1 © 2012 The MathWorks, Inc.
Numerical Computational Modeling
using Electrical Networks for Cerebral
Arteriovenous Malformation
By
Y.Kiran Kumar
Philips Electronics India Ltd.
Bangalore.
2
Agenda
Problem Statement
Introduction – AVM & Clinical Challenges
Methodology
Results
References
3
Problem Statement
The problem is to identify the blood vessel in an
AVM
Why it is important :
– Beneficial for the doctors to do improve in the therapy planning.
– A proper Segmentation of Vessels help for correct diagnosis
4 4
Introduction – AVM & Clinical Challenges
A cerebral Arteriovenous malformation (AVM) is an
abnormal connection between the arteries and the
veins in the brain.
An Arteriovenous malformation is a tangled cluster of
vessels, typically located in the supratentorial part of
the brain, in which arteries connect directly to veins
without any intervening capillary bed.
DSA - AVM
5 5
Introduction – AVM & Clinical Challenges
• A Nidus is the central part of AVM. It is made up of
abnormal blood vessels that are hybrids between
arteries and veins.
• Challenges:
Segmentation of Complex Structure
Clustering of Various Vessels
NIDUS Segmentation
FEEDING ARTERIES
DRAINING VEINS
NIDUS
6
Methodology
Acquisition of Datasets
Automatic Segmentation of image is performed
into various compartments as Arteries, Veins
at different levels [4] .
Design of the electrical circuit for each segmented
vessel of the compartment using R,L,C – Electrical
Networks [5-10]
Input transient voltage will be varied parameters based on the clinical
input measurements range for each compartment
4
7
Automation Segmentation Algorithm
OTSU Segmentation –
– Otsu's method is used to automatically perform histogram
shape-based image/ Global image threshold,
– Otsu's method is named after Nobuyuki Otsu
oo OTSU
OTSU
Input Data
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1
2
3
4
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2
Outputs
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Region Growing & Threshold Technique
Threshold based segmentation : Computation based on the appropriate threshold to use to
convert the grayscale image to binary.
Region Growing : A recursive region growing algorithm for 2D and 3D grayscale image sets with
polygon and binary mask output. The main purpose of this function lies on clean and highly
documented code.
Implementation difficulties:
– Data Loading and Processing require more steps to implement
in c/c++/c#
– Issues in bridging the Managed (UI) and UnManaged Code
(Algorithms)
Advantage of using Matlab : – Ease of Use
– Simple commands
– Execution is easier than other tools
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Region Growing & Threshold Technique
Results Input Data Output Segmentation
Input Data Output Segmentation
10
Level Set Segmentation
Implemented for 2-D interface (curve) evolution.
Used for implementing a 2-D curve evolution or a
diffusion of a 2-D function phi(x,y), e.g. anisotropic
diffusion on a gray-scale image.
11
Level Set Segmentation Results
Input Data Output Segmentation
12
References
Shiro Nagasawa, Masahiro Kawanishi, Susumu Kondoh, Sachiko Kajimoto,Kazunobu Yamaguchi, and Tomio
Ohta.Hemodynamic. Simulation Study of Cerebral Arteriovenous Malformations. Part 2. Effects of Impaired Auto
regulation and Induced Hypotension. Department of Neurosurgery, Osaka Medical College Takatsuki, Japan.
Journal of Cerebral Blood Flow and Metabolism.1996, 162-169.
Tarik F. Massoud, George J. Hademenos, William L. Young, Erzhen Gao, and John Pile-Spellman. Can
Induction of Systemic Hypotension Help Prevent Nidus Rupture complicating Arteriovenous Malformation
Embolization?: Analysis of Underlying Mechanisms Achieved Using a Theoretical Model. AJNR Journal of
NeuroRadiology August 2000.
Tarik F. Massoud, George J. Hademenos, Antonio A.F. De Salles, Timothy. Experimental Radio surgery
Simulations Using a Theoretical Model of Cerebral Arteriovenous Malformations. Editorial Comment. Stroke
2000, 2465-2477.
Martin Spiegel.Patient-Specific Cerebral Vessel Segmentation with Application in Hemodynamic Simulation.
Technical Report, University of Erlange, July 2011.
Hrvoje Bogunovi´c. Blood Flow analysis from Angiogram Image Sequence. Technical report, University of
Zagreb, Faculty of Electrical Engineering and Computing, 2005.
13
References
VuKMilisic, Alfio. Analysis of lumped parameter models for blood flow simulations and their relation with 1D
model. ESIAM: Mathematical Modeling and Numerical Analysis.Vol.38, 2004, 613-632.
Yubing Shi, Patricia Lawford and Rodney Hose. Review of Zero-D and 1-D Models of Blood Flow in the
Cardiovascular System. Medical Physics Group, Technical report, Department of Cardiovascular Science,
Faculty of Medicine, Dentistry and Health, University of Sheffield, Sheffield S10 2RX, UK.
Steinman DA, Taylor CA. Flow imaging and computing: large artery hemodynamics. Ann Biomed Eng, Vol 33,
2005, 1704-1709.
Burkhoff D, Alexander J Jr, Schipke J. Assessment of Windkessel as a model of aortic input impedance.
American Journal of Physiology, Vol 255, 1998, 742-753.
Burattini R, Natalucci. Complex and frequency-dependent compliance of viscoelastic windkessel resolves
contradictions in elastic windkessel. Med Eng Phys, Vol 20, 1998, 502-514.
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