Numerical Computational Modeling using Electrical Networks for Cerebral Arteriovenous Malformation

Transcription

1 Numerical Computational Modeling using Electrical Networks for Cerebral Arteriovenous Malformation By Y.Kiran Kumar Philips Electronics India Ltd. Bangalore The MathWorks, Inc. 1

2 Agenda Problem Statement Introduction AVM & Clinical Challenges Methodology Results References 2

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 3

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 4 4

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 NIDUS Segmentation FEEDING ARTERIES DRAINING VEINS 5 5

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 6

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 3 4 Input Data Outputs 7

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 8

9 Region Growing & Threshold Technique Results Input Data Output Segmentation Input Data Output Segmentation 9

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. 10

11 Level Set Segmentation Results Input Data Output Segmentation 11

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, 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 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, Martin Spiegel.Patient-Specific Cerebral Vessel Segmentation with Application in Hemodynamic Simulation. Technical Report, University of Erlange, July Hrvoje Bogunovi c. Blood Flow analysis from Angiogram Image Sequence. Technical report, University of Zagreb, Faculty of Electrical Engineering and Computing,

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, 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, Burkhoff D, Alexander J Jr, Schipke J. Assessment of Windkessel as a model of aortic input impedance. American Journal of Physiology, Vol 255, 1998, Burattini R, Natalucci. Complex and frequency-dependent compliance of viscoelastic windkessel resolves contradictions in elastic windkessel. Med Eng Phys, Vol 20, 1998,