Improved Diffusion Basis Functions Fitting and Metric Distance for Brain Axon Fiber Estimation

  • Ramón Aranda
  • Mariano Rivera
  • Alonso Ramírez-Manzanares
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7088)

Abstract

We present a new regularization approach for Diffusion Basis Functions fitting to estimate in vivo brain the axonal orientation from Diffusion Weighted Magnetic Resonance Images. That method assumes that the observed Magnetic Resonance signal at each voxel is a linear combination of a given diffusion basis functions; the aim of the approach is the estimation of the coefficients of the linear combination. An issue with the Diffusion Basis Functions method is the overestimation on the number of tensors (associated with different axon fibers) within a voxel due to noise, namely, the over fitting of the noisy signal. Our proposal overcomes such an overestimation problem. In additionally, we propose a metric to compare the performance of multi-fiber estimation algorithms. The metric is based on the Earth Mover’s Distance and allows us to compare in a single metric the orientation, size compartment and the number of axon bundles between two different estimations. The improvements of our two proposals is shown on synthetic and real experiments.

Keywords

Ground Truth Fractional Anisotropy Synthetic Data Gaussian Mixture Model Magnetic Resonance Signal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ramón Aranda
    • 1
  • Mariano Rivera
    • 1
  • Alonso Ramírez-Manzanares
    • 2
  1. 1.Centro De Investigación en MatemáticasGuanajuatoMéxico
  2. 2.Departamento de MatemáticasUniversidad de GuanajuatoGuanajuatoMéxico

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