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Y.Kiran Kumar, Dr. Shashi Mehta, Dr. Manjunath Ramachandra
Philips Healthcare
July 10, 2014
Modelling of Cerebral Complex Vessels
– Cerebral ArterioVenous Malformation
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Confidential Philips Healthare, February 23, 2014
Agenda
• Problem Statement
• Introduction
• Clinical Challenges
• Methodology
• Results
• References
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Problem Statement
• The problem is to identify the blood vessel in an
Complex Structures in Brain like AVM Disease
• Why it is important :
– Beneficial for the doctors to do improve in the therapy planning.
– A proper Segmentation of Vessels and modeling of pressure
measurements help for correct diagnosis and treatment planning
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Introduction – AVM & Clinical Challenges
• A cerebral ArterioVenous malformation (CAVM) 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
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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
Modeling for complex feeding
arteries before Nidus and draining
vein after Nidus Complex Vessels
FEEDING ARTERIES
DRAINING VEINS
NIDUS
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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 – Lumped Parameter Modeling [5-10]
Input transient voltage will be varied parameters based on the
clinical input measurements range for each compartment
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Segmentation 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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Simulation
• Simulation of Electrical Networks using MATLAB – Simscape,
Simpower Systems. Components like resistors and capacitors are
imported to the model file from Simpower Systems.
• Signal generators, plots, voltage measurements etc., are from Simulink.
• To make the signals of Simpower Systems and Simulink to match
Converters are used.
• The network is created for the each 10% variation of the vessel
diameter and cascaded to form complete network.
• The outputs are measured for the Magnitude and Phase variation for
signal variations.
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Results
Equivalent
electrical
model for
the
simulated
blood vessel
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Results
Circuit showing an example of cascaded model of vessel
with varying diameter
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Node1
Input
voltage
Node 2Node 3
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Confidential Philips Healthare, February 23, 2014
Results - Node analysis
Input Voltage Node 1 Node2 Node3
1.3 1 0.88 0.03648
1.2 1.14 1.05 0.0338
0.9 0.78 0.7051 0.03048
0.85 0.74 0.668 0.0265
0.8 0.65 0.59 0.0228
0.75 0.57 0.51 0.021
0.7 0.682 0.5875 0.01815
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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.
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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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