exceeds the chosen Venc, aliasing
can be generated in velocity images. Phase unwrapping can be used to improve
the accuracy of flow and velocity measurements in such cases, but it requires a
visual inspection of the peak systolic and diastolic cardiac phases to ensure that
the velocity images are free of uncorrected velocity aliasing [6]. In addition,
an appropriate segmentation of the region of interest is an important aspect of
the flow and velocity quantification process [7].4D Flow MRI and Validation of Hemodynamics Parameters
Validating hemodynamic parameters
in 4D flow MRI data is important for ensuring the accuracy and reliability of
flow measurements. Hemodynamic parameters, such as wall shear stress, vorticity, helicity density, viscous energy
loss, kinetic energy, circulation, pressure gradients, could be
important indicators of cardiovascular disease [1,2]. In the literature most of
the validation methods of these parameters involves comparing the MRI
measurements to those obtained from in-silico models (Poiseuille equation [8], Womersley
Equation [8] and Lamb-Ossen equation [9,10]), in-vitro models (Realistic
Phantoms Studies [11,12]), and numerical simulations-based models [13,14]. This
comparison can help to identify and correct any errors in the quantification of
the hemodynamics parameters and to ensure that the measurements are consistent
with expected values. Conclusions
Validating hemodynamic parameters
in 4D flow MRI data is particularly important for clinical applications, where
the data is used to diagnose and monitor cardiovascular disease. Accurate and
reliable measurements of hemodynamic parameters can help clinicians to make better
informed decisions about patient care and treatment and can ultimately lead to
improved patient outcomes. Most of the validation method used in the literature
are in base of in-silico, in-vitro and simulations-based models.Acknowledgements
Thanks to ANID –
Millennium Science Initiative Program – ICN2021_004
and ANID – FONDECYT de iniciación
en investigación 11200481.References
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