Modelling of Cerebral Complex Vessels Cerebral ...€¦ · Y.Kiran Kumar, Dr. Shashi Mehta, Dr....

Y.Kiran Kumar, Dr. Shashi Mehta, Dr. Manjunath Ramachandra

Philips Healthcare

July 10, 2014

Modelling of Cerebral Complex Vessels

– Cerebral ArterioVenous Malformation

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

4

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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

7


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

10

Node1

Input

voltage

Node 2Node 3


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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Modelling of Cerebral Complex Vessels Cerebral ...€¦ · Y.Kiran Kumar, Dr. Shashi Mehta, Dr....

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