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

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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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  • 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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  • Confidential Philips Healthare, February 23, 2014

    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

    3

  • Confidential Philips Healthare, February 23, 2014

    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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  • Confidential Philips Healthare, February 23, 2014

    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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  • Confidential Philips Healthare, February 23, 2014

    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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  • Confidential Philips Healthare, February 23, 2014

    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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  • Confidential Philips Healthare, February 23, 2014

    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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  • Confidential Philips Healthare, February 23, 2014

    Results

    Equivalent

    electrical

    model for

    the

    simulated

    blood vessel

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  • Confidential Philips Healthare, February 23, 2014

    Results

    Circuit showing an example of cascaded model of vessel

    with varying diameter

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    Node1

    Input

    voltage

    Node 2Node 3

  • 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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  • Confidential Philips Healthare, February 23, 2014

    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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  • Confidential Philips Healthare, February 23, 2014

    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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