Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Validation of Phase Contrast Flow Quantification and Relaxometry for Cardiovascular Magnetic Resonance Imaging

Bidhult, Sebastian LU (2018)
Abstract
Quantitative imaging, where every pixel of an image represents a physical quantity (e.g. time
or velocity) is being increasingly used in the field of diagnostic radiology and has potential to
enhance medical diagnosis. Quantitative methods for Magnetic Resonance Imaging (MRI)
enables measurements of velocity and flow using a technique called Phase Contrast Magnetic
Resonance (PC-MR), and different time constants of the magnetic resonance signal can be
measured to characterize different tissue types such as muscle and fat in MR images using a
technique called magnetic resonance relaxometry.
One of the first clinical applications of MR relaxometry was to estimate iron load in
different organs noninvasively by... (More)
Quantitative imaging, where every pixel of an image represents a physical quantity (e.g. time
or velocity) is being increasingly used in the field of diagnostic radiology and has potential to
enhance medical diagnosis. Quantitative methods for Magnetic Resonance Imaging (MRI)
enables measurements of velocity and flow using a technique called Phase Contrast Magnetic
Resonance (PC-MR), and different time constants of the magnetic resonance signal can be
measured to characterize different tissue types such as muscle and fat in MR images using a
technique called magnetic resonance relaxometry.
One of the first clinical applications of MR relaxometry was to estimate iron load in
different organs noninvasively by measuring the time constant called T2*. Patients suffering
from iron load disease are at risk of developing organ failure due to iron overload. Iron
chelate therapy has been shown to reduce chronic iron overload but it is toxic and has been
linked to renal failure at high doses. MRI T2* measurements can be used to effectively tailor
chelate therapy for patients with iron load disease, thereby reducing mortality of the disease.
Several methods for calculating T2* from MRI images are currently being used, each with
its own advantages and disadvantages. Different MRI vendors generally use slightly different
methods. Further, some methods are mainly suitable for cases with moderate to normal iron
load while other methods are more suitable for cases with severe iron load.
For other clinical applications of MR relaxometry the MR time constants called T1 and
T2 are measured. For example, T1 measurements before and after administration of a certain
MRI contrast agent makes it possible to determine the extracellular volume in different parts
of the heart muscle which can be used to examine damages to the heart muscle after a heart
attack. T2 measurements can for example be used to detect edema in the heart muscle and
to determine blood oxygen saturation noninvasively. Several methods exist for T1 and T2
calculation from MRI images and software tools that can be used to calculate T1 and T2
values could be of help to standardize methodology in the clinics. A previous software for T1
and T2 analysis exist but it is designed to be used for research only.
The latest MR relaxometry methods often use computer simulations of MR physics together
with MR images to enable measurement of several MR time constants at the same
time or to increase the accuracy of each measurement. These techniques show great promise
in advancing the research field of MRI but current methods require state of the art measurement
techniques which can only be implemented on high-end MRI scanners, limiting wide Imaging clinical use. Phase Contrast Magnetic Resonance (PC-MR) can be used to measure velocity in each pixel of an MRI image and have been used for many years as the reference standard for noninvasive measurements of blood flow. In order to measure the total net flow in a blood vessel over a heartbeat, the vessel of interest has to be delineated in a time-resolved PC-MR image series usually containing 15-35 images. Manual vessel delineation in these images is time consuming and requires user experience for accurate results. Semi-automatic delineation methods based on image analysis have reduced the amount of required user input and the total time of analysis for PC-MR flow measurements. However, currently existing semi-automatic methods often need manual corrections from the user. Non-invasive flow and blood velocity measurements in the fetal cardiovascular system by MRI is a promising alternative to doppler ultrasound for diagnosing disease such as congenital heart defects and intra-uterine growth restriction. Conventional PC-MR flow measurements require an ECG-recording during the MRI scan which is used to sort the collected MRI data to form a time-resolved video over a heartbeat, a process called retrospective image gating. The lack of a usable ECG by surface electrodes for fetal imaging requires alternative image gating techniques. Metric Optimized Gating (MOG) is a previously published image gating technique which does not require a fetal ECG recording. MOG together with PC-MR flow measurements (MOG PC-MR) has demonstrated reproducibility for fetal imaging in studies from one research center. However, MOG PC-MR flow measurements have not been validated for a range of flow rates or a range of peak velocity. This dissertation investigates existing and newly developed MR relaxometry and PC-MR measurement methods with the purpose of evaluating clinical applicability. In Study I a new vendor-independent T2* calculation method was validated over the range of clinically relevant T2* values in phantom experiments. Invivo T2* measurements using the proposed method were in good agreement with T2* measurements using a vendorspecific T2* method in the heart and liver of patients with known or suspected iron load disease. In Study II a vendor-independent software for T1 and T2 analysis was validated in phantom experiments. In Study III a new MR-relaxometry method called SQUAREMR, which was applied to a previously introduced and widely available T1 measurement technique (MOLLI), was shown to provide improved T1 measurement accuracy in phantom experiments. In Study IV a new semi-automatic delineation method for PC-MR flow measurements which uses a database of manual vessel delineations to control the shape of the delineation was validated in a pulsatile flow phantom experiment and showed good agreement with manual delineations in invivo PC-MR images of the ascending aorta and main pulmonary artery. Finally, in Study V MOG PC-MR showed good agreement with conventional PC-MR in a pulsatile flow phantom experiment except for cases with low Velocity to Noise Ratio (VNR), which resulted in underestimation of peak velocity and overestimation of flow which warrants optimization of the PC-MR measurement to individual fetal vessels for accurate MOG PC MR fetal flow measurements. (Less)
Please use this url to cite or link to this publication:
author
supervisor
opponent
  • Professor Kozerke, Sebastian, ETH, Zürich, Switzerland
organization
publishing date
type
Thesis
publication status
published
subject
pages
208 pages
publisher
Department of Biomedical Engineering, Lund university
defense location
Lecture hall GK, Sölvegatan 19, Lund University, Faculty of Engineering LTH, Lund
defense date
2018-08-17 09:00:00
ISBN
978-91-7753-742-7
978-91-7753-743-4
language
English
LU publication?
yes
id
1d2e8e6e-fca6-4228-833c-037d8ce20984
date added to LUP
2018-05-30 08:51:59
date last changed
2018-11-21 21:40:05
@phdthesis{1d2e8e6e-fca6-4228-833c-037d8ce20984,
  abstract     = {Quantitative imaging, where every pixel of an image represents a physical quantity (e.g. time<br/>or velocity) is being increasingly used in the field of diagnostic radiology and has potential to<br/>enhance medical diagnosis. Quantitative methods for Magnetic Resonance Imaging (MRI)<br/>enables measurements of velocity and flow using a technique called Phase Contrast Magnetic<br/>Resonance (PC-MR), and different time constants of the magnetic resonance signal can be<br/>measured to characterize different tissue types such as muscle and fat in MR images using a<br/>technique called magnetic resonance relaxometry.<br/>One of the first clinical applications of MR relaxometry was to estimate iron load in<br/>different organs noninvasively by measuring the time constant called T2*. Patients suffering<br/>from iron load disease are at risk of developing organ failure due to iron overload. Iron<br/>chelate therapy has been shown to reduce chronic iron overload but it is toxic and has been<br/>linked to renal failure at high doses. MRI T2* measurements can be used to effectively tailor<br/>chelate therapy for patients with iron load disease, thereby reducing mortality of the disease.<br/>Several methods for calculating T2* from MRI images are currently being used, each with<br/>its own advantages and disadvantages. Different MRI vendors generally use slightly different<br/>methods. Further, some methods are mainly suitable for cases with moderate to normal iron<br/>load while other methods are more suitable for cases with severe iron load.<br/>For other clinical applications of MR relaxometry the MR time constants called T1 and<br/>T2 are measured. For example, T1 measurements before and after administration of a certain<br/>MRI contrast agent makes it possible to determine the extracellular volume in different parts<br/>of the heart muscle which can be used to examine damages to the heart muscle after a heart<br/>attack. T2 measurements can for example be used to detect edema in the heart muscle and<br/>to determine blood oxygen saturation noninvasively. Several methods exist for T1 and T2<br/>calculation from MRI images and software tools that can be used to calculate T1 and T2<br/>values could be of help to standardize methodology in the clinics. A previous software for T1<br/>and T2 analysis exist but it is designed to be used for research only.<br/>The latest MR relaxometry methods often use computer simulations of MR physics together<br/>with MR images to enable measurement of several MR time constants at the same<br/>time or to increase the accuracy of each measurement. These techniques show great promise<br/>in advancing the research field of MRI but current methods require state of the art measurement<br/>techniques which can only be implemented on high-end MRI scanners, limiting wide Imaging clinical use. Phase Contrast Magnetic Resonance (PC-MR) can be used to measure velocity in each pixel of an MRI image and have been used for many years as the reference standard for noninvasive measurements of blood flow. In order to measure the total net flow in a blood vessel over a heartbeat, the vessel of interest has to be delineated in a time-resolved PC-MR image series usually containing 15-35 images. Manual vessel delineation in these images is time consuming and requires user experience for accurate results. Semi-automatic delineation methods based on image analysis have reduced the amount of required user input and the total time of analysis for PC-MR flow measurements. However, currently existing semi-automatic methods often need manual corrections from the user. Non-invasive flow and blood velocity measurements in the fetal cardiovascular system by MRI is a promising alternative to doppler ultrasound for diagnosing disease such as congenital heart defects and intra-uterine growth restriction. Conventional PC-MR flow measurements require an ECG-recording during the MRI scan which is used to sort the collected MRI data to form a time-resolved video over a heartbeat, a process called retrospective image gating. The lack of a usable ECG by surface electrodes for fetal imaging requires alternative image gating techniques. Metric Optimized Gating (MOG) is a previously published image gating technique which does not require a fetal ECG recording. MOG together with PC-MR flow measurements (MOG PC-MR) has demonstrated reproducibility for fetal imaging in studies from one research center. However, MOG PC-MR flow measurements have not been validated for a range of flow rates or a range of peak velocity. This dissertation investigates existing and newly developed MR relaxometry and PC-MR measurement methods with the purpose of evaluating clinical applicability. In Study I a new vendor-independent T2* calculation method was validated over the range of clinically relevant T2* values in phantom experiments. Invivo T2* measurements using the proposed method were in good agreement with T2* measurements using a vendorspecific T2* method in the heart and liver of patients with known or suspected iron load disease. In Study II a vendor-independent software for T1 and T2 analysis was validated in phantom experiments. In Study III a new MR-relaxometry method called SQUAREMR, which was applied to a previously introduced and widely available T1 measurement technique (MOLLI), was shown to provide improved T1 measurement accuracy in phantom experiments. In Study IV a new semi-automatic delineation method for PC-MR flow measurements which uses a database of manual vessel delineations to control the shape of the delineation was validated in a pulsatile flow phantom experiment and showed good agreement with manual delineations in invivo PC-MR images of the ascending aorta and main pulmonary artery. Finally, in Study V MOG PC-MR showed good agreement with conventional PC-MR in a pulsatile flow phantom experiment except for cases with low Velocity to Noise Ratio (VNR), which resulted in underestimation of peak velocity and overestimation of flow which warrants optimization of the PC-MR measurement to individual fetal vessels for accurate MOG PC MR fetal flow measurements.},
  author       = {Bidhult, Sebastian},
  isbn         = {978-91-7753-742-7},
  language     = {eng},
  month        = {05},
  publisher    = {Department of Biomedical Engineering, Lund university},
  school       = {Lund University},
  title        = {Validation of Phase Contrast Flow Quantification and Relaxometry for Cardiovascular Magnetic Resonance Imaging},
  url          = {https://lup.lub.lu.se/search/files/44397920/SB88dissertation_validation_phase_tobeprinted.pdf},
  year         = {2018},
}