1. ROCIO CABRERA GUILLAUME LEMAITRE MOJDEH RASTGOO CT and MR Imaging of Abdominal Aortic Aneurysm

2. Presentation Outline 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 1/13/2011 2 Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms

3. 1/13/2011 Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms 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 Introduction What is an Abdominal Aortic Aneurysm?

4. CT Imaging 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 1/13/2011 4 Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms

5. 1/13/2011 Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms 5 Level Set Method (LSM) is a numerical method for tracking interface a shapes. More specifically for shape varying objects Level set function plane CT Image Processing AAA Segmentation through level set method

6. 1/13/2011 Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms 6 CT Image Processing AAA Segmentation through level set method Defining the mesh on the object (Level Set function ) Updating the mesh values using the Speed function Multi resolution Analysis – LR( half) volume 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 2 - 3D reconstruction – Using Marching cubes 1 - Segmentation the volumes Force at mesh point (x,y) Advection term = 1 Curvature force (x’,y’) Image force based on the Gaussian derivative filter

7. 1/13/2011 Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms 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 CT Image Processing AAA Segmentation through level set method

8. 1/13/2011 Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms 8 CT Image Processing AAA Segmentation using Active Shape Model 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

9. 1/13/2011 Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms 9 CT Image Processing AAA Segmentation using Active Shape Model Shape Modeling Shape alignment Statistical Computations PCA 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 Modeling the shape variations Computation of the mean shape Computation of the scatter matrix Sorting the eigenvectors and keeping the first k eigenvectors, based on the largest eigenvalues Eigen decomposition of the shapes where, Value of k is based on

10. 1/13/2011 Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms 10 CT Image Processing AAA Segmentation using Active Shape Model Shape Modeling M. Bruijne,B. van Ginneken, M. A. Viergever, W. J. Nieesen, « Interactive Segmentation of Abdominal Aortic Aneurysms in CTA Images », 2004

11. 1/13/2011 Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms 11 CT Image Processing AAA Segmentation using Active Shape Model 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 M. Bruijne,B. van Ginneken, M. A. Viergever, W. J. Nieesen, « Interactive Segmentation of Abdominal Aortic Aneurysms in CTA Images », 2004

12. 1/13/2011 Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms 12 CT Image Processing AAA Segmentation using Active Shape Model 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

13. 1/13/2011 Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms 13 Gadolinium injection (paramagnetic CA)  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 Orta et al. [1] reported its use to diagnose inflammatory AAA  Hyper-intensity surrounding the aorta  Region ADC = 1.24 x 10 -2 mm 2 /s ADC consistent with a restricted diffusion due to inflammation Gadolinium-enhanced MRIDiffusion Weighted MRI MR Imaging of AAA Alternate MR Imaging Approaches

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 A pixel s will switch classes if and only if at least one of its neighbors has already been assigned the new class label 1/13/2011 14 Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms MR Image Processing Markovian-Active Contour Segmentation Likelihood Energy. Natural Logarithm of a Gaussian

15. 1/13/2011 Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms 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 MR Image Processing Markovian-Active Contour Segmentation

16. 1/13/2011 Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms 16 Aortic Surface Pre-segmentation  Fast marching level set method used to compute {appS t } 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 ‹v i, v j › 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. MR Image Processing Graph-Theoretic Segmentation

17. Conclusions Strong interest in exploiting the capabilities of the medical imaging modalities to diagnose AAA Choice of the imaging modality  CT  Modality of choice in most institutions  Appropriate for emergency patients  Use of an iodinated contrast medium and ionizing radiation  MR  Appropriate to detect inflammatory AAA  Does not employ ionizing radiation  Contrast agent is appropriate for patients with renal insufficiency Image processing techniques  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. 1/13/2011 17 Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms

18. References [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. 1/13/2011 18 Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms

19. Appendix - AAA Segmentation through level set method 1/13/2011 Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms 19 Defining static and evenly spaced mesh in the image using Mesh values are updated using the speed function 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

20. 1/13/2011 Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms 20 Elastin  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 to determine aortic diameter Environment Atherosclerosis Smoking Inflamation Environment Atherosclerosis Smoking Inflamation Genetic Factors Elastase activity increase vs. Inhibition decrease Elastin Destruction Failure of Elastin Increased load on collagen Failure of Collagen Aneurysmal Dilation Rupture Ageing Hypertension Collagen remodelling Alteration in vessel geometry Introduction Pathogenesis of an Abdominal Aortic Aneurysm

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 1/13/2011 21 Biological Basis of Medical Imaging - CT and MR Imaging for Abdominal Aortic Aneurysms MR Imaging of AAA Traditional MRI limitations on Vascular Imaging