Improving Assessments of Hemodynamics and Vascular Disease

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Improving

Assessments of

Hemodynamics and

Vascular Disease

Linköping University

Medical Dissertation No. 1675

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L i n k ö p i n g U n i v e r s i t y M e d i c a l D i s s e r t a t i o n N o . 1 6 7 5

Improving Assessments of

Hemodynamics and Vascular

Disease

Magnus Ziegler

D i v i s i o n o f C a r d i o v a s c u l a r M e d i c i n e D e p a r t m e n t o f M e d i c a l a n d H e a l t h S c i e n c e s C e n t e r f o r M e d i c a l I m a g e S c i e n c e a n d V i s u a l i z a t i o n ( C M I V ) L i n k ö p i n g U n i v e r s i t y , L i n k ö p i n g , S w e d e n

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Improving Assessments of Hemodynamics

and Vascular Disease

Linköping University

Medical Dissertation No. 1675 Division of Cardiovascular Medicine Department of Medical and Health Sciences

Center for Medical Image Science and Visualization (CMIV) Linköping University, Linköping, Sweden

http://liu.se/cmr

Printed by:

LiU Tryck, Linköping, Sweden ISBN 978-91-7685-098-5 ISSN 0345-0082

No part of this publication may be reproduced, stored in a retrieval system, or be transmitted, in any form or by any means, electronic, mechanic,

photocopying, recording, or otherwise, without prior permission of the author. Cover: Stylized streamline visualization of blood flow through an abdominal aortic aneurysm.

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Abstract

Blood vessels are more than simple pipes, passively enabling blood to pass through them. Their form and function are dynamic, changing with both aging and disease. This process involves a feedback loop wherein changes to the shape of a blood vessel aﬀect the hemodynamics, causing yet more structural adaptation. This feedback loop is driven in part by the hemodynamic forces generated by the blood flow, and the distribution and strength of these forces appear to play a role in the initiation, progression, severity, and the outcome of vascular diseases.

Magnetic Resonance Imaging (MRI) oﬀers a unique platform for investi-gating both the form and function of the vascular system. The form of the vascular system can be examined using MR-based angiography, to generate de-tailed geometric analyses, or through quantitative techniques for measuring the composition of the vessel wall and atherosclerotic plaques. To complement these analyses, 4D Flow MRI can be used to quantify the functional aspect of the vas-cular system, by generating a full time-resolved three-dimensional velocity field that represents the blood flow.

This thesis aims to develop and evaluate new methods for assessing vas-cular disease using novel hemodynamic markers generated from 4D Flow MRI and quantitative MRI data towards the larger goal of a more comprehensive non-invasive examination oriented towards vascular disease. In Paper I, we de-veloped and evaluated techniques to quantify flow stasis in abdominal aortic aneurysms to measure this under-explored aspect of aneurysmal hemodynam-ics. In Paper II, the distribution and intensity of turbulence in the aorta was quantified in both younger and older men to understand how aging changes this aspect of hemodynamics. A method to quantify the stresses generated by turbu-lence that act on the vessel wall was developed and evaluated using simulated flow data in Paper III, and in Paper V this method was utilized to examine the wall stresses of the carotid artery. The hemodynamics of vascular disease cannot be uncoupled from the anatomical changes the vessel wall undergoes, and therefore Paper IV developed and evaluated a semi-automatic method for quantifying several aspects of vessel wall composition. These developments, taken together, help generate more valuable information from imaging data, and can be pooled together with other methods to form a more comprehensive non-invasive examination for vascular disease.

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Populärvetenskaplig

Sammanfattning

Kardiovaskulär sjukdom är den vanligaste dödsorsak i Sverige och skapar en stor utmaning för vårt sjukvårdssystem. Kärlsjukdomar, till exempel aortaaneurysm och åderförfettning kan utvecklas utan symptom. Därför behöver vi teknik för att kunna undersöka dessa sjukdomar.

Våra blodkärls form och funktion påverkas och skapas delvis av de krafter som blodet skapar på grund av blodtryck och friktion mellan blod och kärlvägg. Att mäta och undersöka dessa krafter och flödesmönster kan hjälpa oss förstå och förutsäga vad som kan hända. Flödesmönster i friska men framförallt sjuka kärl är mycket komplexa. Flödet kan vara turbulent och därmed karaktäriseras av oregelbundenhet och intensiva fluktuationer, snarare än välordnat och laminärt. Kliniskt används idag flera olika metoder för undersökning av kärlsjukdo-mar, till exempel: ultraljud, datortomografi, och magnetisk resonanstomografi (MRT). Varje teknik har för- och nackdelar, men MRT verkar att har störst potential att undersöka båda form och funktion. Blodkärlens form kan mätas och kvantifierade i tre-dimensionella bilder med hjälp av kontrast-förstärkta an-giografibilder, och vi kan även kvantifiera kärlväggens innehåll med hjälp av så kallade Dixon-bilder. Funktionen av kärl, blodflödet, kan kvantifieras med hjälp av tre-dimensionella, tidsupplösta bilder skapade med så kallad 4D flödes-MRT. Därför, med en kombination av olika MRT-genererade bilder kan vi skapa en fullständig bild av kärlsjukdom. I avhandlingen beskrivs flera studier som fo-kuserar på utveckling och validering av nya metoder som tillsammans tar oss närmare målet att ta fram en mer fullständig MRT-baserad undersökning av kärlsjukdom. De metoder som utvecklats i avhandlingsarbetet visar potential för att tillhandahålla unik information som är kliniskt relevant för diagnos och uppföljning av patienter med kärlsjukdom.

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Acknowledgments

While my name stands alone on the cover, this thesis was undoubtably a team eﬀort.

I feel quite lucky to have had Petter Dyverfeldt as my main supervisor and mentor throughout this work, not only because of the freedom he entrusted me with, but also for his constructive and pragmatic advice throughout my studies. Thank you for giving me this opportunity.

Special thanks go to my co-supervisors for their support and input. Thanks to Tino Ebbers, for answering innumerable random questions of mine and in-dulging my curiosity; to Jonas Lantz, for sharing his imposing knowledge of fluid dynamics and computational methods; to Ebo de Muinck for sharing his enthusiasm and expertise about atherosclerosis; and to Carl-Johan Carlhäll for helpful chats about physiology.

I doubt I would have survived this eﬀort without the day-to-day support and energy of my colleagues in the CMR group. I’ve always enjoyed our sometimes surprisingly long fikas and lunch adventures. Thanks to Federica Viola, for always being willing to help and for sharing with me the finer-points of Italian cuisine; to Mariana Bustamante, for sharing her enthusiasm and desire to do new and interesting things; to Belén Casas, for all our chats to distract us from the work and laugh; to Hojin Ha, for our fun and productive collaborations; and to Sofia Kvernby, Sophia Beeck, Vikas Gupta, Merih Cibis, Alexandru Fredriksson, and Jakub Zajac, for creating an open and fun environment to work in. Thanks to Malin Strand and Elin Wistrand for providing all kinds of administrative help.

To my friends in Linköping, thank you for livening up this city with fun din-ners, bbqs, parties, drinks, rides, and hockey. To my family and friends scattered across other countries and timezones, thank for you support throughout.

Magnus, Linköping, April 2019

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Funding

This work has been conducted in collaboration with the Center for Medical Im-age Science and Visualization (CMIV) at Linköping University, Sweden. CMIV is acknowledged for provision of financial support and research infrastructure. The author also acknowledges the financial support provided by:

• The Swedish Research Council (Vetenskåpsrådet), under grant numbers 2013-06077 and 2017-03857

• The County Council of Östergötland, under grant number LIO-752951

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List of Papers

This thesis is based on the following papers, which will be referred to by their Roman numerals:

I Visualizing and Quantifying Flow Stasis in Abdominal

Aor-tic Aneurysms in Men using 4D flow MRI

Ziegler M, Welander M, Lantz J, Bjarnegård N, Lindenberger M, Länne T, Ebbers T, Dyverfeldt P. Magnetic Resonance Imaging, 2018

II Age-related Vascular Changes Aﬀect Turbulence in Aortic

Blood Flow

Ha H, Ziegler M, Welander M, Bjarnegård N, Carlhäll CJ, Linden-berger M, Länne T, Ebbers T, Dyverfeldt P. Frontiers in Physiology 2018, 9:36

III Assessment of Turbulent Flow Eﬀects on the Vessel Wall

using Four-Dimensional Flow MRI

Ziegler M, Lantz J, Ebbers T, Dyverfeldt P. Magnetic Resonance in Medicine 2017; 77 (6), 2310-2319.

IV Automated Quantification of Fat and R⇤

2in Carotid

Atheroscle-rosis

Ziegler M, Good E, Warntjes M, Engvall J, de Muinck E, Dyverfeldt P. In manuscript.

V Exploring the Relationship between Carotid Geometry and

Hemodynamic Wall Shear Stresses

Ziegler M, Alfraeus J, Good E, Engvall J, de Muinck E, Dyverfeldt P. In manuscript.

Papers I-III are reproduced with permission.

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xiv In addition, the following peer-reviewed papers were published in connection to work performed in this thesis:

• Assessment of turbulent viscous stress using ICOSA 4D Flow MRI for prediction of hemodynamic blood damage

Ha H, Lantz J, Haraldsson H, Casas B, Ziegler M, Karlsson M, Saloner D, Dyverfeldt P, Ebbers T. Scientific Reports 2016.

• Estimating the irreversible pressure drop across a stenosis by quantifying turbulence production using 4D Flow MRI

Ha H, Lantz J, Ziegler M, Casas B, Karlsson M, Dyverfeldt P, Ebbers T. Scientific Reports 2017.

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Nomenclature

2D Two-Dimensional

3D Three-Dimensional

4D Four-Dimensional

AAA Abdominal Aortic Aneurysm

AS Aortic Stenosis

CE Contrast-Enhanced

CEMRA Contrast-Enhanced MR Angiography

CFD Computational Fluid Dynamics

CMR Cardiovascular Magnetic Resonance Imaging CNN Convolutional Neural Network

CoA Coarctation of the Aorta

ECG Electrocardiogram

FF Fat Fraction

FOV Field-of-View

IP In-Phase

IPH Intraplaque Hemorrhage

IVSD Intravoxel velocity standard deviation

KE Kinetic Energy

LRNC Lipid Rich Necrotic Core

MP-RAGE Magnetization-Prepared Rapid Acquisition with Gradient Echo

MRA MR Angiography

MRI Magnetic Resonance Imaging

OP Out-of-Phase

OSI Oscillatory Shear Index xv

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PC Phase-Contrast

PD Proton Density

qMRI Quantitative MRI

Re Reynolds Number

RT Residence Time

SNR Signal-to-Noise Ratio

SVM Support Vector Machine

TAWSS Time Averaged Wall Shear Stress

TKE Turbulent Kinetic Energy

TOF Time-of-Flight

tWSS Turbulent Wall Shear Stress VENC Velocity Encoding Range VNR Velocity-to-Noise Ratio

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Chapter 1 Introduction

The form and function of the cardiovascular system are intrinsically linked, each strongly aﬀecting the other. The forces exerted by blood flow dictate a contin-uous remodeling of the heart and vessels, and these forces appear to remodel the vessel for eﬃcient flow. As a result, the healthy cardiovascular system has largely laminar flow in vessels without abrupt changes in size, shape, or direc-tion. At the same time, the forces exerted by blood flow play a significant role in the pathophysiology of many common cardiovascular diseases. Through re-modeling and other compensatory mechanisms, flow irregularities and the forces they generate can lead to a cascade of increasingly more severe abnormalities or conditions.

Therefore, to improve diagnosis, treatment, and the understanding of cardio-vascular disease, the quantification of the abnormal hemodynamics that drive the remodeling processes associated with many vascular diseases is of interest. For example, hemodynamic markers such as the wall shear stress may help de-termine the development or rupture risk of both atherosclerotic plaques and abdominal aortic aneurysms. Similarly, we can measure the degree of turbu-lence present in the carotid bifurcation, as a measure of the flow eﬃciency or the impact of stenoses.

The composition of the wall is another aspect that presents an opportunity for quantification, as the material properties of the vascular wall may be al-tered as a result of the flow-induced stresses or other disease. For example, the rupture risk of an atherosclerotic plaque is known to be linked to its composi-tion. Whether or not the composition is associated with hemodynamic stresses is unknown, however.

Currently, vascular disease is frequently assessed using imaging modalities such as ultrasound, x-ray angiography, and computed tomography. While many of these modalities can provide images about the structure of the vascular system, they are limited in their ability to assess the flow and its impact on the vascular wall. Magnetic Resonance Imaging (MRI) unlocks these assess-ments. With 4D Flow MRI, a technique that acquires the time-resolved three-dimensional flow field in a volume of interest, we can quantify the flow using a wide range of hemodynamic markers in vivo and investigate how they are linked to the form and function of the cardiovascular system. In addition, with quanti-tative MRI (qMRI) techniques such as the Dixon sequence, we can describe the material properties of the vascular wall that change as a result of cardiovascular

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CHAPTER 1. INTRODUCTION 2 disease.

In this work, we develop and evaluate new methods for assessing vessel wall disease using novel hemodynamic markers generated from 4D Flow MRI and quantitative MRI data towards the larger goal of a more comprehensive non-invasive examination oriented towards vascular disease.

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Chapter 2 Aims

The aim of this thesis is to develop new methods for assessing vascular disease using novel hemodynamic markers generated from 4D Flow MRI, and composi-tional information from quantitative MRI data, to examine vascular disease in a more comprehensive manner. Specifically we aimed to:

• Develop and evaluate methods for quantifying and visualizing flow stasis • Investigate where and the degree to which turbulence is present in the

aorta

• Develop and evaluate a method for quantifying the eﬀect of turbulence on the vessel wall

• Examine the flow-induced stresses acting on the wall in vivo

• Develop and evaluate a method for extracting compositional information from the vessel wall

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Chapter 3 Physiological Background

The vascular system has one deceptively simple function: to act as a conduit for blood. However, it is not a static conduit and its form is influenced by the flow itself, which in turn influences the flow in a feedback loop. This feedback loop is dictated by the hemodynamic forces generated by the blood flow, and the distribution and strength of these forces appear to play a role in the initiation, progression, severity, and the outcome of vascular disease.

This chapter will describe the structure of the vascular wall, as well some common vascular diseases where imaging plays an important role. This thesis primarily examined the arterial portion of the vascular system, and so this section will not discuss the anatomy or pathologies of the venous system.

3.1 Anatomy

An artery is a blood vessel that carries oxygenated blood away from the heart to the rest of the body1_{, and therefore responsible for the delivery of oxygen}

and nutrients throughout the body [1–3]. Blood is pumped through the arterial system at a higher pressure and velocity than the venous system [2]. As the vessels become more distant from the heart, their size decreases. A schematic of the arterial tree is shown in Figure 3.1.

The aorta is the largest artery in the arterial tree, receiving blood directly from the left ventricle of the heart through the aortic valve. The aorta extends through the abdomen to its bifurcation into the common iliac arteries. Given the size of this vessel, diﬀerent anatomical regions of the aorta are often described: the ascending aorta, extending from the aortic valve to the peak of the aortic arch; the descending aorta, from the peak of the aortic arch to the diaphragm and the abdominal cavity; and, the abdominal aorta, from the diaphragm to the iliac bifurcation2_{. Each region has localized, clinically relevant considerations}

and pathologies that tend to present there. For example, aneurysms are much more common in the abdominal aorta versus the thoracic aorta [4].

1_{With the exception of the pulmonary and umbilical arteries}

2_{Other definitions for these regions are often used. For example, the aortic arch itself is}

often defined as a region on its own, and under these definitions contains the three upward arterial branches for the brachiocephalic trunk, the left common carotid artery, and the left subclavian artery.

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CHAPTER 3. PHYSIOLOGICAL BACKGROUND 6

Figure 3.1: Schematic of the arterial tree.

O f p a r t i c u l a r r e l e v a n c e t o t h i s t h e s i s a r e t h e c a r o t i d a r t e r i e s . T h e y o r i g i n a t e a t t h e a o r t i c a r c h ( l e f t c o m m o n c a r o t i d ) a n d t h e b r a c h i o c e p h a l i c t r u n k ( r i g h t c o m m o n c a r o t i d 3_{) a n d s u p p l y t h e h e a d a n d n e c k w i t h b l o o d . B o t h l e f t a n d} r i g h t c a r o t i d a r t e r i e s t e r m i n a t e a t t h e c a r o t i d b i f u r c a t i o n , w h e r e t h e y s p l i t i n t o t h e i n t e r n a l a n d e x t e r n a l c a r o t i d a r t e r i e s . T h e i n t e r n a l c a r o t i d a r t e r y t a k e s a d e e p e r p a t h a n d s u p p l i e s t h e s k u l l a n d b r a i n , w h i l e t h e e x t e r n a l t a k e s a m o r e s u p e r fi c i a l p a t h a n d s u p p l i e s t h e n e c k a n d f a c e . T h e c a r o t i d b i f u r c a t i o n i n d u c e s c o m p l e x h e m o d y n a m i c s , a n d a t h e r o s c l e r o t i c p l a q u e s a r e c o m m o n i n t h i s r e g i o n [ 2 , 5 ] . T h e s t r u c t u r e o f t h e a r t e r i a l w a l l c a n b e s e e n i n F i g u r e 3 . 2 . T h e c a v i t y t h r o u g h t h e c e n t r e o f t h e a r t e r y i s k n o w n a s t h e l u m e n , w h i l e t h e w a l l i t s e l f i s c o m p o s e d o f t h r e e l a y e r s : t h e t u n i c a e x t e r n a , t h e t u n i c a m e d i a , a n d t h e t u

-3_{A common anatomical variation has the right common carotid originating independently}

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CHAPTER 3. PHYSIOLOGICAL BACKGROUND 7 n i c a i n t i m a . T h e o u t e r m o s t l a y e r , t h e t u n i c a e x t e r n a , i s c o m p o s e d p r i m a r i l y o f c o l l a g e n fi b r e s a n d s o m e e l a s t i c t i s s u e . T h e m i d d l e l a y e r , t h e t u n i c a m e d i a , i s p r i m a r i l y c o m p o s e d o f s m o o t h m u s c l e c e l l s . T h e i n n e r m o s t l a y e r , t h e t u n i c a i n -t i m a , i s m a i n l y c o m p o s e d o f e n d o -t h e l i a l c e l l s . T h e e n d o -t h e l i a l c e l l s a r e i n d i r e c -t c o n t a c t w i t h t h e c i r c u l a t i n g b l o o d a n d a r e i n v o l v e d w i t h v a r i o u s p h y s i o l o g i c a l p r o c e s s e s , s u c h a s i n fl a m m a t i o n , v a s o - c o n s t r i c t i o n a n d - d i l a t i o n , i n a d d i t i o n t o t h e i r r o l e a s a b a r r i e r b e t w e e n t h e l u m e n a n d s u r r o u n d i n g t i s s u e . T h e e n d o t h e -l i a -l c e -l -l s p e r f o r m m e c h a n o t r a n s d u c t i o n , t r a n s f o r m i n g m e c h a n i c a -l s t r e s s e s i n t o b i o l o g i c a l r e a c t i o n s . M e c h a n o t r a n s d u c t i o n o n t h e e n d o t h e l i a l s u r f a c e i s i n i t i a t e d b y i o n c h a n n e l s ( K , C a , N a , C l ) , c e l l m e m b r a n e r e c e p t o r s , c a v e o l a e , a n d t h e p l a s m a m e m b r a n e l i p i d l a y e r [ 6 ] . M o r e o v e r , t h e l u m e n i s l i n e d w i t h g l y c o c a l y x , a s t r u c t u r e t h a t w a s f o u n d t o b e s p e c i fi c a l l y r e s p o n s i b l e f o r s h e a r s t r e s s - m o d e r a t e d n i t r i c o x i d e ( N O ) p r o d u c t i o n [ 6 , 7 ] . W h e n t h e s e s i g n a l i n g p a t h w a y s a r e c o n s i s t e n t l y a c t i v a t e d o v e r a p r o l o n g e d p e r i o d o f t i m e , v e s s e l r e m o d e l i n g c a n o c c u r , t o r e d u c e t h e h e m o d y n a m i c s t r e s s e s . F o r e x a m p l e , t h e v e s s e l w a l l d o w n s t r e a m f r o m s t e n o t i c j e t s o f t e n d i l a t e s a n d a n e u r y s m s c a n f o r m . C h a n g e s i n s h e a r s t r e s s e s a p p e a r t o p l a y l a r g e r r o l e s t h a n c h a n g e s i n p r e s s u r e b e c a u s e t h e p r e s s u r e c h a n g e s a r e r e l a t i v e l y m i n o r c o m p a r e d t o t h e c h a n g e s i n s h e a r . A s a r e s u l t , t h e e n d o t h e l i u m a p p e a r s m o r e s e n s i t i v e t o c h a n g e s i n s h e a r t h a n c h a n g e s i n p r e s s u r e [ 6 , 7 ] .

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3.2 Vascular Disease

I n n u m e r a b l e d i s e a s e s i m p a c t t h e v a s c u l a r s y s t e m , r a n g i n g f r o m r a r e c o n g e n i t a l d e f e c t s a n d g e n e t i c d i s o r d e r s t o c o m m o n a t h e r o s c l e r o s i s . S o m e v a s c u l a r d i s e a s e s t h a t a r e r e l e v a n t t o t h i s t h e s i s w i l l b e b r i e fl y s u m m a r i z e d h e r e .

Aneurysms

A n a n e u r y s m i s t y p i c a l l y d e fi n e d a s a f o c a l a n d p e r m a n e n t d i l a t i o n o f a n a r t e r y t o 1 5 0 % o r m o r e t h a n t h e d i a m e t e r o f a n u n a ﬀe c t e d a r t e r i a l s e g m e n t [ 5 ] , t h o u g h p r e c i s e d e fi n i t i o n s v a r y w i t h l o c a t i o n . A n e u r y s m s c a n p r e s e n t t h r o u g h o u t t h e a r t e r i a l t r e e , t h o u g h s o m e l o c a t i o n s a r e m o r e f r e q u e n t t h a n o t h e r s . C o m m o n s i t e s f o r a n e u r y s m a r e t h e a b d o m i n a l a o r t a ( F i g u r e 3 . 3 ) , t h e t h o r a c i c a o r t a , a n d t h e i n t e r n a l c a r o t i d a r t e r y . A n e u r y s m s c a n b e c a u s e d b y a v a r i e t y o f f a c t o r s : d e g e n e r a t i v e , i n fl a m m a t o r y , c o n g e n i t a l , a m o n g o t h e r s . T h e d i a m e t e r o f t h e a n e u r y s m i s a l s o c o m m o n l y u s e d f o r p r e d i c t i n g t h e g r o w t h r a t e a n d r u p t u r e r i s k [ 4 , 9 – 1 2 ] , e v e n t h o u g h i t i s n o t s t r o n g l y p r e d i c t i v e o f e i t h e r [ 1 0 , 1 3 ] . R i s k f a c t o r s f o r a n e u r y s m s i n c l u d e s m o k i n g , m a l e g e n d e r , a g e , a t h e r o s c l e r o s i s , a n d c o n n e c t i v e - t i s s u e d i s o r d e r s ( e . g . M a r f a n S y n d r o m e ) [ 4 , 1 4 ] . S u r g i c a l t r e a t m e n t t e n d s t o b e d e c i d e d b y t h e d i a m e t e r o f t h e a n e u r y s m , w h i l e a l s o c o n s i d e r i n g t h e a g e o f t h e p a t i e n t [ 1 4 ] . P a t i e n t s w i t h s m a l l e r a n e u r y s m s t h a t d o n o t w a r r a n t s u r g e r y s h o u l d r e c e i v e r e g u l a r s u r v e i l l a n c e i m a g i n g [ 4 ] . E v e n i f a n a n e u r y s m h a s b e e n i d e n t i fi e d , p r e d i c t i n g t h e r u p t u r e r i s k i s e x t r e m e l y c h a l l e n g i n g a s t h e p r e c i s e r e l a t i o n s h i p b e t w e e n fl o w - i n d u c e d f o r c e s a c t i n g o n t h e v e s s e l w a l l , t h e m e c h a n i c a l s t r e n g t h o f t h e w a l l , a n d o t h e r r i s k f a c t o r s i s s t i l l u n k n o w n [ 1 2 ] . T h i s c h a l l e n g e , c o u p l e d w i t h t h e s i l e n t - p r o g r e s s i o n o f a n e u r y s m a l d i s e a s e p r o d u c e s t h e p o o r s u r v i v a l r a t e s a s s o c i a t e d w i t h r u p t u r e d a o r t i c a n e u r y s m s [ 9 , 1 5 , 1 6 ] . A n a b d o m i n a l a o r t i c a n e u r y s m ( A A A ) i s d e p i c t e d i n F i g u r e 3 . 4 a n d c o m p a r e d a g a i n s t a d i s e a s e - f r e e a o r t a .

Figure 3.3: Schematic depiction of an abdominal aortic aneurysm, with the aneurysm marked using the arrow. Reproduced with permission from [8].

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A

B

D

C

Figure 3.4: Example images from healthy young volunteer (A, B) and patient with AAA (C, D). (A) and (C) show balanced-images of the abdominal cavity, and (B) and (D) show the contrast-enhanced MR angiography for the same subject. The aorta is marked using red arrows in (A) and (C). The dashed red lines in (B) and (D) depict the level of the aorta shown in (A) and (C). The AAA is clearly visible in (D).

Atherosclerosis

Atherosclerosis is the number one cause of death worldwide, primarily by caus-ing myocardial infarctions and stokes [17, 18]. Simply put, atherosclerosis involves the accumulation of lipids and fibrous tissues in the large arteries. Atherosclerosis most often develops asymptomatically, though in the later stages of atherosclerosis, the large plaques force vessel remodeling that in turn, causes stenoses. Stenoses can radically alter the blood flow through the vessel in ques-tion, potentially causing high-velocity jets and turbulent flow, which may dam-age the vessel wall. However, the primary risk is dictated by the rupture risk of the plaque and the emboli generated. Plaque composition and structure can be used to help determine the risk posed by the plaque. For example, plaques with a large lipid rich core have a higher rupture risk. A simplified depiction of the progression of atherosclerosis is shown in Figure 3.5.

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CHAPTER 3. PHYSIOLOGICAL BACKGROUND 1 0

Normal

Vessel Fatty Streaks Fibro-fatty Plaque Vulnerable Plaque

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Chapter 4 Magnetic Resonance Imaging

Magnetic Resonance Imaging (MRI) provides an unparalleled ability for inves-tigating the human body. MRI can generate anatomical images with excellent soft-tissue contrast in any area of the body, without ionizing radiation. Comple-menting those abilities, MRI can generate functional images that describe the physiology of the subject, describing, for example, the passage of blood through the heart or the brain’s response to visual stimuli. Merging anatomical informa-tion such as the diameter of the thoracic aorta with physiological informainforma-tion such as the amount of turbulence in that same region provides clinicians and researchers the opportunity to understand the mechanisms behind a large range of pathologies.

This chapter will describe the basic principles behind image generation in MRI, the goals and challenges of MRI investigations of the cardiovascular system, and the three major types of imaging used in this thesis: Contrast-Enhanced (CE) MR angiography, Dixon, and 4D Flow MRI. This chapter is primarily based on the following texts: [12, 19–25] .

4.1 Basic MRI Principles

Sub-atomic particles like electrons and protons are magnetic. As a result of this, they spin and have a spin angular momentum and a magnetic moment. The spin angular momentum and magnetic moment are proportional to each other by the gyromagnetic ratio ( ). Considering the hydrogen proton, = 2.68 · 108

rad/s/T. Exposing the protons to an external magnetic field B0 causes the

magnetic moments, or spins, to align and precess about that field. The rate of angular precession is described by the Larmor equation:

!0= B0, (4.1)

where !0 denotes the Larmor frequency. Field strengths of 1.5, 3, and 7 T

are used in whole-body MRI scanners, and therefore the Larmor frequencies are approximately 64, 128, and 298 MHz, respectively. Changing the magnetic field, and therefore the Larmor frequency, is used in MRI to generate images.

Instead of considering spins individually, it is more convenient to aggregate them and consider “packets”. With this choice, a classical physics representation can be used, and the packet of spins can be described using its net magnetization

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CHAPTER 4. MAGNETIC RESONANCE IMAGING 12 vector M. Normally, M is aligned with the external magnetic field B0, and this

is known as the “relaxed state”, but when radio-frequency (RF) pulses that match the Larmor frequency are applied, the orientation of M can be altered. As the RF pulse stops, the magnetization undergoes a relaxation period and re-aligns itself with the B0field. The relaxation and re-alignment of M is described

using the Bloch equation: dM dt = M⇥ B Mx T2 ˆ x My T2 ˆ y Mz M0 T1 ˆ z , (4.2)

where T1is the spin-lattice (or longitudinal) relaxation time and T2is the

spin-spin (or transversal) relaxation time. T1 describes the time it takes for the

longitudinal component of the magnetization vector M to recover to 63 of its original value M0. T1 measures the return of excited or perturbed spins to

their natural, relaxed state. T2 describes the time it takes for the transverse

component to decay to 63% of its original value. T2decay is a result of the loss

of phase-coherence (or synchronicity in spin) within the packet of spins. T1and

T2 are both tissue-specific parameters that are exploited to generate contrast

and distinguish between diﬀerent tissues in the body. To generate the images and use T1 or T2 contrast patterns to distinguish between tissues, the

time-varying signal generated by the magnetization vector returning to alignment is measured by the receiving coils of the MR scanner. This signal is called the free-induction decay signal, and electromagnetic induction is used by the receiver coils to measure it.

To generate useful images, the signal generated by the relaxation of spin packets must be spatially located. To do this, magnetic field gradients are used. These gradients cause the magnetic field to vary spatially over the object being imaged and therefore change the Larmor frequency of the spin packets as a result of their spatial position. Spins will also accumulate a phase-shift as a result of these gradients and the length of time they are exposed to the gradients.

Using a combination of magnetic field gradients in diﬀerent directions across the object in the scanner and appropriate timing of the RF excitation pulses, signal can be generated that is able to be spatially localized in the object. The order and manner in which the gradients and RF pulses are applied is known as the pulse sequence. The signal induced in the receiver coil(s) of the scanner are split into real and imaginary components and stored in a spatial-frequency domain known as k-space. The resulting complex signal is a function of the applied gradient waveform and the object being imaged. This complex-valued signal is also the Fourier transform of the tissue-slice in that particular spatial location.

k-space is constructed as a grid with two dimensions (for a 2D image, three for a 3D image, and so on), kx and ky, that correspond to the horizontal and

vertical axes in an image. Each dimension represents the spatial frequencies of the image in that direction. The centre of k-space contains the lowest spatial frequencies, and the outer-regions contain the higher spatial frequencies. The centre, therefore, contains most of the key image information and is generally regarded as most important. The pulse sequences for a specific MR acquisition are designed to fill k-space, by acquiring data that corresponds to all spatial frequencies, so that the image can be constructed. After k-space is filled, taking the inverse Fourier transform yields the complex-valued data in the image do-main. The modulus of this data yields the commonly used magnitude images.

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CHAPTER 4. MAGNETIC RESONANCE IMAGING 13 The complex component (i.e. the phase), is also often of value, in particular for phase-contrast MRI.

The physical space that the image represents is known as the field-of-view (FOV). The number of samples in k-space along a particular dimension and the FOV determine the actual resolution of the image. The FOV in a particular direction is inversely proportional to the number of samples in k-space in that direction. For direction n, F OVn = 1/ kn, where knis the spacing between

two adjacent k-space samples in direction n. Similarly, the spatial resolution in an image in a given direction, n is inversely proportional to the size of k-space

in that direction: n = 1/(kn,max kn,min). Combining these two equations

allows for the calculation of the minimum number of samples necessary for the desired FOV and resolution:

Nn= F OV n = (kn,max kn,min) kn . (4.3)

In 2D imaging, the spatial resolution is discussed in terms of pixel size (i.e.

x· y) and the slice thickness. In 3D imaging, the voxel size (i.e. x· y· z) is

commonly reported.

4.2 MRI of the Cardiovascular System

Cardiovascular MRI (CMR) has become valuable in assessing cardiovascular disease and function, as a result of its non-invasive nature, excellent soft-tissue contrast, ability to create anatomical and functional imagery, and lack of ioniz-ing radiation. Many techniques have been developed specifically for cardiovas-cular applications, and CMR has come to include a range of imaging techniques including angiography, T1 and T2 mapping, fat mapping, flow imagery, strain

imagery, and static- or cine-images of the heart. However, CMR presents sev-eral specific challenges, for example, cardiac and respiratory motion, that add an additional layer of complexity to the task at hand. Each technique has its own practical requirements, though CMR techniques commonly require cardiac and respiratory gating to reduce or remove motion artifacts and acquire tem-porally resolved data. Therefore, cardiac gating and respiratory gating will be described in this section.

4.2.1 Cardiac Gating

Several CMR acquisitions require data acquisition in synchrony with the cardiac cycle, to ensure the heart is at the same location when all spatial frequencies of the image are recorded, and to generate time-resolved flow data for the entire heartbeat. Typically, data from an ECG (preferred), or pulse oximeter is used to detect the heart beat and provide timing information. There are two general strategies for cardiac gating: prospective gating, which uses a predefined acqui-sition time-window and therefore only allows data acquiacqui-sition under a specific fraction of the cardiac cycle [26]; and, retrospective gating, which permits the acquisition of data during the entire cardiac cycle (Figure 4.1) [27, 28].

Prospective gating, also known as “triggering”, often uses the R-wave of the ECG to determine when to begin data acquisition. This type of gating is common for single-timeframe images of the heart, where the image is acquired

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CHAPTER 4. MAGNETIC RESONANCE IMAGING 1 4

Prospective Retrospective ECG

Figure 4.1: Prospective Gating (also known as triggering) acquired data only during specific windows. Retrospective gating continually collects data and groups the data according the the phase of the cardiac cycle during image reconstruction.

b e t w e e n b e a t s t o m i n i m i z e m o t i o n . R e t r o s p e c t i v e g a t i n g a c q u i r e s d a t a d u r i n g t h e e n t i r e c a r d i a c c y c l e , a n d u s e s t h e E C G s i g n a l t o r e c o n s t r u c t t h e d a t a a f t e r a l l t h e d a t a w a s a c q u i r e d . T h e h e a r t r a t e i s n o t a c o n s t a n t s i g n a l , s o t h e n u m b e r o f a c q u i r e d t i m e f r a m e s c a n v a r y f r o m b e a t - t o - b e a t . D u r i n g r e c o n s t r u c t i o n , a t e m p o r a l s l i d i n g - w i n d o w a p p r o a c h i s o f t e n u s e d t o r e c o n s t r u c t t h e d a t a i n t o a s e t o f e v e n l y d i s t r i b u t e d t i m e f r a m e s . R e t r o s p e c t i v e c a r d i a c g a t i n g i s t y p i c a l l y u s e d f o r c i n e - i m a g i n g .

4.2.2 Respiratory Motion Suppression

S i m i l a r t o c a r d i a c g a t i n g , m a n y C M R a c q u i s i t i o n s r e q u i r e r e s p i r a t o r y m o t i o n s u p p r e s s i o n t o p r e v e n t a r t i f a c t s a n d b l u r r i n g a s a r e s u l t o f r e s p i r a t o r y m o t i o n [ 2 9 ] . A c q u i s i t i o n s w i t h s h o r t e r s c a n t i m e s c a n b e p e r f o r m e d b y i n s t r u c t i n g t h e s u b j e c t t o h o l d t h e i r b r e a t h f o r a s h o r t p e r i o d o f t i m e ( 1 5 2 0 s e c o n d s m a x -i m u m ) , d u r -i n g w h -i c h t h e s c a n -i s p e r f o r m e d . H o w e v e r , t h -i s c r e a t e s d -i ﬃc u l t y f o r m a n y s u b j e c t s o r i s s i m p l y n o t p o s s i b l e b e c a u s e t h e a c q u i s i t i o n t a k e s t o o l o n g . T h e r e f o r e , t o m o n i t o r t h e s u b j e c t ’ s r e s p i r a t i o n a n d d e c i d e w h e n t o a c q u i r e d a t a , b e l l o w s o r n a v i g a t o r s c a n s a r e c o m m o n l y u s e d . R e s p i r a t i o n b e l l o w s a r e p l a c e d o n t h e s u b j e c t w h i l e i n s i d e t h e s c a n n e r , a n d t h e y p h y s i c a l l y m o n i t o r t h e m o v e m e n t o f t h e c h e s t o r b e l l y u s i n g p r e s s u r e s e n s o r s . R e s p i r a t o r y n a v i g a t o r s c a n s a r e t y p i c a l l y p e n c i l - b e a m o r c r o s s - p a i r e x c i t a t i o n s c a n s t h a t a c q u i r e a n i m a g e t h a t r e p r e s e n t s t h e m o t i o n o f t h e d i a p h r a g m t h r o u g h t h e c a r d i a c c y c l e . T h e s e s c a n s a c q u i r e a c o l u m n o f v o x e l s a c r o s s t h e l u n g - l i v e r i n t e r f a c e , s o a s t o m o n i t o r t h e m o t i o n o f t h e d i a p h r a g m . U s i n g e i t h e r n a v i g a t o r s c a n s o r b e l l o w s , d a t a a c q u i s i t i o n i s o n l y a l l o w e d i f t h e c u r r e n t r e s p i r a t o r y p o s i t i o n f a l l s w i t h i n a s p e c i fi e d a c c e p t a n c e w i n d o w . T h i s a c c e p t a n c e w i n d o w i s t y p i c a l l y d e fi n e d a r o u n d t h e e n d e x p i r a t o r y p h a s e . T h e s i z e o f t h i s w i n d o w i s a t u n e a b l e p a r a m -e t -e r , w h -e r -e l a r g -e r a c c -e p t a n c -e w i n d o w s w o u l d i n c u r m o r -e r -e s p i r a t o r y m o t i o n i n t h e fi n a l i m a g e ( a n d t h e r e f o r e a d e g r a d a t i o n i n i m a g e q u a l i t y ) , b u t i n c r e a s e t h e e ﬃc i e n c y o f t h e s c a n a s d a t a c a n b e a c q u i r e d f o r l o n g e r p e r i o d s o f t i m e .

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CHAPTER 4. MAGNETIC RESONANCE IMAGING 15

4.3 Phase-Contrast MRI

Flow assessment is widely used in the evaluation and grading of cardiovascular disease. For example, the severity of stenoses in the aortic valve are often graded with respect to the peak velocity measured using ultrasound. MRI also oﬀers the ability to investigate hemodynamics in vivo, with numerous advantages over other techniques. The ability to retrospectively examine data, plan investiga-tions irrespective of acoustic windows, and the decreased observer dependencies make MRI flow-investigations valuable. 4D Flow MRI is a phase-contrast (PC)-MRI technique that measures the velocity of fluids in three spatial dimensions and three velocity dimensions through time [22, 23, 30, 31]. 4D Flow MRI was developed from the more commonly used two-dimensional (2D) PC-MRI technique that is used clinically to measure velocities through a pre-positioned plane [32–35]. MRI-based flow imaging is commonly used to, for example: ex-amine congenital heart conditions, aortic pathologies, aneurysms in cerebral or thoracic circulation, and the eﬀects of valvular pathologies, among other uses [11, 22, 31, 36–49].

In this section, the working principles behind MRI based flow measurements will be described. Following this, the extension of the basic principles to create 4D Flow MRI will be described, as well as typical scan properties and consid-erations. Finally, the unique possibility to map turbulence using 4D Flow MRI will be presented.

4.3.1 PC-MRI Velocity Mapping

PC-MRI flow imaging is based on the fact that spins moving in parallel to a magnetic field gradient will acquire some phase shift, proportional to their velocity[30, 32–35]. This provides MRI with an inherent sensitivity to motion. In some scenarios, this motion must be compensated for as it degrades the image quality, but for PC-MRI velocity mapping, this is critical.

The phase shift accumulated by the spin packet, , is proportional to its velocity. A specific motion sensitivity is normally desired, and so the motion sensitivity is controlled by adding bipolar gradients and other gradients. Assum-ing a symmetric distribution of velocities about the mean velocity U, the phase shift is related to the mean velocity and the motion sensitivity kv according to:

= kvU + add, (4.4)

where add represents an additional phase-shift incurred due to

inhomo-geneities in the B0magnetic field. addis independent of kv and can be

elim-inated using the phase-shifts from two sets of data, known as flow-encoding segments, if they are generated using diﬀerent motion sensitivities. Using the two datasets, the mean velocity can be calculated as:

U = / kv. (4.5)

Practically speaking, one of the critical parameters for velocity mapping is the velocity encoding range (VENC), which relates the flow sensitivity kv to

velocity value which yields a full phase-shift of ⇡ radians: V EN C = ⇡

kv

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CHAPTER 4. MAGNETIC RESONANCE IMAGING 1 6 I f t h e t r u e v e l o c i t y i s l a r g e r t h a n ±V E N C , t h e m e a s u r e d v e l o c i t y i s a l i a s e d a n d w r a p s b a c k i n t o t h a t r a n g e ( p h a s e - w r a p p i n g ) , p r e v e n t i n g i t f r o m b e i n g u n a m b i g u o u s l y d e t e r m i n e d . T h e r e f o r e , t h i s s c a n p a r a m e t e r i s s e t w i t h c a r e b e f o r e t h e a c q u i s i t i o n t o m i n i m i z e p h a s e - w r a p p i n g . H o w e v e r , s e l e c t i n g a V E N C t h a t i s t o o l a r g e h a s a n e g a t i v e e ﬀe c t o n t h e v e l o c i t y - t o - n o i s e r a t i o ( V N R ) . T h e V N R i s r e l a t e d t o t h e s i g n a l - t o - n o i s e r a t i o ( S N R ) ( a s m e a s u r e d i n t h e m a g n i t u d e d a t a , n o t t h e p h a s e d a t a ) a n d V E N C b y : V N R = V EN C SN R . ( 4 . 7 ) T h i s t e c h n i q u e m u s t b e e x t e n d e d i n t h e t e m p o r a l d o m a i n t o c r e a t e u s e f u l d a t a i n v i v o . T h i s e n t a i l s t h e c r e a t i o n o f t h e t e m p o r a l d o m a i n . P u t s i m p l y , t h i s r e q u i r e s t h e a c q u i s i t i o n o f a f u l l k- s p a c e d a t a s e t f o r e a c h p o i n t i n t i m e r e q u i r e d . D a t a i s a c q u i r e d a s d e s c r i b e d i n S e c t i o n s 4 . 1 a n d 4 . 2 . 2 . T h i s p r o c e s s c a n b e l e n g t h y , r e q u i r i n g s e v e r a l t h o u s a n d c a r d i a c c y c l e s w i t h o u t a c c e l e r a t i o n t e c h n i q u e s .

4.3.2 4D Flow MRI

E x t e n d i n g t h e fl o w - e n c o d i n g t e c h n i q u e t o c o n s i d e r t h r e e v e l o c i t y d i r e c t i o n s t h r o u g h o u t t h e c a r d i a c c y c l e i n a v o l u m e i n v o l v e s t h e u s e o f a d d i t i o n a l fl o w -e n c o d i n g s -e g m -e n t s t o c r -e a t -e a 3 D v -e l o c i t y v -e c t o r t h r o u g h t i m -e f o r -e a c h v o x -e l1_{[ 3 0 ] .} T h i s r e s u l t s i n a n a c q u i r e d d a t a s e t t h a t c o n t a i n s f o u r f o u r d i m e n s i o n a l c o m p l e x -v a l u e d -v o l u m e s f r o m w h i c h t h e f o l l o w i n g a r e n o r m a l l y g e n e r a t e d : t h e m a g n i t u d e v o l u m e w h i c h d e p i c t s a n a t o m y , a n d t h r e e v e l o c i t y e n c o d e d v o l u m e s ( F i g u r e 4 . 2 ) . T h i s s e c t i o n w i l l d e t a i l s p e c i fi c c o n s i d e r a t i o n s f o r t h e u s e o f 4 D F l o w M R I , i n -c l u d i n g a -c -c e l e r a t i o n t e -c h n i q u e s , s i g n a l q u a l i t y , a n d b a -c k g r o u n d p h a s e - o ﬀs e t s . 50 100 150 200 250 300 -2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 50 100 150 200 250 300 -2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 50 100 150 200 250 300 -2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 50 100 150 200 250 300 -2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 0.5 1 1.5 2 0.5 1 1.5 2 0.5 1 1.5 2 50 100 150 200 250 300 -2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 0.5 1 1.5 2 0.5 1 1.5 2 0.5 1 1.5 2 50 100 150 200 250 300 -2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 0.5 1 1.5 2 0.5 1 1.5 2 0.5 1 1.5 2 50 100 150 200 250 300 -2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 0.5 1 1.5 2 0.5 1 1.5 2 0.5 1 1.5 2

v

x 50 100 150 200 250 300 -2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

!

x 50 100 150 200 250 300 -2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 50 100 150 200 250 300 -2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 50 100 150 200 250 300 -2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 50 100 150 200 250 300 -2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

!

z 50 100 150 200 250 300 -2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 0.5 1 1.5 2 0.5 1 1.5 2 0.5 1 1.5 2 50 100 150 200 250 300 -2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 0.5 1 1.5 2 0.5 1 1.5 2 0.5 1 1.5 2 50 100 150 200 250 300 -2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 0.5 1 1.5 2 0.5 1 1.5 2 0.5 1 1.5 2 50 100 150 200 250 300 -2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 0.5 1 1.5 2 0.5 1 1.5 2 0.5 1 1.5 2

v

z 50 100 150 200 250 300 -2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 50 100 150 200 250 300 -2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 50 100 150 200 250 300 -2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 50 100 150 200 250 300 -2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

!

y 50 100 150 200 250 300 -2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 0.5 1 1.5 2 0.5 1 1.5 2 0.5 1 1.5 2 50 100 150 200 250 300 -2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 0.5 1 1.5 2 0.5 1 1.5 2 0.5 1 1.5 2 50 100 150 200 250 300 -2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 0.5 1 1.5 2 0.5 1 1.5 2 0.5 1 1.5 2 50 100 150 200 250 300 -2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 0.5 1 1.5 2 0.5 1 1.5 2 0.5 1 1.5 2

v

y

FE

X

FE

₀

FE

_Y

FE

_Z Flow-Encoding Segments (FE)

Figure 4.2: Example dataset from 4D Flow MRI. The four flow-encoding segments, generated at each point in the cardiac cycle, create maps of the mean velocity in each

direction (Vx, Vy, Vz), and turbulence intensity maps ( x, y, z).

N o n - a c c e l e r a t e d 4 D F l o w M R I a c q u i s i t i o n s h a v e i m p r a c t i c a l l y l o n g s c a n t i m e s , a n d t h e r e f o r e c l i n i c a l u s e o f t h i s t e c h n i q u e d e m a n d s t h e u s e o f a c c e l e r a t i o n

1_{3 Spatial dimensions of velocity information, plus the temporal dimension yields the ’4D}

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CHAPTER 4. MAGNETIC RESONANCE IMAGING 17 techniques [22]. For example, an acquisition with a 112 ⇥ 48 phase-encoding lines would require 5376 cardiac cycles (assuming 1 k-space line per beat). At a steady 60 beats per minute and with 100% respiratory navigator eﬃciency this acquisition would take roughly 90 minutes. There are few healthy volunteers who can tolerate such a scan time, let alone patients.

To improve scan time, several strategies exist. One way to reduce the ac-quisition time (Tacq) is to increase the number of k-space lines (often called

“segments”, Nseg) acquired per cardiac cycle. Doubling this, to 2 k-space lines

per cycle, would reduce Tacq by a factor of 2, but cause a reduction in the

mini-mum temporal resolution (estimated by: Tres= 4· TR· Nseg). Additionally, the

size of k-space can be reduced by not acquiring the outer edges. This incurs a SNR penalty and will aﬀect the spatial resolution, but it can yield substantial Tacqreductions, potentially 20-25%. Parallel imaging techniques such as SENSE

(SENSitivity Encoding), which uses coil-sensitivity maps to reconstruct partial FOV images from each coil, or GRAPPA (GeneRalized Auto-calibrating Par-tial Parallel Acquisition), which under-samples the phase-encoding directions and attempts to compensate for this in k-space, are commonly used in 4D Flow MRI. Both strategies can also be combined with acceleration in the temporal direction, resulting in the kt-SENSE and kt-GRAPPA techniques. Combining all of these techniques, acceleration factors >5 are possible without significant reduction in data quality, and have been used in a clinical setting, bringing down Tacq towards the 10-15 minute range [23].

PC-MRI velocity mapping techniques have several common data quality issues, including: Maxwell terms and eddy currents causing phase errors, gradi-ent field distortions causing phase-oﬀset errors, phase-wrapping causing velocity aliasing, and low SNR/VNR as a result of high scan acceleration. Appropriate data processing is required to correct or compensate for these errors and ensure accurate quantification of hemodynamics is possible.

Errors from Maxwell terms are a result of using switching magnetic fields for spatial and velocity encoding [22, 23]. Switching the gradient generates addi-tional transverse magnetic field components that cause the underlying B0field

vector to be misaligned. These additional components are called “concomitant field” or “Maxwell term” errors. These errors can be estimated with knowledge of the applied gradients and corrected for during reconstruction of the 4D Flow acquisition.

Eddy currents are similarly caused by the switching of the magnetic field gradients, though in this case, the switches result in changes in magnetic flux and induce eddy currents in the conducting components of the scanner. These eddy currents can cause changes in the desired gradient strengths and duration and cause spatially varying phase-errors. Eddy currents need to be corrected for in post-processing, and correction strategies often use weighted fits to static tissue [50–52].

Gradient field distortions are deviations from linearity in the magnetic field gradients that tend to increase with distance from the isocentre of the mag-net. These distortions cause image warping in the magnitude images, but also phase-oﬀsets causing velocity errors. These errors must be can be substantial, especially when the FOV is large and there are areas of interest distant from magnet isocentre (e.g. infrarenal AAAs in whole-aorta acquisition volumes). Correction for gradient field distortions can be done during image reconstruc-tion, using knowledge of the non-linearities [22, 23].

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CHAPTER 4. MAGNETIC RESONANCE IMAGING 18 Phase-wrapping, or velocity aliasing, is a result of the real velocity being larger than the VENC. VENC is set prior to acquisition2_{, to some maximum}

value that corresponds to a maximum phase diﬀerence of ±⇡. Therefore, if the velocity is greater than this level, the phase will be aliased (i.e. it falls out of the [ ⇡, +⇡]interval) and is wrapped back onto itself. This obviously causes issues during flow quantification. VENC can be set based on the estimated maximum velocity with a safety margin to prevent wrapping, though at the cost of VNR (Equation 4.7). This implies that VENC should be set as low as possible, given the expected maximum velocities, to optimize VNR. This approach sometimes leads to velocity aliasing, because the expected maximum was too low. Post-processing steps can attempt to correct for aliasing by comparing the wrapped voxel to the surrounding region, and assuming that the phase-change between adjacent areas is less than ⇡, though in some cases manual corrections may be required.

As previously discussed, VENC should optimally be set as low as possible to optimize the VNR [22]. However, in some acquisitions, there is a large range of expected flow velocities which causes the maximum expected velocity to rise and put slower-flow areas at risk of being dominated by the effects of noise. Whole-aorta acquisitions that provide coverage of the aortic valve, with its high velocity jet flow, and the much slower flow in the distal areas of the abdominal aorta, are a prominent example. A hypothetical subject presenting with both an aortic stenosis, causing extreme jet flows, and an AAA, with areas at risk of flow stasis constitutes an extreme case. In this example, the jet flow would dictate a high VENC but such a choice would likely substantially increase the noise in the AAA and prevent accurate flow analysis there. This case could merit of two separate acquisitions, each focused on a different anatomical re-gions to ensure adequate VNR and minimal velocity aliasing. Fortunately, the development of multi-VENC acquisitions enables a single-acquisition volume while conserving (and potentially improving) VNR. Unfortunately, as tradeoffs are common in MRI, a multi-VENC acquisition will increase Tacq. In addition

to multi-VENC strategies, VNR/SNR can be improved by using gadolinium-based contrast-agents. The use of contrast-agents can compensate for parallel acquisition strategies that decrease VNR/SNR.

4.3.3 Turbulence Mapping

Velocity measurements using 4D Flow MRI represent the mean velocity from a given voxel [53–56]. However, turbulence is characterized by chaotic, random fluctuations in velocity magnitude and direction. The velocity in a given voxel, u, can therefore be described as having a mean and a fluctuating velocity (U and u0_{, respectively):}

ui= Ui+ u0i, (4.8)

where i represents an arbitrary direction. The turbulent intensity in each direc-tion, i, is defined as the standard deviation of the fluctuating component:

i=

q (u02

i ) . (4.9)

2_{VENC has typical values of 120-180 cm/s for thoracic or cardiac acquisitions, though it}

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CHAPTER 4. MAGNETIC RESONANCE IMAGING 19 The mean value of u0

iis zero by definition. This approach for statistical

sepa-ration of the mean and fluctuation components of velocity is known is Reynolds decomposition [57].

While the mean velocity in a voxel can be accurately measured using 4D Flow MRI, current temporal and spatial resolutions are insuﬃcient to measure u0. However, the presence of disturbed and turbulent flows attenuates the MRI signal magnitude as a result of the distribution of velocities (spins) within that voxel. The strength of signal attenuation depends on the characteristics of the bipolar gradients used during imaging, and the spread of velocities. Modeling the spread of velocities using a gaussian distribution, and given known gradi-ents, we can use the magnitude of the signal to estimate the intravoxel velocity standard deviation (IVSD). IVSD is a measure of turbulence intensity. IVSD can be calculated as:

IV SD = i= 1 kv s 2_{· ln(}|Si(0)| |Si(kv)| ) , (4.10)

where Si(0) and Si(kv) are the signals acquired with zero motion sensitivity

and kv motion sensitivity, respectively. This technique is known as IVSD- or

turbulence-mapping, and is depicted in Figure 4.3. It enables estimation of the turbulence intensity in any desired direction. iis also known as the Reynolds

or Turbulent Normal Stress in direction i and forms the diagonal of the Reynolds Stress tensor R: R = ⇢u0_iu0_j⇠ ⇢ 2 6 4 u0₂ 1 u 0 1u 0 2 u 0 1u 0 3 u0₂u0₁ u02 2 u 0 2u 0 3 u0 3u 0 1 u 0 3u 0 2 u 0₂ 3 3 7 5 , (4.11)

where ⇢ is the fluid density. This tensor is 2nd order symmetric and describes the average momentum flux as a result of the velocity fluctuations.

However, it is often convenient to have a non-directional estimate of the tur-bulence intensity, and therefore the turbulent kinetic energy (TKE) is also of interest. TKE is a direction-independent measure of the energy of the fluctuat-ing velocity components, and can be calculated as:

T KE =1 2⇢ 3 X i=1 2 i , (4.12)

where i is the turbulence intensity in each principal direction i = x, y, z in

a given voxel. Using this technique, a voxel-by-voxel map of the TKE can be created alongside the velocity information.

As IVSD-mapping uses the magnitude of the MRI signal, instead of the phase, as would be the case for velocity mapping, there are diﬀerent consider-ations for optimizing data quality. The VENC parameter defines the dynamic range of velocities that can be measured without phase-aliasing, but also has some eﬀect on IVSD-mapping. It can be shown that VENC defines the point of maximum IVSD sensitivity, ˜:

˜ = 1 kv

= ⇡

V EN C . (4.13)

When the optimal VENC values for velocity- and IVSD-mapping are similar, both quantities can be accurately reconstructed from the same acquired data.

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Figure 4.3: Schematic description of turbulence mapping approach. Two datasets are used, one with low (or zero) motion sensitivity and a second with high motion sensitivity. The loss in signal magnitude between these two images is used to calculate the turbulence intensity.

4.4 Contrast-Enhanced MR Angiography

C o n t r a s t - E n h a n c e d M R a n g i o g r a p h y ( C E M R A ) a c q u i s i t i o n s a r e d e s i g n e d t o p r o d u c e b r i g h t - b l o o d i m a g e s o f t h e v a s c u l a t u r e . C E M R A c a n b e p e r f o r m e d o n j u s t a b o u t a n y s c a n n e r a n d u s e d f o r a w i d e r a n g e o f c l i n i c a l q u e s t i o n s . C o m -p a r e d t o T i m e - o f - F l i g h t ( T O F ) o r P C - M R A t e c h n i q u e s t h a t a r e a l s o u s e d f o r e x a m i n i n g v a s c u l a t u r e , t h e y a r e f a s t e r , w i t h b e t t e r s p a t i a l r e s o l u t i o n e v e n w h i l e h a v i n g a l a r g e r F O V [ 1 9 ] . I n a d d i t i o n , t h e y c r e a t e i m a g e s t h a t a r e i n d e p e n d e n t o f fl o w c h a r a c t e r i s t i c s . H o w e v e r , t h e y r e q u i r e t h e u s e o f o f t e n e x p e n s i v e a n d p o -t e n -t i a l l y h a r m f u l c o n -t r a s -t - a g e n -t s a n d i n -t r a v e n o u s a c c e s s . F i n a l l y , i m p l e m e n -t i n g a fi r s t - p a s s c o n t r a s t - e n h a n c e m e n t p r o t o c o l c a n b e c h a l l e n g i n g g i v e n t h e v a r i a n c e i n p a t i e n t s ’ c a r d i a c o u t p u t . I n t h i s s e c t i o n , t h e a c q u i s i t i o n p a r a m e t e r s w i l l b e d i s c u s s e d a l o n g s i d e t h e c h a l l e n g e o f c o n t r a s t t i m i n g , a n d t h e u s e s f o r t h i s d a t a . C E M R A a c q u i s i t i o n s a r e t y p i c a l l y T1- w e i g h t e d 2 D o r 3 D s p o i l e d g r a d i e n t e c h o s e q u e n c e s . G a d o l i n i u m - b a s e d c o n t r a s t a g e n t s a r e u s e d t o s i g n i fi c a n t l y s h o r t e n t h e T1r e l a x a t i o n t i m e o f b l o o d . T h i s i m p l i e s t h a t t h e b l o o d w i t h w h i c h t h e c o n t r a s t a g e n t h a s m i x e d w i t h h a s t h e s t r o n g e s t s i g n a l c o m p a r e d t o t h e s u r -r o u n d i n g t i s s u e . TE a n d TRa r e t y p i c a l l y a s s h o r t a s p o s s i b l e , w i t h TE t y p i c a l l y 1 - 2 m s , a n d TR2 - 5 m s . F l i p a n g l e s a r e t y p i c a l l y b e t w e e n 1 5 - 4 0 º, w h e r e h i g h e r fl i p a n g l e s i n c r e a s e t h e b a c k g r o u n d s u p p r e s s i o n b u t c a n a l s o a t t e n u a t e s o m e o f

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CHAPTER 4. MAGNETIC RESONANCE IMAGING 2 1 t h e s i g n a l i n t h e v e s s e l s o f i n t e r e s t . P a r t i a l k- s p a c e a c q u i s i t i o n s a n d t h o s e t h a t i n i t i a l l y fi l l t h e c e n t r e o f k- s p a c e a r e p r e f e r r e d t o m i n i m i z e t h e a c q u i s i t i o n t i m e t o e n s u r e t h e i m a g e c o n t a i n s o n l y t h e a r t e r i a l p h a s e . T y p i c a l l y , C E M R A o f t h e c a r o t i d a r t e r i e s a r e a c q u i r e d u s i n g a s a g i t t a l s l a b , w i t h t h e a n t e r i o r - p o s t e r i o r d i r e c t i o n h a v i n g t h e s h o r t e s t F O V t o m i n i m i z e o n e o f t h e p h a s e e n c o d i n g d i -r e c t i o n s . S i m i l a -r l y , s c a n s o f t h e t h o -r a c i c a o -r t a o -r A A A s a -r e o b l i q u e s a g i t t a l s l a b s o r i e n t e d i n p a r a l l e l t o t h e a o r t i c a r c h . E x a m p l e C E M R A i m a g e s o f t h e a b d o m e n a n d t h e n e c k a r e s h o w n i n F i g u r e 4 . 4 .

A

B

Figure 4.4: Example maximum intensity projections of CEMRA acquisitions for the abdomen of a subject with AAA (A), and the neck of a healthy subject (B).

F o r a h i g h - q u a l i t y C E M R A , t h e a c q u i s i t i o n m u s t b e s y n c h r o n i z e d w i t h t h e d u r a t i o n o f a r t e r i a l e n h a n c e m e n t a n d t o p r e c e d e v e n o u s i n v o l v e m e n t ( F i g u r e 4 . 5 ) . F o r t h i s r e a s o n , fi r s t - p a s s c o n t r a s t e n h a n c e m e n t i s u s e d t o e n s u r e t h a t t h e a r t e r i a l s y s t e m h a s t h e s t r o n g e s t s i g n a l e n h a n c e m e n t . “ F i r s t - P a s s ” r e f e r s t o t h e fi r s t t i m e t h e c o n t r a s t b o l u s p a s s e s t h r o u g h t h e a r t e r i a l c i r c u l a t i o n a f t e r l e a v i n g t h e h e a r t . C o n t r a s t d o s a g e , i n j e c t i o n r a t e , a n d t i m i n g a r e a l l k e y f a c t o r s t h a t d e t e r m i n e i m a g e q u a l i t y . C o n t r a s t d o s a g e i s s t a n d a r d i z e d b y s u b j e c t m a s s . A t y p i c a l s i n g l e d o s e i s 0 . 1 m m o l / k g o f b o d y m a s s , w i t h a n u p p e r l i m i t o f 0 . 3 m m o l / k g . F o r w h o l e - a o r t a o r A A A i n v e s t i g a t i o n s , a d o u b l e d o s e i s p r e f e r r e d t o e n s u r e t h a t t h e a m o u n t o f c o n t r a s t i s s u ﬃc i e n t t o e n h a n c e t h e e n t i r e F O V . T h e i n j e c t i o n r a t e a ﬀe c t s t h e a m o u n t o f c o n t r a s t d i s p e r s i o n , p e a k i n t e n s i t y o f a r t e r i a l e n h a n c e m e n t , a n d t h e c i r c u l a t i o n t i m e ( F i g u r e 4 . 6 ) . A s c o n t r a s t i s i n j e c t e d i n t r a v e n o u s l y , t h e c o n t r a s t m a t e r i a l m i x e s w i t h u n - e n h a n c e d v e n o u s b l o o d a n d a c e r t a i n a m o u n t o f c o n t r a s t - d i s p e r s i o n o c c u r s e n r o u t e t o t h e h e a r t . I n c r e a s e d c o n t r a s t d i s p e r s i o n d e c r e a s e s t h e p e a k a r t e r i a l e n h a n c e m e n t . I n c r e a s -i n g t h e -i n j e c t -i o n r a t e l o w e r s t h e a m o u n t o f d -i s p e r s -i o n a n d t h e r e f o r e t h e p e a k a r t e r i a l e n h a n c e m e n t i s i n c r e a s e d b e c a u s e t h e p e a k c o n t r a s t c o n c e n t r a t i o n i s m a x i m i z e d . H o w e v e r , i n c r e a s i n g t h e i n j e c t i o n r a t e m e a n s t h a t t h e c o n t r a s t b o -l u s t r a n s i t s t o t h e v a s c u -l a t u r e o f i n t e r e s t f a s t e r ( i . e . s h o r t e n e d c i r c u -l a t i o n t i m e ) . T i m i n g t h e a c q u i s i t i o n t o c o i n c i d e w i t h a r t e r i a l e n h a n c e m e n t i s a k e y d e t e r -m i n a n t o f C E M R A q u a l i t y [ 1 9 , 5 8 ] . V a r i o u s s t r a t e g i e s e x i s t f o r t i -m i n g t h e s t a r t

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CHAPTER 4. MAGNETIC RESONANCE IMAGING 22

Time

Imaging

Arterial

Enhancement

Acquisition Time Scan Delay Circulation  

Time Enhancement Time

Figure 4.5: Ideal contrast and acquisition timing. Data should be acquired in syn-chrony with the first pass of the contrast material through the arteries of interest. Circulation time, and therefore the scan delay, are partially tuneable through the injection rate, but are mostly determined by the subject’s cardiac output.

of the acquisition after beginning the contrast injection: guessing the circula-tion time; the test bolus method; or a bolus-deteccircula-tion method. The circulacircula-tion time could be guessed based on estimates of the cardiac output of the patient or from experience based on prior acquisitions. This is generally a poor strategy given the variance in cardiac output among subjects. Improving upon the guess-method, the test-bolus method involves the injection of small contrast dose and while measuring the the time until peak enhancement. However, a test bolus will decrease the peak contrast enhancement in the subsequent image because of the diluted contrast material in the blood and surrounding tissue. The bolus-detection method is a further improvement, and it involves using real-time or rapidly acquired images displayed to the MR technician so that they can fol-low the contrast bolus through the venous system, heart, and into the arterial system and trigger the acquisition at the appropriate time. Fluroscopy-style imagery is normally created using a 2D gradient echo sequence that rapidly fills the centre of k-space. An example fluoroscopy-style bolus-tracking image is shown in Figure 4.7a alongside the maximum intensity projection of the re-sulting image. This correctly timed image does not show venous involvement. Figure 4.7b shows an example of a poorly timed CEMRA. In this subject, the contrast enhancement is seen in the cerebral circulation at the point when the acquisition was triggered. As a result, the CEMRA shows significant venous involvement.

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CHAPTER 4. MAGNETIC RESONANCE IMAGING 23 Fast Injection Rate

Time

Arterial

Enhancement

Slow Injection Rate Injection

Figure 4.6: The injection rate controls not only the length of arterial enhancement, but also the peak arterial enhancement. Faster injection rates result in shorter circulation times, higher intra-arterial contrast concentrations, and shorter enhancement periods, all else being equal.

Generating high spatial resolution images of the vasculature makes CEMRA valuable for the identification of stenoses, dissections, or aneurysms. In addition, these bright-blood images are useful for vessel segmentation. These segmenta-tions can be used for geometric analyses or registered to other acquisisegmenta-tions for use in a variety of analyses. Their large FOV also makes CEMRA data a useful target for registering multiple images together.

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(a) Contrast bolus leaving the heart (left, red arrow), and the maximum intensity projection of the resulting correctly timed CEMRA which shows minimal venous involvement (right).

(b) Contrast bolus entering the cerebral circulation (left, red arrow), and the maximum inten-sity projection of the resulting CEMRA which shows significant venous involvement (right).

Figure 4.7: Bolus-track images and resulting CEMRA. Figure 4.7a depicts a well-timed CEMRA acquisition, where the bolus-track image depicts the contrast material leaving the heart. Figure 4.7b depicts a sub-optimal CEMRA, and the bolus-track image depicts the contrast entering the cerebral circulation. Red arrow indicates the location of the contrast bolus at the start of image acquisition.

4.5 Dixon

T h e D i x o n t e c h n i q u e i s a n M R I s e q u e n c e t h a t e x p l o i t s t h e f a c t t h a t w a t e r a n d f a t m o l e c u l e s p r e c e s s a t d i ﬀe r e n t r a t e s i n o r d e r t o g e n e r a t e f a t o n l y a n d w a t e r -o n l y i m a g e s [ 2 4 , 5 9 ] . T h e s e i m a g e s c a n b e u s e d f -o r a v a r i e t y -o f i n v e s t i g a t i -o n s , f o r e x a m p l e , d i s t i n g u i s h i n g b e t w e e n v a r i o u s t y p e s o f a b d o m i n a l l e s i o n s b y e x -a m i n i n g t h e f -a t c o n t e n t . T h e f -a t - o n l y i m -a g e s c o u l d -a l s o b e u s e d t o e x -a m i n e t h e a m o u n t o f a b d o m i n a l f a t a s u b j e c t h a s . T h i s s e c t i o n w i l l d e s c r i b e t h e D i x o n t e c h n i q u e , o u t l i n i n g h o w f a t - a n d w a t e r - o n l y i m a g e s a r e g e n e r a t e d , a n d d i s c u s s

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CHAPTER 4. MAGNETIC RESONANCE IMAGING 25 common challenges related to its use.

A fundamental assumption of most Dixon techniques is that water and fat are the only two chemical species in the object that generate signal [24]. Using this assumption, the Fourier transform of the acquired signal S can be expressed as:

S(x, y, z) = [W (x, y, z) + F (x, y, z)⇤ ei↵_]_{⇤ e}i (x,y,z)_{⇤ e}i 0(x,y,z)_, (4.14)

where (x, y, z) is the spatial coordinate of the voxel, W and F are real and non-negative numbers representing the magnitude of the magnetizations at a given location for water and fat. ↵ is the phase angle of fat relative to water given their chemical shift diﬀerence, is the phase error due to field homogeneity, and 0 is a second phase error term representing other system

imperfections. The accumulated signal can be visualized in a vector form (Figure 4.8). The primary objective of Dixon techniques is to determine W and F from the acquired image(s) that encode the chemical shift diﬀerence into the signal phase, that are represented by S in Equation 4.14.

0

S

W

F

Figure 4.8: Vector representation of the complex signal S for a given voxel that is generated by the two components fat (F) and water (W). ↵ is the phase angle of fat

relative to water, is the phase error due to magnetic field inhomogeneity, and ois

Improving Assessments of Hemodynamics and Vascular Disease

Improving

Assessments of

Hemodynamics and

Vascular Disease

Improving Assessments of

Hemodynamics and Vascular

Disease

Magnus Ziegler

Improving Assessments of Hemodynamics

and Vascular Disease

Abstract

Populärvetenskaplig

Sammanfattning

Acknowledgments

Funding

List of Papers

Nomenclature

Contents

Chapter 1

Introduction

Chapter 2

Aims

Chapter 3

Physiological Background

3.1 Anatomy

3.2 Vascular Disease

Aneurysms

A

B

D

C

Atherosclerosis

Chapter 4

Magnetic Resonance Imaging

4.1 Basic MRI Principles

4.2 MRI of the Cardiovascular System

4.2.1 Cardiac Gating

4.2.2 Respiratory Motion Suppression

4.3 Phase-Contrast MRI

4.3.1 PC-MRI Velocity Mapping

4.3.2 4D Flow MRI

v

!

!

v

!

v

FE

FE

FE

FE

4.3.3 Turbulence Mapping

4.4 Contrast-Enhanced MR Angiography

A

B

Time

Time

Imaging

Arterial

Enhancement

Time

Arterial

Enhancement

4.5 Dixon

S

W

F