CT and MR Imaging of
Abdominal Aortic Aneurysm
ROCIO CABRERA
GUILLAUME LEMAITRE
MOJDEH RASTGOO
Presentation Outline
2
 Introduction to Abdominal Aortic Aneruysms
 Computed Tomography of AAA


Imaging Technique
Image Processing
Level Set Methods
 Active Shape Models

 Magnetic Resonance of AAA

Imaging Techniques
Galodinium-enhancement
 Diffusion weighted


Image Processing
Markovian Method
 Graph-Theoretic Approach

 Conclusions
Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms
1/13/2011
Introduction
What is an Abdominal Aortic Aneurysm?
3
 Aneurysm

Vascular pathology consisting of an
irreversible dilation of a segment
of a blood vessel
 Abdominal aorta


Continuation of the thoracic aorta
and begins at the level of the
diaphragm
Largest artery in the abdominal
cavity
 Abdominal Aortic Aneurysm

Accepted criterion: 50% increase in
vessel diameter
Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms
1/13/2011
CT Imaging
4
CT has been used widely in AAA
 CT Imaging speed
 CT provides detailed quality for better analysis of aneurysm and
adjacent arteries
 Detailed information of aorta and its branches for 3D
reconstruction
 Flexible to different post processing methods
 Appreciated in surgical planning
Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms
1/13/2011
CT Image Processing
AAA Segmentation through level set method
5
 Level Set Method (LSM) is a numerical method for tracking interface a
shapes . More specifically for shape varying objects
plane
Level set function
Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms
1/13/2011
CT Image Processing
AAA Segmentation through level set method
6
1 - Segmentation the volumes
Defining the mesh on the object (Level Set function )
Updating the mesh values using the Speed function
F ( x, y)  ( F0 x, y  Fc ( x, y) x, y )e Fi ( x, y)
Force at mesh
point (x,y)
Curvature force (x’ ,y’)
Advection term = 1
Image force based
on the Gaussian
derivative filter
2 - 3D reconstruction – Using Marching cubes
Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms
Multi resolution Analysis –
LR( half) volume
Top – Down , Narrow band update
restricted to the zero level set
Scaling up the result
Repeating the algorithm on full data
set
1/13/2011
CT Image Processing
AAA Segmentation through level set method
7
 Advantage:

Level set method has the
advantage to provide more
accurate results specially in
segmenting the small details
 Disadvantage

It has a very high computational
cost
 Suggestions

Combination of the methods ,
while level set can be used to
improve the initial segmentation
results
Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms
1/13/2011
CT Image Processing
AAA Segmentation using Active Shape Model
8
 Active shape model (Smart Snake) was developed by Cootes et
al. in order to over come the problems with snake segmentation


Active counter model (Snake) segmentation depends on the initial snake
Active counter model is not capable to deal with the occluded objects
 In Medical Imaging ASM is applied on the combination of shapes
and gray level sets
Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms
1/13/2011
CT Image Processing
AAA Segmentation using Active Shape Model
9
Shape Modeling
Shape alignment
Aligning the images in the same reference axes , using Procrustes Analysis
 Procrustes Analysis minimize the distance between reference shape and each shape in the
dataset

Statistical Computations PCA





Modeling the shape variations
1 N
1 N
x   xi
s
( xi  x )( xi  x )T

Computation of the mean shape
N i 1
N  1 i 1
Computation of the scatter matrix
Sorting the eigenvectors and keeping the first k eigenvectors i , based on the largest
eigenvalues
Eigen decomposition of the shapes x  x  b where ,   {1 ,  2 ,... k }
N

Value of k is based on

i 1
i
k
 f v  i
i 1
0  fv  1
Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms
1/13/2011
CT Image Processing
AAA Segmentation using Active Shape Model
10
Shape Modeling
x  x  b
 3 1
 3 4
 3 3
 3 2
 3 5
 3 6
M. Bruijne ,B. van Ginneken, M. A. Viergever, W. J. Nieesen, « Interactive Segmentation of Abdominal Aortic Aneurysms in
CTA Images », 2004
Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms
1/13/2011
CT Image Processing
AAA Segmentation using Active Shape Model
11
Grey level Appearance Modeling
 Sum of absolute difference between the reference and sample image over several
resolutions
 The sum is performed for each landmarks for a defined window size
AD 
k t k n
 I ( x)  I ( x)
i 1
s
r
M. Bruijne ,B. van Ginneken, M. A. Viergever, W. J. Nieesen, « Interactive Segmentation of Abdominal Aortic Aneurysms in
CTA Images », 2004
Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms
1/13/2011
CT Image Processing
AAA Segmentation using Active Shape Model
12
Model Fitting
 First slice manually Initialized while for the others previous counter was considered
as initialization
 Performing the multiresolution analysis for higher accuracy
M. Bruijne ,B. van Ginneken, M. A. Viergever, W. J. Nieesen, « Interactive Segmentation of Abdominal Aortic Aneurysms in
CTA Images », 2004
Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms
1/13/2011
MR Imaging of AAA
Alternate MR Imaging Approaches
13
Gadolinium-enhanced MRI
Diffusion Weighted MRI
 Gadolinium injection (paramagnetic CA)
 Orta et al. [1] reported its use to


Shortens the T1 relaxation time of blood,
distinguishing it from its surroundings
No known side effects nor nephrotoxicity
 Prince et al. [2] reported an agreement
in measured AAA size in CT, MR and US
diagnose inflammatory AAA


Hyper-intensity surrounding the aorta
Region ADC = 1.24 x 10-2 mm2/s
 ADC
consistent with a restricted
diffusion due to inflammation
Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms
1/13/2011
MR Image Processing
Markovian-Active Contour Segmentation
14

X – 2D [MxN] random field that models the segmentation labels
Y – 2D [MxN] random field that models the input image
s – site (pixel) located at position (i,j)
MAP – Maximum A Posteriori Probability, through the Bayes Rule

Assuming Gibbsian distributions



Likelihood Energy.
Natural Logarithm of a Gaussian
A pixel s will switch classes if and only if at least one of its
neighbors has already been assigned the new class label
Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms
1/13/2011
MR Image Processing
Markovian-Active Contour Segmentation
15
 Direct extension to 3D and 4D


3D – Neighbouring sites in k+1 and
k-1 images
4D – Neighbouring sites in t+1 and
t-1 time frames
 AAA reconstruction from MRI


Initialization done by expert hand
Seed growth until convergence
 4D segmentation results
Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms
1/13/2011
MR Image Processing
Graph-Theoretic Segmentation
16
 Aortic Surface Pre-segmentation
 Fast marching level set method used to
compute {appSt}tє[0,N-1]
 Centerline Extraction
 Centerline
determined from each
approximate surface by skeletonization
 Accurate Surface Segmentation
 Weighted graph G = (V; E)




V – node set of image pixels
E – arc set of neighbourhood system
Every arc ‹vi, vj› є E has a cost
Graph-cuts aim to partition a weighted
graph into 2 disjoint subsets

Minimize the cost function ε(f)
 Appropriate
design of a energy
function, a minimum s-t cut can
segment a region of interest in an
image.
Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms
1/13/2011
Conclusions
17
Strong interest in exploiting the capabilities of the medical imaging modalities to diagnose AAA
 Choice of the imaging modality


CT




MR




Appropriate to detect inflammatory AAA
Does not employ ionizing radiation
Contrast agent is appropriate for patients with renal insufficiency
Image processing techniques




Modality of choice in most institutions
Appropriate for emergency patients
Use of an iodinated contrast medium and ionizing radiation
2 CT and 2 MR methods have been presented
Most methods rely on segmentation of the aorta and measurement of the vessel diameter
Each methods could be extended to the other modality, but not much research has been done on it
It would be interesting to perform a study in which the several segmentation methods are used
on CT and MR in order to evaluate the methods and the imaging modalities in a better way.
Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms
1/13/2011
References
18
[1] Orta, K. and Kilickesmez, O. Clear Depiction of Inflammatory Abdominal Aortic Aneurysm with Diffusion-Weighted Magnetic Resonance Imaging.
Cardiovascular and Interventional Radiology. (2010) 33:379-382.
[2] Prince, M. et al. Gadolinium-enhanced Magnetic Resonance Angiography of Abdominal Aortic Aneurysms. Journal of Vascular Surgery. (1995) Volume 21.
Number 4.
[3] Jodoin, P. et al. Markovian Method for 2D, 3D and 4D segmentation of MRI. (2008) 15th IEEE International Conference on Image Processing.
[4] Sonka, M. et al. Early Detection of Aortic Aneurysm Risk from 4D MR Image Data. (2006) Computers in Cardiology.
[5] Kang Li et al. Optimal Surface Segmentation in Volumetric Images - A Graph-Theoretic Approach (2006) IEEE Transactions on Pattern Analysis and Machine
Intelligence. Volume 28. Number 1.
[6] Crawford, C. et al. Abdominal Aortic Aneurysm: An Illustrated Narrative Review. (2002) Journal of Manipulative and Physiological Therapeutics. Volume 26.
Number 3.
[7] Marleen de Bruijne and Bram van Ginneken and Max A. Viergever and Wiro J. Niessen. Interactive Segmentation of Abdominal Aortic Aneurysms in CTA
Images. 2004.
[8] TF. Cootes and A. Hill and C.J.Taylor and J.Haslam and Manchester M Pt. The Use of Active Shape Models For Locating Structures in Medical Images. 1994.
[9]T.F. Cootes and C.J. Taylor and Manchester M Pt. Statistical Models of Appearance for Computer Vision. 2000.
[10] Cootes, T. F. and Taylor, C. J. and Cooper, D. H. and Graham, J. Active shape models - their training and application. Comput. Vis. Image Underst. 1995.
Volume 61. Issue 1. Pages 38-59.
[11] A. Hill and A. Thornham and C. J. Taylor. Model-Based Interpretation of 3D Medical Images. In British Machine Vision Conference. 1993. BMVA Press.
[12] Michael Kass and Andrew Witkin and Demetri Terzopoulos. Snakes: Active contour models. International Journal of Computer Vision. 1988. Volume 1.
Number 4.
[13] Derek Magee and Andrew Bulpitt and Elizabeth Berry. Level Set Methods for the 3D Segmentation of CT Images of Abdominal.
[14] Steven C. Mitchell and Boudewijn P. F. Lelieveldt and Rob J. van der Geest and Hans G. Bosch and Johan H. C. Reiber and Milan and Milan Sonka. Multistage
Hybrid Active Appearance Model Matching: Segmentation of Left and Right Ventricles in Cardiac MR Images. IEEE Transactions on Medical Imaging. 2001.
Volume 20.
[15] Wikipedia. Level Set Method. December 2010. $http://en.wikipedia.org/wiki/Level\_set\_method$
[16] Wikipedia. Isosurface. December 2010. $http://en.wikipedia.org/wiki/Isosurface$
[17] Wikipedia. Marching Cubes. December 2010. $http://en.wikipedia.org/wiki/Marching\_cubes$
[18] George G. Hartnell. FRCR, FACC. Imaging of Aortic Aneurysm and Dissection: CT and MRI. Journal of Theoretical Imaging. 2001.
Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms
1/13/2011
Appendix - AAA Segmentation through level set method
19
 Defining static and evenly spaced mesh in the image using
 Mesh values are updated using the speed function
t 1  F   0
t
 Speed function F depends on :



Advection term (constant value)
Curve term based on zero level set
Image term (Based on the edges)
Updating the curve and Image terms based on nearest neighbor in
the zero level set !! Computationally expensive
Reducing the computational cost by updating the mesh restricted to
the zero level set area ; Narrow band with the level set method
Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms
1/13/2011
Introduction
Pathogenesis of an Abdominal Aortic Aneurysm
20
 Elastin
Environment
Genetic
Factors
Atherosclerosis
Smoking
Inflamation
Elastase activity increase
vs. Inhibition decrease

Elastin Destruction
Collagen
remodelling
Failure of Elastin
Hypertension
Increased load on
collagen
Failure of Collagen
Alteration in
vessel geometry
Aneurysmal Dilation
Rupture

Ageing
Responsible for the elastic recoil of
the arteries due to the pulsatile
blood flow
Degradation of elastin fibres will
shift load to collagen fibres
 Elasticity decrease
 Diameter increase
 Aortic rupture
 Genetic
and
environmental
factors contribute to AAA
development
 Great
clinical importance
determine aortic diameter
Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms
to
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MR Imaging of AAA
Traditional MRI limitations on Vascular Imaging
21
 Traditional MRI
 Widely used to retrieve anatomical and physiological information of
patients
 Vascular imaging limitations
 Flow artifacts generated in different pathologies
Aneurysms – slow, swirling flow
 Stenotic vessels – turbulent flow

Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms
1/13/2011