Non-linear Model Fitting to Parameterize Diseased Blood Vessels

Alexandra La Cruz, Matús Straka, Arnold Köchl, Milos Srámek, Meister Eduard Gröller, Dominik Fleischmann
Non-linear Model Fitting to Parameterize Diseased Blood Vessels
TR-186-2-04-05, April 2004 [ paper]
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Abstract

Accurate estimation of vessel parameters is a prerequisite for automated visualization and analysis of normal and diseased blood vessels. The objective of this research is to estimate the dimensions of lower extremity arteries, imaged by computed tomography (CT). The vessel is modeled using an elliptical or cylindrical structure with specific dimensions, orientation and blood vessel mean density. The model separates two homogeneous regions: Its inner side represents a region of density for vessels, and its outer side a region for background. Taking into account the point spread function (PSF) of a CT scanner, a function is modeled with a Gaussian kernel, in order to smooth the vessel boundary in the model. A new strategy for vessel parameter estimation is presented. It stems from vessel model and model parameter optimization by a nonlinear optimization procedure (the Levenberg-Marquardt technique). The method provides center location, diameter and orientation of the vessel as well as blood and background mean density values. The method is tested on synthetic data and real patient data with encouraging results.

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@techreport{LaCruz-2004-NMF,
  title =      "Non-linear Model Fitting to Parameterize Diseased Blood
               Vessels",
  author =     "Alexandra La Cruz and Mat{\'u}s Straka and Arnold
               K{\"o}chl and Milos Sr{\'a}mek and Meister Eduard
               Gr{\"o}ller and Dominik Fleischmann",
  year =       "2004",
  abstract =   "Accurate estimation of vessel parameters is a prerequisite
               for automated visualization and analysis of normal and
               diseased blood vessels. The objective of this research is to
               estimate the dimensions of lower extremity arteries, imaged
               by computed tomography (CT). The vessel is modeled using an
               elliptical or cylindrical structure with specific
               dimensions, orientation and blood vessel mean density. The
               model separates two homogeneous regions: Its inner side
               represents a region of density for vessels, and its outer
               side a region for background. Taking into account the point
               spread function (PSF) of a CT scanner, a function is modeled
               with a Gaussian kernel, in order to smooth the vessel
               boundary in the model. A new strategy for vessel parameter
               estimation is presented. It stems from vessel model and
               model parameter optimization by a nonlinear optimization
               procedure (the Levenberg-Marquardt technique). The method
               provides center location, diameter and orientation of the
               vessel as well as blood and background mean density values.
               The method is tested on synthetic data and real patient data
               with encouraging results.",
  address =    "Favoritenstrasse 9-11/186, A-1040 Vienna, Austria",
  institution = "Institute of Computer Graphics and Algorithms, Vienna
               University of Technology",
  note =       "human contact: technical-report@cg.tuwien.ac.at",
  month =      apr,
  number =     "TR-186-2-04-05",
  keywords =   "Diseased Blood Vessel Detection, Segmentation, Visualization",
  URL =        "http://www.cg.tuwien.ac.at/research/publications/2004/LaCruz-2004-NMF/",
}