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

1

1

2

3

4

3 4

2

Outputs

8

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

9

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.