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Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004

Volume 3217 of the series Lecture Notes in Computer Science pp 663-670

Estimation of Anatomical Connectivity by Anisotropic Front Propagation and Diffusion Tensor Imaging

  • Marcel JackowskiAffiliated withLancaster UniversityDept. of Diagnostic Radiology, Yale School of Medicine
  • , Chiu Yen KaoAffiliated withCarnegie Mellon UniversityDept. of Mathematics, University of California Los Angeles
  • , Maolin QiuAffiliated withLancaster UniversityDept. of Diagnostic Radiology, Yale School of Medicine
  • , R. Todd ConstableAffiliated withCarnegie Mellon UniversityDept. of Diagnostic Radiology and Biomedical Engineering, Yale School of Medicine
  • , Lawrence H. StaibAffiliated withCarnegie Mellon UniversityDept. of Diagnostic Radiology and Biomedical Engineering, Yale School of Medicine

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Abstract

Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) allows one to capture the restricted diffusion of water molecules in fibrous tissues which can be used to infer their structural organization. In this paper, we propose a novel wavefront propagation method for estimating the connectivity in the white matter of the brain using DT-MRI. First, an anisotropic version of the static Hamilton-Jacobi equation is solved by a sweeping method in order to obtain accurate front arrival times and determine connectivity. Our wavefront then propagates using the diffusion tensor rather than its principal eigenvector, which is prone to misclassification in oblate tensor regions. Furthermore, we show that our method is robust to noise and can estimate connectivity pathways across regions where singularities, such as fiber crossings, are present. Preliminary connectivity results on synthetic data and on a normal human brain are illustrated and discussed.