The Development of Computational Fluid Dynamic Models for Studying the Detection and Diagnostic Techniques of Aortic Coarctation

The Development of Computational
Fluid Dynamic Models for Studying the
Detection and Diagnostic Techniques of
Aortic Coarctation
Cristina Staicu
Department of Cardiovascular Science
Faculty of Medicine, Dentistry and Health
The University of Sheffield
Thesis submitted to the University of Sheffield for the degree of
Doctor of Philosophy
June 2012

To my father, Ioan, and grandmother, Aurelia

1
Abstract
Aortic coarctation requires cardiac catheterization or surgery and late prognosis is
affected by associated intra-cardiac pathology, residual coarctation, arch hypoplasia, and
hypertension at rest or under exercise. Diagnosis is not straight-forward as many patients
are asymptomatic at rest and the aorta may have a region of stiffened wall rather than an
overall narrowing. The aim of this research is to develop computational fluid dynamic models
that predict non-invasively the pressure drop between the ascending and diaphragmatic
aortic levels, currently clinically obtained through catheter gradient methods.
This project studies anonymised clinical data for both patients with (native or recurrent)
aortic coarctation and control patients with healthy aortic geometries. A processing workflow
is designed, and implemented, in five stages (figure 0.1): image data loading, aortic
segmentation, 3D volume mesh generation, setting up process for simulation boundary
conditions, and computational fluid dynamic simulation.
The 3D surface of the aortic geometry is segmented from the medical image with a
newly developed algorithm based on image registration. Efforts are presented to generate a
segmentation model for aortic coarctation geometries. The openings for the patient-specific
physical model are chosen normal to the geometry’s centreline.
The volume enclosed in the extracted geometry is discretised in a 3D mesh. A
sensitivity analysis is run for generating suitable mesh models of mild, moderate and severe
coarctation studies, with converged results.
Figure 0.1 Overview of the integrated clinical processing workflow for aortic model development
Boundary
Conditions
Segmentation3D MRA Geometry Mesh
Simulation
Results

Overview
2
Boundary conditions are set based on clinical data. Two processing algorithms are
developed, one for flow data from time-resolved 2D phase contrast MRI, and one for
invasive pressure measurements. Efforts are made to model flow profiles at the openings of
the physical model, at the supra-aortic branches and collaterals.
The information provided by the models is compared with clinical data as this workflow
is desired to provide a physically accurate detail that completes the information included in
the medical images.
Thesis Outline
The project is conducted as a co-operation between the Department of Imaging
Sciences and Biomedical Engineering, King’s and St. Thomas’ School of Medicine, London,
UK and Department of Cardiovascular Science, University of Sheffield, Sheffield, UK.
Clinical data is acquired with ethically approved protocol - NHS R&D REC reference number:
08/H0804/134. It uses computational fluid dynamics (CFD) simulations for model developing
(figure 0.2) of the blood flow in patient-specific aortic geometries.
Figure 0.2 Steps in the processing workflow
The first three processing steps are developed in GIMIAS (Center for Computational
Image and Simulation Technologies in Biomedicine, Universitat Pompeu Fabra, Barcelona,
Spain) - a software framework designed as an integrative tool for fast prototyping of medical
applications, for advanced biomedical image computing and simulations, that can be
extended through the development of problem-specific plug-ins (Larrabide 2009). GIMIAS
(Graphical Interface for Medical Image Analysis and Simulation) is a workflow-oriented
environment developed in C++. The phase-contrast magnetic resonance volumetric flow
rate images are visualized and initially processed in Philips DICOM Viewer.

Overview
3
The segmentation algorithm developed for the second processing step is using ShIRT
(Sheffield Image Registration Toolkit), an in-house image processing software, developed in
C, with a Matlab interface (Barber 1999).
The volumetric mesh generated inside the segmented surface is run under the
commercial software ICEM (ANSYS ICEM CFD v12.0 2009).
The matrix-based algorithms required for the fourth processing step are developed in
Matlab, high-level language for the development of (MATLAB v7.5 2007).
The fifth, and last, processing step of the workflow, the simulation itself, is run under
the commercial software ANSYS (ANSYS CFX v12.0 2009). The simulation results are
compared with available clinical data.
The thesis is structured in six main chapters, as follows:
Chapter 1 provides background information and literature review about thoracic aorta
(healthy geometry anatomy, pathogenesis and diagnostic challenges of the disease labelled
as aortic coarctation), clinical data acquisition (magnetic resonance imaging for the
acquisition of volume image for aortic segmentation, phase contrast magnetic resonance for
the acquisition of volumetric flow rate data, hybrid XMR system for acquisition of invasive
pressure measurements), image processing (registration, centreline extraction, vascular
segmentation), computational fluid dynamic simulations (governing model equations, 3D
mesh, fluid physical properties and flow regimes), pressure wave analysis and Windkessel
modelling. Details are presented about anonymised patient data used in the study, and, in
this context, the three main research objectives are stated.
Chapter 2 starts with the mathematical theory used in ShIRT for image registration.
Image processing algorithms are understood at one-, two- and three-dimensional levels. The
in-house aortic segmentation algorithm is then presented, with implementation tests and
comparison with available methods (classic or connected threshold and Otsu segmentation).
The implementation of the GIMIAS plug-in required for this processing step is detailed and a
discussion is made about the results processed with the newly developed segmentation tool.
The section ends with efforts for generating a geometrical model for coarctation.

Overview
4
Chapter 3 presents a newly developed processing algorithm for time-resolved 2D
phase contrast MRI data. It is applied on the available clinical data, and a discussion is
made for flow profiles measured at sites along the aortic centreline. As the hemodynamic
events in thoracic aorta are indicated by elements in the flow data, the Reynolds number is
determined, the pressure difference across the stenosis is estimated and collateral
circulation is identified. The last element in this section is the model generating efforts for the
flow profiles at aortic branches.
Chapter 4 generates mesh models for mildly, moderately and severely coarcted
geometries. Three types of mesh types are investigated: isotropic tetrahedral, anisotropic
tetrahedral without and with prismatic boundary layers. A mesh sensitivity analysis is
presented for both laminar and turbulent flow. The conclusions are based on validation of the
simulation results with clinical data.
Chapter 5 presents a third newly developed processing algorithm, for invasive
pressure measurements. Idealised pressure waveforms are used for hemodynamic studying
of healthy aortic geometries and findings are presented for matching flow and pressure
boundary conditions for the geometry’s openings at both rest and exercise conditions. A line
analysis is performed for the invasively measured pressure waveforms and further pulse
pressure details are investigated with three methods: augmentation index, time and
frequency domain analysis. A method to estimate the parameters for a three-element
Windkessel pressure boundary condition is presented at the end of the section.
Chapter 6 provides a summary of the key research findings, a concluding discussion
in the context of the stated objectives, and gives suggestions for future research.
The thesis ends with appendices and the list of references.

5
Journal Publications
Barber, D.C., Staicu, C., Valverde, I., Beerbaum, P., and Hose, D.R. (2012)
‘Registration Based Segment Growing for Vascular Segmentation’ – manuscript submitted to
IEEE Transactions on Medical Imaging (IEEE T-MI).
Brown, A.G., Shi, Y., Marzo, A., Staicu, C., Valverde, I., Beerbaum, P., Lawford, P.V.,
and Hose, D.R. (2011) ‘Accuracy vs. Computational Time: Translating Aortic Simulations to
the Clinic’. J.Biomech. 45(3):516-523.
Smith, N., de Vecchi, A., McCormick, M., Camara, O., Frangi, A.F., Delingette, H.,
Sermesant, M., Ayache, N., Krueger, M.W., Schulze, W., Hose, R.D., Valverde, I.,
Beerbaum, P., Staicu, C., Siebes, M., Spaan, J., Hunter, P., Weese, J., Lehmann, H.,
Chapelle, D., and Rezavi, R. (2011). ‘euHeart: Personalised and Integrated Cardiac Care
using Patient-Specific Cardiovascular Modeling’. Interface Focus. 1(3): 349-364.
Singh, P.K., Marzo, A., Staicu, C., William, M.G., Wilkinson, I., Lawford, P.V.,
Rufenacht, D.A., Bijlenga, P., Frangi, A.F., Hose, R., Patel, U.J., and Coley, S.C. (2010).
‘The Effects of Aortic Coarctation on Cerebral Hemodynamics and its Importance in the
Etiopathogenesis of Intracranial Aneurysms’. J. Vasc. Interv. Neurol. 3(1): 17-30.
Journal Publications in Preparation
Barber, D.C., Staicu, C., Shi, Y., and Hose, D.R. ‘Measurement of Aortic Pressure
Wave Velocity by 4D Image Registration’
Peer Reviewed Full Length Conference Papers
Staicu, C., Valverde, I., Lycett, R., Shi, Y., Barber, D.C., Beerbaum, P., and Hose,
D.R. (2011) ‘Image Based Modelling of Blood Flow for Aortic Coarctation Studies’,
Physiological Fluid Mechanics: The Cardiovascular System, Brunel University, Uxbridge,
UK.

Publications
6
Valverde, I., Staicu, C., Marzo, A., Grotenhuis, H., Rhode, K., Tzifa, A., Razavi, R.,
Lawford, P.V., Hose, D.R., and Beerbaum, P. (2011) ‘Prediction of Pressure Gradient in
Aortic Coarctation by Computational Fluid-Dynamic Simulations’, Cardiology in the Young -
45th
Annual Meeting of the Association for the European Paediatric Cardiology and Cardiac
Surgery, S117-S118, Granada, Spain.
Valverde, I., Staicu, C., Grotenhuis, H., Marzo, A., Rhode, K., Shi, Y., Brown, A.G.,
Tzifa, A., Hussain, T., Greil, G., Lawford, P.V., Razavi, R., Hose, D.R., and Beerbaum, P.
(2011) ‘Predicting Hemodynamics in Native and Residual Coarctation: Preliminary Results of
a Rigid-Wall Computational-Fluid-Dynamics Model (RW-CFD) Validated Against Clinically
Invasive Pressure Measures at Rest and During Pharmacological Stress’, Society for
Cardiovascular Magnetic Resonance (SCMR)/ European Society of Cardiology (EuroCMR) –
Joint Scientific Sessions, Nice, France.
Staicu, C., Valverde, I., Beerbaum, P., and Hose, D.R. (2010) ‘The Designing of a 3D
Modeling Framework to Quantify Pathological Changes Associated with Aortic Coarctation’,
European Society of Biomechanics (ESB) 17th
Annual Congress, Edinburgh, UK.
Barber, D.C., Staicu, C., Shi, Y., Valverde, I., Beerbaum, P., Lawford, P.V., and Hose,
D.R. (2010) ‘Computation of Aortic Pulse Wave Velocity by Registration of Time Series
Images’, European Society of Biomechanics (ESB) 17th
Annual Congress, Edinburgh, UK.
Staicu, C., Barber, D.C., Lawford, P.V., and Hose, D.R. (2010) ‘Workflow for Detection
of Aortic Coarctation’, Medical School Research Meeting, The University of Sheffield,
Sheffield, UK.
Staicu, C., Barber, D.C., Lawford, P.V., and Hose, D.R. (2009) ‘Detection of Aortic
Coarctation’, Medical School Research Meeting, The University of Sheffield, Sheffield, UK.

Publications
7
Oral Presentations
Shi, Y., Brown, A.G., Staicu, C., Lawford, P.V., Hose, D.R. (2012) ‘One-Dimensional
Simulation of Hemodynamics in Aortic Coarctation’ – abstract accepted for oral presentation
for European Society of Biomechanics (ESB) 18th
Congress, Technical University of Lisbon,
Portugal.
Staicu, C. Lawford, P.V., Barber, D.C. and Hose, D.R. (2011) ‘Diagnostic Tool for
Aortic Coarctation’, First World Cardiovascular, Diabetes, and Obesity Online Conference -
TargetMeeting.
Hose, D.R., Barber, D.C., Staicu, C., and Lawford, P.V. (2011) ‘The Computation of
Pulse Wave Velocity in the Human Aorta by Registration of Time-Series Medical Images’,
Physiological Fluid Mechanics: The Cardiovascular System, Brunel University, Uxbridge,
UK.
Staicu, C. Lawford, P.V., Barber, D.C. and Hose, D.R. (2011) ‘Aortic Coarctation
Disease – Characterization Workflow’, The Faculty of Medicine, Dentistry and Health, The
University of Sheffield, Sheffield, UK.
Hose, D.R., Barber, D.C., Staicu, C., Shi, Y., Brown, A.G., and Lawford, P.V. (2009)
‘Fluid Solid Interaction for Vascular Applications’, Isaac Newton Institute for Mathematical
Sciences, Cambridge, UK.
Staicu, C., Lawford, P.V., Barber, D.C. and Hose, D.R. (2009) ‘Evaluation of Aortic
Coarctation with 4D Magnetic Resonance Imaging’, The Faculty of Medicine, Dentistry and
Health, The University of Sheffield, Sheffield, UK.
Staicu, C., Lawford, P.V., Barber, D.C. and Hose, D.R. (2008) ‘Noninvasive Modalities
of Detection for Aortic Coarctation’. The Faculty of Medicine, Dentistry and Health, The
University of Sheffield, Sheffield, UK.

8
Acknowledgements
This thesis represents not only my work at the keyboard; it is a peak milestone in the
decade of study at University, both in Romania and UK. I have been given unique
opportunities and I took advantage of them. This includes collaboration with KCL (King’s
College London) and Oxford in UK, UPF (Universidad Pompeu Fabra) in Spain, INRIA
(Institut National de Recherche en Informatique et Automatique) in France, DKFZ
(Deutsches KrebsForschungsZentrum) - Heidelberg and Philips Research - Aachen in
Germany, and last, but not least, HemoLab in the Netherlands.
Throughout these years I have learned that there are those who build tools and those
who use them; my passion is in creating models and algorithms to be used in cutting edge
research. This thesis presents the lessons learned in creating a workflow of setting a
diagnostic for aortic coarctation, a cardiovascular congenital disease. The work, and with it,
its author, has enjoyed a lot of encouragement and support from many sides.
First and foremost I would like to express my gratitude to my supervisors: Professor
Rodney Hose, Dr. Patricia Lawford and Professor David Barber for giving me the opportunity
to take part in a very modern and interesting research topic. They supported me not only by
providing a research assistantship over almost three and a half years, but also academically
and emotionally through the rough road to finish this thesis. A special thought for Dr. Marco
Stijnen, who joined my supervisors especially in the most difficult times. I would also like to
thank Dr. Andrew Narracott, Dr. John Fenner, Dr. Martin Bayley and Dr. Steven Wood for
the guidance during the research time.
I am grateful to Dr. Israel Valverde Perez, Professor Reza Razavi, Dr. Kawal Rhode,
and Dr. Philipp Beerbaum from King’s College London for the anonimized medical data and
provided support without which this research could not have been possible.
A special acknowledgement goes to MD Pankaj Singh and Dr Alberto Marzo for their
help, as I learned immensely from their experience in the @neurIST European project.

Acknowledgements
9
I would also want to thank Professor Iain Wilkinson and Dr. David Jones for
recommending me as a candidate for the Chartered Engineer position.
Thank you to those who helped the project as staff: Jodie Burnham, Carol Fidler, and
Victoria Palmer for submitting essays and the thesis; Fozia Yasmin for updating the status of
my RTP modules; Dr. Martina Daly for guiding my academic development and Stephen
Parkin for continuous technical support.
I would also like to thank Dr. David Evans, Dr. Scott Reeve, Dr. Desmond Ryan, Dr.
Norman Powell, Patricia Arcangeli, Marta Balzan and Simi Ninan for the pleasant work
environment and for their help in practical problems.
I was involved in activities outside the University of Sheffield, where I met amazing
people while showcasing Romanian culture in the Students’ Union. Denisa Darabanţ stands
out notoriously as an amazing source of support and friend, with whom I also underwent
volunteering work for British Heart Foundation, under the guidance of Lauren Mallinson, the
fundraising volunteer manager.
Last, but not least, I would like to thank Nick and Sue Currie for offering me a
temporary job in the writing-up period.
This research is supported by European Commission under the Grant No. FP7-ICT-
2007-224495, euHeart. This sponsorship is gratefully acknowledged.

10
Abbreviations
Blood Properties:
  - blood density
 P - blood pressure
 Q – volumetric flow rate
 Re – Reynolds number
  - blood viscosity
Units of Measurement:
 bpm – beats per minute
 cc – cubic centimeter
 cP - centipoises
 mmHg - millimetres of mercury
Anatomy:
 Asc – Ascending aorta
 AV – aortic valve
 BCA – BrachioCephalic Artery
 CoA – Aortic Coarctation
 Dia – aorta at diaphragmatic level
 Gd – Gadolinium contrast agent
 IVC – Inferior Vena Cava
 LCCA – Left Common Carotid
Artery
 LSA – Left Subclavian Artery
 RV – right (pulmonary) valve
 SVC – Superior Vena Cava
 Trans – transverse arch of the
aorta
Software:
 DICOM – Digital Imaging and Communications in Medicine
 GUI – Graphical User Interface
 GIMIAS – Graphical Interface for Medical Image Analysis and Simulation
 ShIRT – Sheffield Image Registration Toolkit
 v. – version
 VTK- Visualizing ToolKit

Abbreviations
11
Image Processing:
 1D/2D/3D – one / two / three dimensional
 CT – Computed Tomography
 DOF- Degrees Of Freedom
 ECG – ElectroCardioGram
 GAR – Geodesic Active Regions
 MRA- Magnetic Resonance Angiogram
 MRI – Magnetic Resonance Imaging
 PACS – Picture Archiving and Communication System
 PC-MR VFR – Phase Contrast – Magnetic Resonance Volumetric Flow Rate
 Pixel –picture element (two dimensional)
 RBRG – Registration Based Region Growing
 ROI – Region Of Interest
 SNR - Signal to Noise Ratio
 SSFP – Steady-State Free Precession Imaging
 VENC – Velocity ENCoding
 Voxel –volume element (three dimensional)
 XRA – X-Ray Angiography
 XMR - Hybrid Imaging System of X-ray and Magnetic Resonance
General:
 Eq – equation
 NHS – National Health Service
 R&D – Research and Development
Simulation:
 AC – Alternative Current
 CFD – Computational Fluid Dynamics
 DES – Detached Eddy Simulation
 DNS – Direct Numerical Simulation
 Exp – exponential growth law for prism boundary layers
 LES – Large Eddy Simulation
 Lin – linear growth law for prism boundary layers
 RANS – Reynolds Averaged Navier-Stokes

12
Table of Contents
Chapter 1: Introduction 27
1.1 Thoracic aorta 28
Anatomy of Healthy Thoracic Aorta 28
Development of Anatomy for Aortic Coarctation 29
1.2 Current Diagnostic Challenges for Aortic Coarctation 31
1.3 Study Design for Data Acquisition 33
Diagnostic Imaging Modalities 34
MRI Contrast Medium 36
Control of Heart Rate 38
Protocol for Collecting Clinical Data 40
Study Cohort 42
1.4 Independent Clinical Risk Factors 43
1.5 Clinical Data Acquisition Techniques 45
Magnetic Resonance Imaging 45
Phase Contrast Magnetic Resonance Volumetric Flow Rate 47
Hybrid Imaging System of X-ray and Magnetic Resonance 50
1.6 Image Processing 51
DICOM Format 52
Image Registration 53
Centreline Extraction 56
Vascular Segmentation 57
1.7 Computational Fluid Dynamics 60
Governing Model Equations 61
3D Mesh for Fluid Flow Simulation 62
Physical Properties of the Fluid 64
Flow Regimes 65

Contents
13
1.8 Pressure Wave Analysis 68
1.9 Windkessel Modelling 70
1.10 Thesis Research Aims 72
Chapter 2: Aortic Segmentation 73
2.1 The Sheffield Image Registration Toolkit 74
2.2 One-Dimensional Image Processing 77
2.3 Two-Dimensional Image Processing 81
2.4 Three-Dimensional Image Processing 83
Classic Threshold Segmentation 83
Connected Threshold Segmentation 84
Otsu Segmentation 95
2.5 Region Based Region Growing Segmentation Model 97
Seed Definition 98
Input Image 99
2.6 Implementation of the RBRG Segmentation Workflow 100
Workflow Step 1: Pre-Processing and Segmentation 100
Workflow Step 2: Post-Processing of the Segmented Surface 103
Workflow Step 3: Centreline Extraction 104
Workflow Step 4: Position Choice for Surface Openings 105
2.7 Segmented Aortic Surfaces - Discussion 107
2.8 Geometrical Model for Coarctation Studies 110
2.9 Summary 112
Chapter 3: Processing of Time-Resolved 2D Phase Contrast MRI Data 113
3.1 Hemodynamics: Factors Affecting Blood Flow 114

Contents
14
3.2 Processing Method 115
Visualisation of Velocity Maps 115
Requirements from the Processed Product 115
Volumetric Flow Extraction Algorithm 116
Output Data 118
3.3 Measured Volumetric Flow Profile 119
3.4 Functional Assessment of Hemodynamic Events 122
Determining the Reynolds Number 122
Estimating the Pressure Difference across the Stenosis 124
Identifying Collateral Circulation 126
3.5 Flow Waveforms for Aortic Branches 128
3.6 Summary 130
Chapter 4: Mesh Generation for the Thoracic Aorta 131
4.1 GIMIAS Tool for Mesh Generation 132
4.2 Three Dimensional Mesh Types 133
Mesh Type A: Isotropic Tetrahedral Mesh 133
Mesh Type Β: Αnisotropic Tetrahedral Mesh 134
Mesh Type C: Anisotropic Tetrahedral Mesh with Prism Boundaries 134
Methodology for Mesh Parameter Settings 136
4.3 Configurations for the Fluid Domain 137
Mildly Coarcted Aortic Study 138
Moderately Coarcted Aortic Study 140
Severely Coarcted Aortic Study 142
4.4 Mildly Coarcted Aortic Model 144

Contents
15
4.5 Moderately Coarcted Aortic Model 150
4.6 Severely Coarcted Aortic Model 153
4.7 Summary 156
Chapter 5: Clinical Pressure Data Processing 157
5.1 The Cardiac Cycle 158
5.2 Processing Algorithm 160
Input Data 160
Product Requirements 161
Pressure Waveform Extraction Algorithm 161
Output Data 163
5.3 Idealised Aortic Blood Pressure Waveform 166
Healthy Child Aortic Geometry 167
Healthy Adult Aortic Geometry 170
5.4 Invasive Aortic Blood Pressure Waveform 172
Line Analysis 174
Pressure Trace at Different Measurement Sites 176
Augmentation Index 185
Time Domain Analysis 190
Frequency Domain Analysis 198

Contents
16
5.5 Three Element Windkessel Model 198
Parameter Estimation 200
5.6 Summary 200
Chapter 6: Discussion and Final Remarks 201
6.1 Summary of Findings 201
Aortic Segmentation 201
Mesh Generation 203
Volumetric Flow Processing 204
Pressure Signal Processing 205
6.2 Discussion 207
Pre-processing Filter for Aortic Segmentation 207
Elasticity of the Aortic Wall 211
CFD Simulations – Methodology and Boundary Conditions 212
Invasive Pressure Measurements 213
6.3 Future Work 215
Aortic Segmentation 215
Mesh Generation 215
Volumetric Flow Processing 215
Pressure Signal Processing 216
Computing Pressure Wave Velocity from Clinical Images 217
Experimental Validation of Pressure Wave Propagation 218
Computing Pressure Wave Velocity from Invasive Measurements 220
Appendices 221
Appendix 1: Aortic Coarctation – World Situation 221
Appendix 2: Gadolinium-based MRI Contrast Agent 223
The Electron Configuration for the Gadolinium Atom 223

Contents
17
The Gadopentetic Acid 224
Appendix 3: Hormone-Receptor Interactions 226
The Catecholamine 226
Isoprenaline and Dobutamine 229
Appendix 4: Representative Values for Human Circulation 230
Appendix 5: Segmented Aortic Geometries 231
Appendix 6: Volumetric Flow Data 238
Appendix 7: Invasive Pressure Data 242
References 245

18
List of Figures
Figure 0.1. Overview of the integrated clinical processing workflow for aortic model
development 1
Figure 0.2 Steps in the processing workflow 2
Figure 1.1 Aorta and its principal branches 28
Figure 1.2 Aortic geometry. A: Healthy. B: Coarcted 29
Figure 1.3 Development stages for aortic coarctation. A: healthy fetal circulation.
B: fetal circulation with CoA. C: neonate circulation with CoA.
D: infantile circulation with CoA 30
Figure 1.4 The autonomic nervous system 39
Figure 1.5 Variation of heart rate with age for male studies with healthy aortic
geometry 43
Figure 1.6 Variation of heart rate with age for male studies with CoA 44
Figure 1.7 MR image acquisition –components and setup 46
Figure 1.8 Sections of the PC-MR VFR folder. A: magnitude image. B: in-plane
phase-contrast image. C: through-plane phase-contrast image 48
Figure 1.9 Comparison of the MRI data. A: PC-MR VFR. B: 3D morphology MRI 49
Figure 1.10 Example of processing window for Philips DICOM Viewer 49
Figure 1.11 The XMR guidance system – components and setup 50
Figure 1.12 DICOM data element structure 52
Figure 1.13 Classification of image registration methods 53
Figure 1.14 Three dimensional mesh element types. A: tetrahedron. B: pyramid.
C: prism with triangular base or wedge. D: prism with quadrilateral base
or hexahedron. E: arbitrary polyhedron 62
Figure 1.15 One of the openings of the aortic geometry meshed with hexahedral
elements 62
Figure 1.16 Prediction of turbulence model versus eddy scale 66

List of Figures
19
Figure 1.17 Pulse wave propagation at a step junction 68
Figure 1.18 The components of the pressure waveform 68
Figure 1.19 Tapering of healthy aortic geometry 69
Figure 1.20 Variation of pressure wave velocity with lumen diameter 69
Figure 1.21 Three element Windkessel model 71
Figure 1.22 Detailed overview for the processing workflow 72
Figure 2.1 A: Example of binary image. B: Representative Gaussian function 77
Figure 2.2 Rigid registration of Gaussian functions with the same maximum 78
Figure 2.3 Rigid registration of Gaussian functions with different maximum 78
Figure 2.4 Functions with Fourier series 79
Figure 2.5 The effect of the shape function used in image registration 80
Figure 2.6 Image registration. A: 3D. B: 4D. C: Affine registration product. 81
Figure 2.7 Image registration. A: 3D. B: 4D 82
Figure 2.8 Thresholding the affine registration product 82
Figure 2.9 Image thresholding. A: 3D. B: 4D. C: Affine registration product. 82
Figure 2.10 Thresholding segmentation for 3D MRI with Gadolinium 83
Figure 2.11 Thresholding segmentation for 4D SSFP 84
Figure 2.12 Morphological image filters. A: raw image. B: ‘fill hole’. C: ‘grind peak’. 84
Figure 2.13 Aortic segmentation with ‘fill hole’ pre-processing filter. 86
Figure 2.14 Aortic segmentation. A: raw input. B: input with ‘fill hole’ pre-
processing filter. 86
Figure 2.15 Aortic segmentation with ‘grind peak’ pre-processing filter. 87
Figure 2.16 Aortic segmentation. A: raw input. B: input with ‘grind peak’ pre-
Figure 2.17 De-noising image filters. A: raw image. B: Gaussian. C: curvature
anisotropic diffusion. D: gradient anisotropic diffusion. E:.median filter 88
Figure 2.18 Aortic segmentation. A: raw input. B: input with Gaussian pre-

List of Figures
20
Figure 2.19 Aortic segmentation. A: raw input. B: input with ‘curvature anisotropic’
pre-processing filter. 90
Figure 2.20 Aortic segmentation. A: raw input. B: input with ‘gradient anisotropic’
pre-processing filter. 91
Figure 2.21 Aortic segmentation. A: raw input. B: input with ‘median’ pre-processing
filter. 92
Figure 2.22 Aortic segmentation with ‘fill hole’ filter. A: Gaussian. B: curvature
anisotropic diffusion. C: gradient anisotropic diffusion. D: median filter 93
Figure 2.23 Aortic segmentation with ‘grind peak’ filter. A: Gaussian. B: curvature
anisotropic diffusion. C: gradient anisotropic diffusion. D: median filter 93
Figure 2.24 Aortic segmentation with ‘fill hole’ and ‘grind peak’ morpho-filters.
A: Gaussian. B: curvature anisotropic diffusion. C: gradient anisotropic
diffusion. D: median filter 94
Figure 2.25 Aortic segmentation. A: raw input. B: input with pre-processing filter. 94
Figure 2.26 Otsu segmentation from 3D Gd-MRI. A: raw. B: aorta 96
Figure 2.27 Otsu segmentation from 4D SSFP. A: raw. B: aorta 97
Figure 2.28 Image segmentation. A: input image. B: segmented surface 97
Figure 2.29 Parameter choices for seed ROI. A: radius range. B: working time 98
Figure 2.30 Input image choice. A: 3D-Morphology. B: Gd-MRA 99
Figure 2.31 DICOM Plug-in: image data loading 100
Figure 2.32 ShIRT Plug-in: image cropping 101
Figure 2.33 ShIRT Plug-in: section 1 of command panel 101
Figure 2.34 ShIRT Plug-in: ROI creation 102
Figure 2.35 ShIRT Plug-in: segmentation result loading 103
Figure 2.36 ShIRT Plug-in: centerline extraction 104
Figure 2.37 ShIRT Plug-in: section 2 and 3 of command panel 105
Figure 2.38 Position choices for geometry’s openings 105
Figure 2.39 ShIRT Plug-in: final simulation surface extraction 106

List of Figures
21
Figure 2.40 Identification of the Coarctation Index 107
Figure 2.41 CoA study 2 108
Figure 2.42 Study 202 108
Figure 2.46 Representatives of mild (A), moderate (B) and severe (C) CoA studies 110
Figure 2.47 Model for mild-moderate (A), moderate-severe (B) and mild-severe (C)
analysis 111
Figure 3.1 Blood distribution at rest 114
Figure 3.2 Application of linear filter in the spatial domain 117
Figure 3.3 Example of flow profile after processing 118
Figure 3.4 Line analysis of the flow profile 119
Figure 3.5 CoA study 1. Flow data at ascending, transverse arch and CoA site,
at rest 120
Figure 3.6 CoA study 1. Flow data at ascending, under rest and exercise 120
Figure 3.7 Reynolds number for coarctation studies at the resting condition 123
Figure 3.8 Reynolds number for coarctation studies at the exercise condition 124
Figure 3.9 Pressure difference for coarctation studies at the resting condition 125
Figure 3.10 Pressure difference for coarctation studies at the exercise condition 125
Figure 3.11 Coarcted aortic geometries. A: study 3. B: study 4. C: study 5 126
Figure 3.12 Cardiac output for selected coarctation studies at the resting condition 127
Figure 3.13 Cardiac output for selected coarctation studies at the exercise condition 127
Figure 4.1 MeshICEM Plug-in: widget for mesh settings in command panel 132
Figure 4.2 Isotropic tetrahedral mesh 133
Figure 4.3 Anisotropic tetrahedral mesh 134
Figure 4.4 Linear (A) and exponential (B) growth law for prism boundary layers 134
Figure 4.5 Cross-sectional slices through thoracic aorta in MR images 135

List of Figures
22
Figure 4.6 CoA study 10 geometry 138
Figure 4.9 Results for laminar flow, at rest, mildly coarcted mesh type A 144
Figure 4.10 Results for laminar flow, at exercise, mildly coarcted mesh type A 144
Figure 4.11 Results for laminar vs. turbulent flow, rest, mildly coarcted mesh type A 145
Figure 4.12 Results for laminar vs.turbulent flow,exercise,mildly coarcted mesh A 145
Figure 4.13 Results for laminar flow, at rest, mildly coarcted mesh type B 146
Figure 4.14 Results for laminar flow, at exercise, mildly coarcted mesh type B 146
Figure 4.15 Results for laminar vs. turbulent flow, rest, mildly coarcted mesh B 147
Figure 4.16 Results for laminar vs.turbulent flow,exercise,mildly coarcted mesh B 147
Figure 4.17 Results for laminar flow, at rest, mildly coarcted mesh type C 148
Figure 4.18 Results for laminar flow, under exercise, mildly coarcted mesh type C 148
Figure 4.19 Results for laminar vs.turbulent flow, at rest,mildly coarcted mesh C 149
Figure 4.20 Results for laminar vs.turbulent flow, exercise,mildly coarcted mesh C 149
Figure 4.21 Results for laminar vs.turbulent flow, rest,moderate coarcted mesh A 150
Figure 4.22 Results for laminar vs.turbulent flow,exercise,moderate coarcted mesh A150
Figure 4.23 Results for laminar vs. turbulent flow, rest, moderate coarcted mesh B 151
Figure 4.24 Results for laminar vs.turbulent flow,exercise,moderate coarcted mesh B151
Figure 4.25 Results for laminar vs. turbulent flow, rest, moderate coarcted mesh C 152
Figure 4.26 Results for laminar vs.turbulent flow,exercise,moderate coarcted mesh C152
Figure 4.27 Results for laminar vs. turbulent flow, at rest, severe coarcted mesh A 153
Figure 4.28 Results for laminar vs. turbulent flow, exercise, severe coarcted mesh A 153
Figure 4.29 Results for laminar vs. turbulent flow, at rest, severe coarcted mesh B 154
Figure 4.30 Results for laminar vs. turbulent flow,exercise,severe coarcted mesh B 154
Figure 4.31 Results for laminar vs. turbulent flow, rest, severe coarcted mesh C 155
Figure 4.32 Results for laminar vs. turbulent flow,exercise,severe coarcted mesh C 155
Figure 5.1 Wiggers diagram 158

List of Figures
23
Figure 5.2 Invasive pressure measurements. Good (A) and bad (B) case scenarios 160
Figure 5.3 Phase 1 for pressure data processing. Good (A) and bad (B) case
scenarios 162
Figure 5.4 Phase 2 for pressure data processing. Good (A) and bad (B) case
scenarios 162
Figure 5.5 Ascending aortic pressure – rest versus exercise – evolution with age 164
Figure 5.6 Diaphragmatic aortic pressure–rest versus exercise–evolution with age 164
Figure 5.7 Minimum aortic pressure – rest versus exercise – evolution with age 165
Figure 5.8 Pressure waveform from 1D model 166
Figure 5.9 Healthy study 101 geometry 167
Figure 5.10 Pressure waveforms – ascending (CFD simulated),
diaphragm (scaled, 1D) 168
Figure 5.11 Flow profiles at ascending site 169
Figure 5.12 Pressure waveforms – ascending (CFD simulated),
diaphragm (scaled, 1D) 169
Figure 5.13 Healthy study 208 geometry 170
Figure 5.14 Variation of pressure drop with flow at supra-aortic branches 171
Figure 5.15 Typical central aortic pressure waveform 174
Figure 5.16 Pressure data along the aortic centerline. α: ascending aorta.
β: isthmus site. γ: diaphragmatic level 176
Figure 5.17 Aortic compliance – rest versus exercise 187
Figure 5.18 Augmentation index – variation with height (A) and age (B) 187
Figure 5.19 Left ventricle workload – variation with age 189
Figure 5.20 Wave analysis –pressure data, ascending – CoA study 10, rest 191
Figure 5.21 Wave analysis –pressure data, diaphragm – CoA study 10, rest 191
Figure 5.22 Wave analysis –pressure data, ascending – CoA study 10, exercise 192
Figure 5.23 Wave analysis –pressure data, diaphragm – CoA study 10, exercise 192

List of Figures
24
Figure 5.32 Windkessel circuit with three elements 198
Figure 6.1 Aortic segmentation for CoA study 13. A: ShIRT. B: Otsu’s method 207
Figure 6.2 ShIRT vs. Otsu. A: CoA 2 B: CoA 3 C: CoA 5 D: CoA 8 E: CoA 9
F: CoA 10 G: CoA 14 H: CoA 16 208
Figure 6.3 Otsu segmentation on the difference of morphological filters – CoA 10 209
Figure 6.4 Aortic segmentation for CoA study 10 on: A: raw image. B: histogram
equalised image 209
Figure 6.5 Aortic segmentation for CoA study 10 on: A: raw image. B: image
normalised by the maximum pixel value 209
Figure 6.6 Zero crossing based edge detection filter for Otsu’s segmentation
– CoA study 10 210
Figure 6.7 Elements influencing the aortic biological system 211
Figure 6.8 Mock-up screenshot for visualizing the simulation results 213
Figure 6.9 Physical and analogous electric system for the catheter-sensor system 214
Figure 6.10 Experimental set-ups 219
Figure 6.11 ‘Foot-to-foot’ method of computing pressure wave velocity 220

25
List of Tables
Table 1.1 Patient demographics for aortic coarctation studies 42
Table 1.2 Patient demographics and healthy aortic geometry features 42
Table 1.3 Review of methods for centreline extraction 57
Table 1.4 Review of extraction techniques for blood vessels 58
Table 2.1 Interpolation shape functions 79
Table 2.2 The effect of the shape function used in image registration 80
Table 2.3 Example of common requirements for surface post-processing 103
Table 2.4 Coarctation Index 107
Table 3.1 Aortic coarctation studies-gender: male, coarctation: mild, moderate and
severe 121
Table 3.2 Flow boundary conditions for steady state simulations 129
Table 4.1 Clinical pressure data for model validation in the case of CoA studies 137
Table 4.2 Boundary details for CoA study 10 under resting condition 138
Table 4.3 Boundary details for CoA study 10 under exercise condition 138
Table 4.4 Mesh parameters for the mildly coarcted model 139
Table 4.7 Mesh parameters for the moderately coarcted model 141
Table 4.10 Mesh parameters for the severely coarcted model 143
Table 5.1 Pressure values for studies with aortic coarctation 163
Table 5.2 Idealised boundary details for healthy study 101 – rest condition 167
Table 5.3 Clinical boundary details for healthy study 101 – rest condition 167
Table 5.4 Boundary details for healthy study 208 under resting condition 170
Table 5.5 Boundary details for healthy study 208 under exercise condition 170
Table 5.6 Aortic coarctation studies – gender: male, coarctation: mild 177

List of Tables
26
Table 5.7 Aortic coarctation studies – gender: male vs. female, coarctation: mild 178
Table 5.8 Aortic coarctation studies – gender: male, coarctation: mild vs. moderate 181
Table 5.9 Aortic coarctation studies – gender: male, coarctation: moderate vs.severe 183
Table 5.10 Pressure data for pulse analysis – rest condition 186
Table 5.11 Pressure data for pulse analysis – exercise condition 186
Table 5.12 Time data for pulse analysis – rest condition 188
Table 5.13 Time data for pulse analysis – exercise condition 188
Table 5.14 Augmentation Index Method 197
Table 5.15 Wave Intensity Method 197
Table 6.1 Processing 4D-SSFP data for the available 25 time points at ascending 217

27
Chapter 1: Introduction
Congestive heart failure is the state in which abnormal circulation exists due to the
inability of the heart to supply the amount of blood required by the system. It is caused by
abnormal activation or structural abnormalities, like coarctation of the aorta (CoA) which
accounts for %5 of cases with congenital heart disease. The hemodynamic changes of the
systemic circulation in the presence of CoA remain unclear, but the physiological behaviour
of the system can be quantitatively described using mathematical and computational
models.
This first chapter provides background information and literature review for the
research developed in this project. It starts by presenting the anatomy of the geometrical
region of interest, the thoracic aorta, in the healthy state. It is followed by the presentation of
the stages of anatomical development for CoA and this first chapter section ends with a
review of current diagnostic challenges.
Clinical data is required to test the scientific hypothesis of the project. In the following
chapter section are presented the elements that lead to the formulation of acquisition
methodologies, like imaging modality, contrast medium and heart controlling agents used. In
the next chapter section the standard of practice is outlined for acquiring the clinical data for
this project. Discussion about independent for clinical risk factors ends the section.
The principles for clinical data acquisition techniques are then summarised: magnetic
resonance imaging (MRI) for the aortic geometry, phase contrast magnetic resonance for the
volumetric flow rate, and the hybrid XMR system for the invasive pressure measurements.
In the second part of the chapter is presented background information about the
processing methods used in this project: image processing (registration, centreline
extraction, and vascular segmentation), computational fluid dynamic simulations (governing
model equations, 3D mesh, flow regimes), pressure wave analysis and development of
Windkessel model. The research aims of this thesis are stated in the final part of the chapter.

28
1.1 Thoracic aorta
Anatomy of Healthy Thoracic Aorta
The aorta, the largest blood vessel in the human circulation, has a complex, three-
dimensional curved geometry that arises from the upper part of the left ventricle and, after a
few centimetres, arches and descends through the thorax and the abdominal cavity (figure
1.1). It is divided by the diaphragm into two segments: the thoracic and the descending
abdominal aorta. The thoracic aorta includes three sections: the ascending aorta, the aortic
arch and the descending aorta. During the cardiac cycle, due to left ventricle contraction,
blood is pumped through the aortic valve in ascending aorta. From the aortic arch, through
the upper branches (brachiocephalic trunk, left common carotid and left subclavian arteries),
blood is transmitted in the upper side of the body to the visceral organs and to the peripheral
regions in the systemic circulation. Similarly, through all the other branches, the oxygenated
blood reaches the lower side of the body.
Figure 1.1 Aorta and its principal branches

29
Development of Anatomy for Aortic Coarctation
Aortic coarctation is a defect that accounts for %10 of congenital cardiovascular
disorders (Appendix 1). The male-to-female incidence ratio is 2:1 (Verheugt 2008). It is
commonly seen at children in association with bicuspid aortic valve (Edwards 1978), mitral
valve (Patel 2008), atrial or ventricular septal defect (Levy 1983), and Turner syndrome (Carlson
2007), while at adults with aortic stenosis and patent ductus arteriosus (Harling 2009).
A B
Figure 1.2 Aortic geometry. A: Healthy. B: Coarcted
Aortic coarctation represents a disorder of the tunica media, in most cases located
adjacent to ductus arteriosus and distal to left subclavian artery (Ad 1999). The narrowing of
the aortic wall (figure 1.2.B) can be focal - aortic coarctation, diffuse - hypoplastic aortic
isthmus, or complete -aortic arch interruption (Russo 2006).
The location of the narrowing originates from the embryonic state of the aorta. In figure
1.3 below (Rosenthal 2005) is sketched the difference in circulation between the cases of
healthy and coarcted aortic geometries. The oxygenated blood is represented with red line,
while the de-oxygenated one with blue.
In normal fetal circulation (figure 1.3.A), the aorta is divided by the isthmus (above the
arterial duct) into two sections: one with oxygenated blood towards the upper side of the
body, and one with de-oxygenated blood towards the lower side of the body. When
coarctation develops in the fetal stage (figure 1.3.B) the blood flow pattern is not affected.

30
In the neonate stage (figure 1.3.C), due to birth, oxygenated blood starts flowing in
descending aorta towards the lower side of the body. The resistance of the pulmonary
vessels decreases with the increase of blood flow, less blood is transported through the
arterial duct. In the infantile stage (figure 1.3.D), as the arterial duct is less and less used,
reduces in diameter, the aorta transports only oxygenated blood.
While the body is growing up, the focal narrowing of the aorta restricts the left
ventricular outflow to the body, fact that is reflected in major changes to the aortic flow
waveform and regime (Berger 2000; Warnes 2008). This leads to compromised coronary flow
(O'Rourke 2008) or pulmonary and systemic hypertension (Oechslin 2008). The presence of
high regurgitant flow increases the chances for left ventricular hypertrophy (Ziegler 1954),
which leads to cardiac failure. Under stress condition, the collagen fibres from the aortic wall
are remodelled (Hariton 2007), which in time leads to tears of the aortic inner wall, element
diagnosed under the name of aortic dissection (Rajagopal 2007, Patel 2008).
Figure 1.3 Development stages for aortic coarctation. A: healthy fetal circulation. B: fetal
circulation with CoA. C: neonate circulation with CoA. D: infantile circulation with CoA

31
In vitro and in vivo observations demonstrated that flow‟s conditions affect the
behaviour of vessel wall‟s cells at bifurcations, less in large (Jafari 2008) but more effective in
small vessels, like the capillaries (Jafari 2009). The coarctation divides the thoracic aorta into
two segments: one hypertensive and one hypotensive (Gupta 1951, Hollander 1968). In the
hypertensive segment the total (the addition between the static and dynamic) pressure is
increased, and the resistance to deformation of blood‟s layers, and its measure, „viscosity‟, is
also increased, (Gariepy 1993). Resistance to increased pressure is expressed also by the
smooth muscle cells in the vessel wall (Dabagh 2007) to possibly create an increased wall
thickness (O'Rourke 2008, Singh 2010). In the hypotensive segment the situation should be
reversed, but local hemodynamic features cannot be measured clinically (Fowkes 1993).
1.2 Current Diagnostic Challenges for Aortic Coarctation
Early detection for CoA is a requirement to increase the percentage of patients who
can benefit from treatment, as late detection may be fatal and it is commonly associated with
other diseases (Maron 1973, Cohen 1989). The clinical diagnostic procedure includes: blood
tests, measurement of aortic dimensions, ECG study of the heart condition and cardiac
catheterization (Warnes 2008).
Prenatal diagnosis with echocardiograms is based on the study of ventricular
disproportion, transverse aortic arch, isthmic hypoplasia and the ratio of aortic to pulmonary
artery size. False positives and negatives are recorded. If ductus arteriosus is present,
setting the diagnose with %100 certainty is not possible (Franklin 2002).
For the neonatal and infantile stage the evaluation is different for non-critical and
critical cases. For the critical cases the arterial access is established, mechanical ventilation
is instituted (with no vasodilating agents), the metabolic acidosis is corrected (to improve the
myocardial dysfunction) and end-organ ischemia is assessed (renal and central nervous
system).

32
For non-critical and asymptomatic cases: a physical evaluation is performed to check
for blood pressure difference between the upper and lower part of the body and for the
presence of systolic murmur (Bing 1948); electrocardiography and echocardiography are
performed to check for ventricular hypertrophy; magnetic resonance imaging (MRI) or
computed tomography (CT) scan (check: shape of the aorta and cardiomegaly) (Ziegler
1954).
Doppler echocardiographic techniques are inadequate for a full assessment (like
measuring the pressure wave velocity) at the site of coarctation in adults (Tan 2005).
The transcatheter peak-to-peak pressure gradient represents the standard measure to
assess the severity of the aortic coarctation (Warnes 2008). Cardiac catheterisation for
hemodynamic measurements is recommended for assessment of stenosis severity in
symptomatic patients when non-invasive tests are inconclusive or if there is a discrepancy
between non-invasive tests and clinical findings. In the adult stage for aortic coarctation the
patients are asymptomatic at rest but symptomatic under exercise condition and the
diagnosis is set as in the non-critical cases of children.
If in the patient‟s clinical history there was no previous detection of aortic coarctation,
surgery is recommended (Bing 1948, Wells 1996) if the peak-to-peak pressure gradient is
greater than or equal to mmHg20 (in the case of isolated coarctation) or less than mmHg20 (in
case of collaterals). In the case of isolated coarctation with peak-to-peak pressure gradient
of at least mmHg20 then percutaneous catheter intervention (stenting) is indicated to be
performed.
In the case of recoarctation (unsuccessful first surgery) concomitant hypoplasia of the
aortic arch is recommended for small recoarctation segments or surgery for long ones.
Percutaneous catheter intervention may be considered for long recoarctation segments but
the long-term efficacy and safety are unknown (Rosenthal 2005). In the case of children is
suggested to have surgery than balloon dilation angioplasty (Lock 1983, Rossi 1998).

33
1.3 Study Design for Data Acquisition
Diagnosis for aortic coarctation is not straight-forward as many patients are
asymptomatic at rest and the aorta may have a region of stiffened wall rather than an overt
narrowing. For this reason investigation requires imaging and invasive pressure
measurements and may also require the patient to be stressed pharmacologically during the
procedure.
It is our hypothesis that a combination of medical imaging and modelling provides an
alternative non-invasive method for diagnosis and treatment planning in the case of aortic
coarctation. Validated computational models for healthy and diseased thoracic aorta give
valuable insight that allows improving the diagnosis, treatment planning and interventions,
and thus reducing the allied healthcare costs.
The project is conducted as a co-operation between the Department of Imaging
Sciences and Biomedical Engineering, King‟s and St. Thomas‟ School of Medicine, London,
UK and Department of Cardiovascular Science, University of Sheffield, Sheffield, UK. The
collection protocol for the study cohort has ethical approval (NHS R&D REC reference
number: 08/H0804/134). Clinical data is available for patients with native or recurrent aortic
coarctation and for control patients with healthy aortic geometries. The clinical relevance of
this study is the prediction of the hemodynamic response (pressure drop) under exercise
condition in the individual patient with aortic coarctation.
The clinical data elements required to test the scientific hypothesis are: aortic
geometry, flow and pressure waveforms. The choices available for data acquisition
instruments and tool selection to support and output computer data model are outlined in this
section. There are two diagnostic imaging techniques considered for acquiring the aortic
geometry: computed tomography (CT) and magnetic resonance imaging (MRI). The heart
rate is pharmacologically increased with two agents: isoprenaline or dobutamine. The study
cohort is chosen based on the eligibility criteria set in the approved ethically approved
clinical data acquisition protocol.

34
Diagnostic Imaging Modalities
The aortic flow and geometry are extracted in clinical practice from chest radiography.
The thorax has a relatively poor signal to noise ratio due to low proton density in the lung
fields. The tissue information is mapped in both spatial and temporal direction using
computed tomography (CT), contrast angiography, or echocardiography. Magnetic
resonance imaging (MRI) is used primarily as problem-solving tool: it provides, as with X-ray
CT, high resolution anatomic structure (Goel 2008) but, in addition to the information that the
CT scan offers, it provides high contrast between different soft tissues (Meaney 1999). The
physical basis of soft tissue contrast and the enhancement mechanism with exogenous
contrast materials are different for the two imaging modalities. Both CT and MRI have the
ability to change the imaging plane without moving the patient, but it takes longer to acquire
an MRI scan than with CT and the MRI screening is more susceptible to patient motion.
In a CT radiographic examination, the X-ray beam is projected through the thorax. The
entrance surface point near the centre of the primary beam receives the maximum radiation
exposure and the thorax volume is captured on image due to attenuated backscattered
radiation of electrons, ions and photons. The rotation of the primary X-ray beam around the
thorax volume produces uniform distribution of radiation exposure. The ionising radiation is
passed through the body and received by a detector and then integrated by the computer to
obtain a cross sectional image that is displayed on the screen. CT screening can pose the
risk of irradiation on the patient‟s body as increased image quality requires increased patient
exposure.
In contrast, no biological hazards have been reported with the use of the MRI
screening (Shields 2009). The acquisition of the MR image involves interaction between the
hydrogen nuclei (or protons) in the body‟s water molecules, an external magnetic field and
applied radiofrequency waves. The protons are used to create images because of their
abundance in water molecules ( %80 of most soft tissues). The MRI signal intensity is
proportional to the tissue density of excess spins (magnetic moments).

35
The relaxation behaviour of the protons varies in different tissues. The tissue
relaxation is described by two parameters: 1T (the characteristic time for spins to re-establish
the longitudinal thermal equilibrium distribution) and 2T (the measurement of energy
exchange between spins and the lattice, the environment, after applying a transversal RF
pulse) and *
2T (characteristic time for decay of the transverse magnetisation produced by the
RF pulse which includes time for spin dephasing due to magnetic field gradients and
inhomogeneities). Thinner slices and a finer matrix provide high-resolution imaging of the
thorax.
Flexibility is both strength and weakness of MRI. The number of ways for MRI
screening of the chest is virtually unlimited, but not all imaging sequences can be applied to
every patient. The protocol of MRI acquisition, with respiratory and cardiac motion
compensation, needs to be designed to answer the project‟s specific clinical question.
Gadolinium-enhanced 3D MR angiography volume scan depicts best the origins and
direction of branch vessels. In-plane or through-plane 2D+time phase-contrast velocity-
encoded (VENC) data captures the flow development in the aortic arch. The left ventricular
outflow tract is captured in 1-3 slices of ‘white blood’ 2D+time steady-state free precessing in
sagittal or coronal plane. The same modality, but with slices orientated transverse to the
aortic valve, is known to capture the leaflets of the aortic valve. The single-phase ( 1T -
weighted) ‘black blood’ 2D spin-echo scan, gated to systole to achieve the optimal black
blood to tissue contrast, captures the aortic arch and thoracic aorta, in- and through-plane.
MRI is a non-invasive, radiation-free imaging method for the aortic vasculature and no
contrast injection is needed. As the project‟s study cohort includes children, MRI is favoured
for acquiring information about geometry and flow development in the aortic vasculature.
Contrast medium is routinely used in conjunction with fast dynamic imaging of the
cardiac tissue and vasculature to enhance the MR image contrast. It is not directly imaged
but its magnetic properties affect the tissue and vasculature relaxation. Increased doses of
contrast medium improve vascular visualisation.

36
MRI Contrast Medium
MR signal-enhancing effect, vasculature highlighting, or delineation of normal from
non-malignant tissues are possible as a result of the chemical reactions between the
contrast medium and screened body region. The chemical composition of the contrast
medium contains functional groups with unpaired electrons that affect either the 1T or 2T
relaxation times for the surrounding protons.
The contrast agents can be classified as either static or motion, exogenous or
endogenous, or as either positive or negative. Static contrast is sensitive to relaxation
properties of the spins while motion contrast is sensitive to the spin movement through the
lattice. Exogenous substance is foreign agent (drug) administered intravenously while
endogenous substance is dependent on the intrinsic property of the tissue. Positive agent is
called relaxation agent as it increases the spin flip transitions which results in reduced 1T
(and 2T ) values and increased brightness on 1T -weighted images. Negative agent is called
(chemical or frequency) shift agent as, due to magnetic susceptibility, it produces substantial
magnetic inhomogeneity to perturb the Larmor frequency of protons, resulting in a loss of
phase coherence and causing hypo intensity on 2T -weighted images.
The class of exogenous contrast agents are used in conjunction with the MR imaging
sequences for this project. They are classified based on magnetic properties into:
ferromagnetic, super-paramagnetic, and paramagnetic agents.
Ferromagnetic materials (like Fe , Ni ,Co ) have a crystalline structure that aligns the
cells‟ Weiss (magnetic) domains in the direction of the applied external magnetic field,
resulting magnetic domain that is retained when the external magnetic field is removed.
Super-paramagnetic materials (like magnetite 43OFe ) are smaller crystalline solids that
exhibit similar behaviour to the ferromagnetic materials, with the difference that, after
removing the external magnetic field, the orientation of the single domain disperses. They
are negative agents producing large reductions in 2T or *
2T (but no influence on 1T ).

37
Ultra-small super-paramagnetic iron oxides can also reduce 1T and produce 1T weighting.
Paramagnetic materials have activated the magnetic properties only in the presence of
applied external magnetic field. They have small positive magnetic susceptibility due to the
presence of one or more unpaired electrons, as for example 3
Gd with 7 unpaired
electrons; 3
Dy with 5 unpaired electrons; 2
Fe with 5 unpaired electrons; and Mn3+
with
4 unpaired electrons). They are both positive and negative (like the Dysprosium chelates)
agents. The paramagnetic contrast agent affects tissue relaxation through dipole-dipole
interactions- determined by strength and distance of the magnetic moments involved ( 2T ),
molecular motion ( 1T ), and magnetic susceptibility ( *
2T ).
The most common gadolinium-based contrast agent is the gadopentetic acid
DTPAGd  (Appendix 2). DTPAGd  is an extracellular low molecular weight positive
agent, but not yet FDA approved. The DTPAGd  molecule has a strong magnetic moment
compared to proton‟s one, resulting in strong dipole-dipole interactions with tissue protons.
The structure of the chelating agent will determine the distance between the unpaired
electrons of the Gadolinium ion and water protons. When the DTPAGd  molecule binds to
serum albumin the frequency of the complex is adjusted for enhanced relaxation.
There are eight gadolinium-based contrast agents marketed within the European
Union: gadodiamide (Omniscan), gadobenic acid (Multihance), gadobutrol (Gadovist),
gadofosveset (Vasovist), gadopentetic acid (Magenevist), gadoteric acid (Artirem, Dotirem),
gadoteridol (Prohance), and gadoxetic acid (Primovist). Omniscan, in particular, was shown
to be noxious (Stenver 2008). Magnevist was approved on the market in 1988 for anatomy,
blood flow and tissue characterisation (with delayed enhancement), while Prohance in 1992
and Omniscan in 1993. Magnevist is an ionic agent, while Prohance, Omniscan and
Optimark are non-ionic. The gadopentetic acid was shown, on a cohort of more than a
thousand adults, to have higher degree of safety and tolerance than conventional iodinated
contrast agents (Goldsmith 1986).

38
Control of Heart Rate
The nervous system consists of two main components connected using complex
neural pathways: the central nervous system and the peripheral nervous system. It contains
a network of specialised cells called neurons that transmit signals between different parts of
the human body. There are three main types of neurons: sensory (caries impulses from
peripheral receptors to the central nervous system), relay (caries impulses from the sensory
to the motor neurons) and motor neuron (caries impulses from the central nervous system to
effectors).
The main components of the central nervous system are the brain and the spinal cord.
The peripheral nervous system consists of sensory neurons and it is motor or sensory,
somatic (voluntary) or visceral (autonomic). In particular, the autonomic nervous system
(figure 1.4) controls at a level below the level of consciousness the functions of the internal
organs and it is divided into two subsystems: the parasympathetic and the sympathetic
division. The parasympathetic nervous system regulates the activities that occur in the
human body under rest condition. The sympathetic nervous system stimulates the activities
that occur under exercise condition but it is constantly active to maintain homeostasis.
Sympathomimetic hormones produce similar effects of transmitter substances of the
sympathetic nervous system. Based on the chemical structure they are divided into
catecholamines (adrenaline, noradrenaline, isoprenaline, dopamine, dobutamine) and non-
catecholamines (ephedrine, amphetamine, phenylephrine, tetrahydrozoline). According to
the mode of action the sympathomimetic drugs are divided into direct, indirect and dual
acting.
In this project two sympathomimetic hormones are used to induce the condition under
exercise: isoprenaline and dobutamine, both catecholamines (Appendix 3). They stimulate
the cardiac muscle contraction, increase the heart rate, and are vasodilators for blood
vessels, reduce the peripheral resistance, reduce the diastolic blood pressure and increase
the blood pulse pressure.

39
Effects Action Action Effects
α Dilatation of pupil Radial muscle of
pupil (+)
Circular muscle of
iris (+)
Constriction of pupil
α Secretion of thick
saliva
Salivary glands (+) Salivary glands (+) Secretion of watery
saliva
α
β2
Vasoconstriction
Vasodilatation
Blood vessel (+)
Blood vessel (-)
Lacrimal gland (+) Tear secretion
β1 Rate and force
increased
Heart (+) Heart (-) Rate and force
reduced
β2 Bronchodilatation Lung airways(-) Lung airways(+) Bronchoconstriction
β1,2
α
Decrease in
motility and tone
Gut wall (-)
Gut sphincters (+)
Gut wall (+)
Gut sphincters (-)
Increase in motility
and tone
β2
α/
β2
Glycogenolysis
Glyconeogenesis
(glucose release)
Liver (+) Pancreas (+) Increase in exocrine
and endocrine
secretion
α Adrenaline Adrenal medulla
(+)
β2 Relaxation Bladder
α
β2
Contraction or
relaxation
Sphincter (+)
Uterus (+/-)
Sphincter (-)
Bladder (+)
Micturition
α Ejaculation Vas deferens (+)
Seminal vesicles
(+)
Penis venous
sphincters
contracted (+)
Erection
α Sweating Sweat glands (+) Rectum (+) Defecation
α Piloerection (hair
stands on end)
Pilomotor
muscles)
Cillary muscle (+) Accommodation for
near vision
The target of catecholamines is the adrenergetic surface cell receptors. Both
isoprenaline and dobutamine are direct acting on  receptors. Isoprenaline is non-selective,
acting on 1 (heart and kidney) and 2 (bronchial smooth muscle) receptors while
dobutamine is selective, acting only on 1 adrenoreceptors.
The human  receptors are folded into groups of seven hydrophobic membrane-
spanning helices arranged as closely packed bundles with folded loops protruding into the
cytoplasm and extracellular space. The N termini of the proteins are thought to be in the
extracellular space and the C termini in the cytoplasm. They are responsible for heart
muscle contraction, smooth muscle relaxation and glycogenolysis (conversion of glycogen
polymers to glucose monomers). Defects in structure or functioning of human  receptors
have been associated with both asthma and heart failure.
Noradrenaline
Release
SYMPATHETIC SYSTEM
PostGanglionic
Nerves
PARASYMPATHETIC SYSTEM
Release
Acetylcholine
Figure 1.4 The autonomic nervous system.
Note: (+) excitation and (-) inhibition of the predominant adrenoreceptor

40
Protocol for Collecting Clinical Data
The standard operating procedure for data collection is based on clinically indicated
procedures (Moss 2008) while the non-clinically indicated procedures are based on the
ethical approved protocol (Ecabert 2008). The MR images are used for defining the aortic
geometry and flow data. The invasive pressure measurements are acquired in a hybrid
imaging system of X-ray and magnetic resonance so that the measurement location is
mapped in the coordinates of the medical image. The image blurring is reduced by
compensating the cardiac and respiratory motion. The cardiac motion is compensated by
synchronizing the image acquisition with the cardiac cycle using electrocardiographic ( ECG )
gating. The respiratory motion is compensated by using breath-hold. The medical image is
stored in the DICOM format with specific tags anonymised in the header.
The data collection is performed in 4 phases. In phase 1 all medical image data is
collected for the rest condition (90 minutes). In phase 2 invasive pressure measurements
are recorded for the rest condition ( 45 minutes). In phase 3 the sympathomimetic
hormones is administered and invasive pressure measurements are gathered for the
exercise condition ( 25 minutes). In the last phase the medical image data is provided for the
exercise condition (30 minutes).
In the first phase, acquisition of MRI for the resting condition, the respiratory motion is
compensated and the image acquisition is performed under breath hold. The acquisition time
for each image type is 53 minutes. The thoracic aorta is scanned firstly with MRAD 3
contrast-enhanced volume sequence (spatial resolution: mm5.1 slice thickness). Secondly,
a 4D steady-state free precision ( SSFP ) volume scan is acquired (spatial resolution:
mm4.2 ). Volumetric flow data is acquired for various positions along the aortic centreline
(ascending, transverse arch, pre-coarctation, post-coarctation, diaphragm level) using the
timeD 2 VENC phase-contrast cine flow (spatial resolution is set for mm86  slice
thickness and the VENC value in the range of
s
cm
450250  ).

41
In the second phase, the patient is moved on a second table for cardiac catheterisation
under breath-hold. Invasive pressure measurements are acquired with static protocol for
dual F5 multipurpose catheter and the drawback protocol is performed for single catheter at
kHz1 sampling rate. The position of the catheter is registered in the coordinate system of
the MR image.
In the third phase, the patient remains on the same table but the isoprenaline is
administered. The measurements recorded at the second phase are repeated for the new
condition, under exercise.
In the fourth phase, the patient is moved back on the first table and the volumetric flow
data is acquired with the same protocol as in phase one but for the condition under exercise.
At this point the clinical investigation finished, the patient is extubated and prepared for
recovery.
The study cohort is chosen to share the common characteristic of being suspected or
diagnosed with aortic coarctation. There are no age restrictions expected for this group of
people. Cardiac MRI is safe to be performed if coronary stents, joint replacements and most
prosthetic heart valves are present. Exclusion criteria appear for either MRI screening or
MRI guided cardiac catheter.
Patients with metallic implants like central nervous system aneurysm clips, implanted
neural stimulator, implanted cardiac pacemaker or defibrillator, cochlear implant, ocular
foreign body, insulin pump, metal shrapnel or bullet, or in the case of pregnant women are
not allowed to enter the study group.
The MRI-guided cardiac catheter has no absolute contraindications with the exception
of patient refusal. Relative contraindications are: severe uncontrolled hypertension,
ventricular arrhythmias, acute stroke, severe anaemia, active gastrointestinal bleeding,
allergy to MRI contrast medium, acute renal failure, uncompensated congestive failure
(impossibility to lie flat), unexplained febrile illness and/or untreated active infection,
electrolyte abnormalities (like hypokalemia) and severe coagulopathy.

42
Study Cohort
For the development of the model for aortic coarctation, retrospective and prospective
clinical data is provided in this study (table 1.1). Among a total of 16 patients diagnosed with
aortic coarctation, 7 were identified retrospectively, between December 1991 and November
0720 , and 9 investigated between May 0820 and March 1120 , before (pre-) or after (post-)
surgical intervention.
Table 1.1 Patient demographics for aortic coarctation studies
CoA
Study #
Sex Age
[years]
Weight
[Kg]
Height
[cm]
BSA
[m
2
]
Surgical
intervention
1 Female 16 52 158 1.5 Post-
2 Male 15 59 172 1.7 Post-
3 Male 25 64 175 1.8 Post-
4 Male 21 95 180 2.1 Post-
5 Male 20 71 183 1.9 Post-
6 Female 17 88 169 2.0 Post-
7 Male 7 22 120 0.9 Post-
8 Male 18 64 177 1.8 Pre-
9 Male 20 63 176 1.8 Post-
10 Male 17 71 177 1.9 Pre-
11 Male 35 95 175 2.1 Pre-
12 Male 12 46 151 1.4 Post-
13 Male 28 75 175 1.9 Post-
14 Male 35 63 206 1.9 Post-
15 Male 18 72 180 1.9 Pre-
16 Male 25 92 180 1.9 Post-
The control data (table 1.2) for 7 studies in group 1 (children) and 11 studies in group
2 (adults) is used for providing clinical measurements of healthy aortic geometries.
Table 1.2 Patient demographics and healthy aortic geometry features
Control Group 1: Children Control Group 2: Adults
Healthy
Study #
Sex Age
[years]
Healthy
Study #
Sex Age
[years]
101 Female 2 201 Male 23
102 Female 2 202 Female 21
103 Male 1 203 Male 39
104 Male 6 204 Female 42
106 Female 6 206 Male 19
208 Female 33
209 Male 24
210 Male 16
211 Female 20

43
1.4 Independent Clinical Risk Factors
The autonomic nervous system is transmitting impulses from the central nervous
system to the peripheral organs. This controls the heart rate, the cardiac contractility force,
dilation and constriction of the vessels, relaxation and contraction of smooth muscle cells
and gland activation.
Heart rate variability is an independent mortality risk factor as it represents a rhythm
indicative of the degree of physiologic health of the human system. Heart rate, like the
cardiac output, is influenced by both the parasympathetic and the sympathetic nervous
systems. The parasympathetic nervous system regulates the heart rate under rest condition.
The sympathetic nervous system adds influence on the regulation of heart rate under
exercise condition. Clinical evidence shows the same heart rate variation direction for both
male and female studies (O'Brien 1986).
In healthy subjects (figure 1.5) the heart rate is in the literature range (appendix 4) and
it declines with increasing age, at rest. When the dobutamine is administered, the variation
slope of the heart rate reduces with increasing hormone concentration.
20
40
60
80
100
120
140
160
180
0 10 20 30 40
HeartRate[bpm]
Age [years]
Heathy Aortic Male Studies
Rest
Exercise Dob10
Exercise Dob20
Linear (Rest)
Linear (Exercise Dob10)
Linear (Exercise Dob20)
Figure 1.5 Variation of heart rate with age for male studies with healthy aortic geometry

44
The aortic coarctation studies (figure 1.6) have the heart rate is in the same range and
declining variation under resting condition as in the case of the studies with healthy aortic
geometries. The highest concentration of dobutamine in healthy studies is proven to have
the same response in the body as with the concentration of isoprenaline injected in the
studies with aortic coarctation.
The higher the heart rate reached under exercise condition the better the life
prognosis. Smokers have less increase in heart rate under exercise than non-smokers. In
the case of aortic coarctation younger studies achieve higher heart rate increase under
exercise condition, while in the case of healthy studies the heart rate increase under
exercise is constant with age.
Generally, poor heart rate response to exercise is linked with reduced life expectancy
(Freeman 2006). The presence of left ventricular hypertrophy (due to flow regurgitation) is
associated with possible future development of heart failure or sudden cardiac death.
There is low risk of sudden cardiac death for patients with „normal‟ heart rate response
to exercise and high for those with „abnormal‟ heart rate response to exercise (with both
negative and positive history of cardiovascular disease). The normal range for heart rate
variation in the case of aortic coarctation studies is still unknown.
20
40
60
80
100
120
140
160
180
5 10 15 20 25 30 35
HeartRate[bpm]
Age [years]
Aortic Coarctation Male Studies
Rest
Exercise
Linear (Rest)
Linear (Exercise)
Figure 1.6 Variation of heart rate with age for male studies with aortic coarctation

45
1.5 Clinical Data Acquisition Techniques
Three-dimensional medical images are becoming more and more clinically used for
surgical planning, quantitative diagnosis and monitoring disease progress (Berti 2008). Data
acquisition represents the process of converting into digital numerical values the sampling
signals that measure the physical conditions of the real world.
Magnetic Resonance Imaging
Magnetic resonance imaging (MRI) and magnetic resonance angiography (MRA) are
fast becoming valuable tools in the non-invasive delineation of vascular abnormalities and
are beginning to replace the use of catheter-generated invasive x-ray angiography (XRA)
(Hummel 2008).
145MR Philips Achieva Nova scanner (sketched in figure 1.7) is designed to be patient
environmental, equipped with T5.1 magnet and high performance gradient system,
FreeWave RF system, MR workspace and scan tools. It produces 3D volume images by
reconstruction of recorded 2D slices. One immediate useful application is that the pressure
gradient in children with aortic coarctation is determined by low-field MRI (Rupprecht 2002).
In terms of the physics, the hydrogen atom inside the body possesses a nuclear spin.
Their fundamental property is relevant for MRI if it is unpaired. In the absence of external
magnetic field, the spin directions of all atoms are random and cancel each other. When the
material element is placed in an external magnetic field, the spins align with the external
field. By applying a rotating magnetic field in the direction orthogonal ( z ) to the static field,
the spins can be pulled away from it with an angle (Townsend 2008). The bulk magnetization
vector rotates in a precession around z at the Larmor frequency. The precession relaxes
gradually, with the yx  component reducing in time and z -component increasing. The
yx  component of the magnetization vector produces a voltage signal which is the
measured MRI signal.

46
The rapidly rotating transverse magnetization ( yx  ) creates a radio frequency
excitation within the material, excitation that produces an induced transverse voltage signal.
The magnetization vectors return to equilibrium by a double relaxation process, which give
rise to tissue contrast in the medical image acquired (Russo 2006).
The voxel brightness in the medical image can be enhanced if a contrast agent, like
DTPAGd  , is used. It is administered intravenously, surrounds the hydrogen atoms and
makes the lumen of the blood vessels, the highly vascularised tissues and the areas of blood
leakage appear brighter in the medical image.
Image blurring is reduced by compensating the cardiac and respiratory motion. The
cardiac motion is compensated by synchronizing the image acquisition with the cardiac cycle
using electrocardiographic (ECG) gating. The respiratory motion is compensated by using
breath-hold (Attili 2011). The medical image is stored in the DICOM format with specific tags
anonymised in the header.
Figure 1.7 MR image acquisition – components and setup

47
Phase Contrast-Magnetic Resonance Volumetric Flow Rate
Phase-contrast Magnetic Resonance Volumetric Flow Rate (PC-MR VFR) is well-
known, but undervalued, method of obtaining quantitative information on blood flow (Srichai
2009). It is based on the principle that blood flowing at a constant velocity through a magnetic
field gradient will experience a predictable change in spin phase relative to the surrounding
stationary tissue.
PC-MR VFR is an MRA acquisition technique that can provide flow velocity, and, by
post-processing, the volume flow rate and flow characteristics. Flow measurements are most
precise if the imaging plane is perpendicular to the vessel of interest and flow encoding is set
to through-plane flow, but they lack the necessary resolution in the near wall region
(Svensson 2006). The overall error in flow measurement can be reduced to less than %10 , an
acceptable level of error for routine clinical use (Wentland 2010).
In terms of the physics, the radio-frequency induced transverse magnetization before
the application of the flow-sensitizing gradients assures the acquisition of the motion-induced
phase shift. Every MR imaging data acquisition yields information about the signal
magnitude as well as the phase of each voxel. Signal intensities are processed into the
magnitude anatomic image. In phase-contrast measurement, the phase information is used
to calculate the velocity in each voxel in the form of a phase or velocity image.
Magnetic moments (spins) moving along a magnetic field gradient acquire a phase
shift  (within a range of o
180 ) relative to the ones of the stationary tissue. For linear
gradients,  is proportional to the velocity of the moving spin. The voxel calculation of
velocities uses the phase difference that remains after subtraction of data sets obtained with
both directions of the bipolar gradient.
Velocity encoding ( encv ) determines the highest and lowest detectable value given by a
phase-contrast sequence and it is inversely related to the area of the flow-encoding
gradients.

48
By entering the threshold value of encv , the amplitude of the flow-sensitizing gradients
are calculated so that the peak velocity corresponds to a phase shift of o
180 . The velocity v
can be determined by the phase difference  acquired in the two interleaved
measurements:
vm 
where  is the gyromagnetic ratio and m denotes the difference of the first moment of the
gradient-time curve.
For rectangular bipolar gradient pulses, m simply means the product of the gradient
area and the time between the two lobes of the bipolar gradient.
The MRI data contains 3 sections, in each with a number of images that present the
evolution along the cardiac cycle. The aortic geometry is presented in the first section, the
magnitude image (figure 1.8.A). The change in flow pattern in oblique sagittal plane is shown
in the second section, the in-plane phase-contrast image (figure 1.8.B). The third section
represents the through-plane phase-contrast image (figure 1.8.C).
Figure 1.8 Sections of the PC-MR VFR folder. A: magnitude image. B: in-plane phase-
contrast image. C: through-plane phase-contrast image.
The velocity is extracted from the third section with the Philips DICOM Viewer. The
relevant region from the image is identified by comparing, after a few processing steps, the
first section of this file (figure 1.9.A) with the MRI folder that records the 3D morphology
(figure 1.9.B).
A B C
eq 1.1

49
Figure 1.9 Comparison of MRI data. A: PC-MR VFR.
B: 3D morphology MRI
The volumetric flow rate is computed by multiplying the values for mean velocity and
cross-sectional area (figure 1.10).
Figure 1.10 Example of processing window for Philips DICOM Viewer
Ascending aorta
Descending aorta
A B

50
Hybrid Imaging System of X-ray and Magnetic Resonance
The XMR interventional suite at King‟s College London (Rhode 2003) comprises an X-
ray and RF shielded room. The room contains a T5.1 cylindrical bore MR scanner (Philips
Intera I/T) and a mobile cardiac X-ray set (Philips BV Pulsera). The patient can be moved
easily, in less than s60 , between the two systems (Philips Angio Diagnost 5 Syncratilt table).
The room has two distinct zones: the MRI zone (with magnetic field above mT5.0 ), and the
non-MRI zone (with the X-ray system), as sketched in figure below.
Figure 1.11 The XMR guidance system – components and setup
The position of the catheter is mapped on the MR image (Rhode 2003, Shekhar 2007).
The transcatheter peak-to-peak pressure gradient is considered the gold standard for
aortic coarctation cases (Takeda 2008). The aortic vessel is cannulated under aseptic
conditions with a cannula (catheter) using Seldinger (guideline) technique.
Philips Intera I/T
MR System
Philips BV Pulsera
X-ray System
Sliding Table
Top
X-ray Table
X-ray Image &
Registration Matrix Tracking
Software
MR/X-ray
Visualisation
Software

51
On the liquid-filled catheter is located a sensor that can be either extra-vascular or
intra-vascular. The value recorded by the sensor in air is 32768 instrumental units, value that
needs to be removed to have the recorded values in liquid:
(value [instrumental units]-32768)•0.01=value [mmHg]
Inaccurate invasive pressure measurements may appear due to the fact that the
endothelium layer of the vessel wall can be damaged by mechanical (too big a cannula, poor
insertion technique, inflexible cannula) or chemical means (infection from the skin puncture
wound on cannulation or from contaminated infusions).
The recorded data is a 1D vector of pressure values over several cardiac cycles. The
primary focus of processing this data is to split it in vector per cardiac cycle, and filter it to
remove instrumental noise. One method of splitting the data is to search the location of a
minimum value (for starting point of the cardiac cycle). This can be done using a
minimization strategy: Brent‟s method (Golden section search), gradient descent method
(the minimum is at the steepest descent), Nelder-Mead method (downhill simplex method),
Powell‟s method (direction set method), with coarse-to-fine strategies (Zöllei 2007).
1.6 Image Processing
The volume of datasets produced in the hospitals has created the need for an efficient
structure of their management and processing. The underlying framework of this work is the
automation of a processing workflow for the aortic geometries so that it becomes clinically
usable in real-time (Raval 2005). General segmentation of 3D vascular structures within
medical images continues to be a challenge as every patient has unique aortic dimensions
and shape. Two common artefacts appear in medical images: the background to be as
bright as vessel areas and, more importantly, the “partial voluming” (the vessel wall is only
partially inside the boundary voxel, whose intensity is a combination of the vessel intensity or
the background). An accurate determination of the vessel geometry can provide quantitative
morphological information directly from the original 3-D images.
eq 1.2

52
DICOM Format
The acquired medical image is stored as a data set in the DICOM file format (ISO 12052
2011). A DICOM data set is represented as a series of sequences of items, and each item
contains data elements (figure 1.12). The data is stored sequentially. Each data element
contains four fields: data element tag, value representation field (optional), value length and
value field.
The data element tag is an ordered pair of two 16-bit unsigned integers. The first
integer represents the group number and the second is the element number. The group
number contains four 16-bit unsigned integers for data elements, while the delimitation of
sequences and items (both start and end points) are stored with the group number fffe . The
element number is four 16-bit unsigned integers for data elements, 000e for the start
delimitation and de00 for the end delimitation in the case of both sequences and items.
The value representation (optional) field is a 2-byte character string. It is encoded
using the Data Dictionary (PS3.6).The value length field is a 16 or 32-bit unsigned integer
containing the length of the value field in number of bytes.
The DICOM standard allows the exchange of files, containing data with different
acquisition techniques, from different scanners, using the Picture Archiving and
Communication System (PACS).
Figure 1.12 DICOM data element structure
Data Set
order of transmission
Data Element
Data
Element
Data
Element
Data
Element
. . . . .
Tag
field
Optional
field
Value
Length
Value
field

53
Image Registration
The rapid development of image acquisition devices invoked the need of automatic
methods for image registration in the processing workflow. Sometimes also known as
„spatial normalisation‟, Image registration is the process of estimating an optimal
transformation between two images (Crum 2004). For simplicity, one image is nominated as
„fixed‟ (f ) and the second image, that is compared to the first one, as „moved‟ ( m ).
There are several classification criteria of the image registration methods (figure 1.13).
According to dimensionality, the registration of two images is performed in either spatial or
temporal direction. Registration uses with images from the same modality (for example MRI),
or from different modalities, for example MRI with CT. Image registration methods are
divided into three categories: image-image, feature-feature or model-image. For the first two
registration methods the input data is either intra-subject (from the same source) or inter-
subject (from more than one source). In the case of model-image registration atlas image
can be used to register subject image (Maintz 1998).
The components of the image registration process are: the reference (fixed) and target
(moved) datasets, the transformation model, the similarity criterion and the optimization
method.
The datasets use in the registration process: raw intensities (smoothed and re-
sampled), curves and surfaces, landmarks, or feature images (e.g. edge images).
Figure 1.13 Classification of image registration methods
methods

54
The transformation model can be either image (intrinsic) or non-image (extrinsic)
related. It can be either rigid (preserve distances between every pair of points), or non-rigid:
similarity (conformal mapping; linear change of coordinates), affine (preserve straight lines
and ratios of distances), piece-wise affine (preserve the area), or elastic transformation. The
number of degrees of freedom (DOF), in the Cartesian coordinate system, is: 6 - 3 for
translations and 3 for rotation (rigid transformation), 7 (similarity transformation), 12 (affine
transformation), or up to hundreds or thousands of DOF (elastic transformation).
Similarity measures involve intensity-based and geometric-based terms with an
additional weighting factor to control the influence of the two terms. There are 2 types of
similarity measures: intensity-based methods and feature-based methods. The intensity-
based similarity measures are: sum of squared differences (valid only in the case of mono-
modal image registration, with properly normalized intensities in the case of MR); normalized
cross-correlation (allows linear relationship between the intensities of the 2 images); mutual
information (more general metric which maximizes the clustering of the joint histogram). The
feature-based similarity measures are: distance between corresponding points, similarity
metric between feature values (Glocker 2008).
When the registration problem is ill posed (which means that number of variables is
greater than the number of observations), the algorithm requires optimisation strategies like:
gradient descent, conjugate gradient descent, multi-resolution search, deterministic
annealing, or locally adaptive regularization (Stefanescu 2004).
There are many types of image registration algorithms, many designed for specific
applications. One of them, voxel matching method, very popular for bone structures, is not
be well suited for registering vascular images due to the poorly differentiation of the tissues
in the image. In practice, there are three basic general approaches: optical flow, mutual
information and finite element methods.

55
Optical flow was a method developed for detecting small movement in 2D image
sequences for uses in robotics for example (Weickert 2006). The assumption of the optical
flow method is the equation:
t)ty,yx,f(xt)y,m(x, 
when t)y,f(x, is registered to t)y,m(x, . In other words, the assumption states that a point in
the image moved in space and time has the same intensity value. Although the optical flow
equation was developed for 2D imaging, it can be extended to 3D.
Manipulation of the equation eq1.3, with appropriate approximations, gives:
0









t
f
t
y
f
y
x
f
xmf
A key feature of the approach is that it provides a direct algorithm for computing the
displacements zyx  ,, for every voxel and the equations are linear in parameters of the
Fourier series decomposition. The registration equation is accurate if the images represent
topologically-similar structures.
Another approach for computing the mapping is to measure mutual dependence of the
information in the images in the method known as Mutual Information. This was initially
developed for inter-subject registration. It is based on the assumption that the tissue has
regions with similar intensities in the two images that are to be registered (Pluim 2003). Based
on the 2D image histogram, the average ratio for all regions, 2
)( mf  , is minimized to obtain
registration. Although this method is considered a „gold standard‟ approach, it has one
problem, that the Fourier decomposition for clinical images is not typically twice
differentiable.
The third approach is the deformable Finite Element Method. The spatial relationship
between volume elements of corresponding structure across image data sets are considered
to distort under the influence of forces modelled as
r
f
mf


 )( , where },,,{ tzyxr (Brock 2005).
eq 1.3
eq 1.4

56
If a solid model is used then, since the forces go to zero as the images move towards each
other, complete registration cannot be achieved. An alternative is to model the images as a
viscous material in which case the forces going to zero is less of a problem. The main
conceptual problem is that the forces have no physical reality and there is an uncertainty
about their scale and the values for the material properties. An advantage of the viscous
model is that different values of viscosity can be used for different structures. The model is
formulated as KuF  , where F is the force, u is the displacement and K is based on the
material properties. The method is easy to implement; different material properties can be
used in the strain model, and the code is easy to debug.
Centreline Extraction
The skeleton for a geometric shape, also known as the symmetry axis or centreline, is
a series of points equidistant from at least 2 points on the boundaries. Its extraction is not
very sensitive to image noise as it processes a larger portion of the vessel length. It is an
important element in the processing workflow, either before or after image segmentation, or
sometimes as independent step required for surgical planning and guidance (Aylward 2002).
Although it may be done by hand labelling, automation of the method would make this
usable in clinical practice. Most techniques (table 1.3), depending on the image modality,
require a more sophisticated shape extraction routine than global image thresholding (basic
or adapted to image regions) (Wilson 1999). The vessel shape can be approximated using
Voronoi polygons, and the centreline is composed of their centres, but the method is
computationally intensive (Frangi 1999).
Skeletonization via distance maps and level set method divides the shape‟s boundary
based on points of maximum curvature, a map of distances is created for each segment, all
maps are superimposed and the zero level is found between all of them. Although the
method is quite accurate, it makes heavy use of interpolations (Lorigo 1999). The speed and
accuracy of the process can improve with dynamic allocation of the scales (Aylward 2003).

57
Table 1.3 Review of methods for centreline extraction
Method Centreline
Generation
Centreline
Accuracy
Speed Automation Application and
Modality
(Aylward 2003)
Dynamic vs. static
scale
Explicit
centerline
transversal
Best Better
requires
isotropic voxels
(0.4s/20 voxels)
Best
requires
seeding
Surgical planning
and guidance
arbitrary tubes
arbitrary modality
(Frangi 1999)
Model optimization:
conjugate gradient
algorithm
Explicit
(B-spline)
centerline
refinement
Better
no small vessels
~9% error
Better Good
end-points
and boundary
required
Aneurysms at
carotid bifurcation
arbitrary tubes
requires boundaries
(Lorigo 1999)
Colour
segmentation with
threshold value as a
function of noise
Implicit
level-set
evolution
Better
difficult to
balance noise with
small vessels
Best Best
full
Neurosurgical
planning
arbitrary tubes
arbitrary modality
(Wilson 1999)
Expectation
maximization
algorithm
Post process
adaptive
threshold
Better
not small
vessels
centerline
requires thinning
Best Better
cannot limit to
extracting tubes
Neurosurgical
planning
non-tubular objects
requires ROI
Global thresholding Post-process Good
no small vessels
centerline
requires thinning
Best Better
cannot limit to
extracting tubes
Coarse surgical
planning
non-tubular objects
requires ROI
Hand labelling Explicit Unsatisfactory
poor localization
poor connectivity
Unsatisfactory Unsatisfactory
none
Current clinical
standard
arbitrary objects
arbitrary modality
Vascular Segmentation
In computer vision, segmentation is the processing technique that extracts from the 3D
digital image an anatomical structure of interest. The image is divided into regions and then,
based on the segmentation algorithm, every element (pixel for 2D or voxel for 3D images) is
allocated into a category: region of interest (ROI), boundary or background.
Currently, the clinical method for segmentation is hand-labelling. As it provides
unsatisfactory amount of time and user effort, automatic and semi-automatic techniques are
necessary to increase the accuracy of the results and to reduce the duration of this step.
Although MRI is based on non-ionizing radiation, little work has been reported on automated
quantitative MRI directly from 3D data (Frangi 1999). In table 1.4 are presented various
methods to define vessel boundaries. Terms like „good‟, „better‟, „best‟ are given as the
evaluation of the algorithm‟s performance is often a difficult task because of the lack of the
ground truth for comparison (Chao 2008).

58
Analyzing anatomical structures that exhibit non-spherical topologies, concavities, or
protrusions is difficult without first applying a robust segmentation method that can handle
any combination of these object conditions (Poon 2008).
It is a compulsory first processing step in the workflow as it provides the boundary that
encloses the fluid volume required for CFD simulations. Its accuracy is crucial for the
simulation result as it influences the development of the complex flow imposed by the
boundary conditions (Svensson 2006).
Table 1.4 Review of extraction techniques for blood vessels
Method Study Accuracy Speed Automation
Application
and
Modality
1.
P
A
T
T
E
R
N
R
E
C
O
G
N
I
T
I
O
N
T
E
C
H
N
I
Q
U
E
S
Multi-Scale
Segmentation
(Koller 1995)
Parameter-free
multi-scale
technique
Best
Detect
simultaneously
lines of both
polarities
Better
Half of
normal time
Better
(semi-
automated)
Brain, MRA
(Székely 1993)
+level set
method
Good
only with use of
multi-resolution
filter
Good
Incipient
stage
(analysis)
Better
(semi-
automated)
Brain, MRA
Very thin
vessel
extraction
(Lai 2009)
Hierarchical
evolutionary
algorithm
(genetic)
Better than
competitive
Hopfield neural
networks,
dynamic
thresholding, k-
means clustering
and fuzzy c-
means methods
Best
For specific
parameters
Best Skull, CT
Abdomen,
brain, knee,
MRI
Computer
generated
phantom
image, MRI
Region
Growing &
Edge Detection
(Adams 1993) Better
Better Better
(semi-
automated)
Any
Histogram-
based
Segmentation
(Gao 1996) Best
Best
(5-6mins/
3D image)
Better
(semi-
automated)
Liver, CT
Segmentation
by Graph
Partitioning
(Haris 1999)
Better
80%
Good
1 min/2D
image
No
User
interaction
Coronary
arterial tree,
MRI/MRA
handles well
bifurcations
Segmentation
with
Watershed
Transformation
(Hamarneh 2009)
+ clustering
methods
-the over
segmentation
problem is
handled by
clustering
Better
Affected by
limitations
of the
clustering
algorithm
Better
There were
cases were the
images
required further
processing
Left ventricle
wall and brain
smooth
boundaries
with sub pixel
resolution
2.
Model-based
Segmentation (McInerney 1996) Best Best
Best
(fully-
automated)
Any
0. Hand labelling Explicit Unsatisfactory
10-11mins
/3D image
Unsatisfactory
none
Current clinical
standard
arbitrary
objects and
modality

The Development of Computational Fluid Dynamic Models for Studying the Detection and Diagnostic Techniques of Aortic Coarctation

The Development of Computational Fluid Dynamic Models for Studying the Detection and Diagnostic Techniques of Aortic Coarctation

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