Analyzing multi-fiber reconstruction in high angular resolution diffusion imaging using the tensor distribution function

  • Authors:

  • Liang Zhan;Alex D. Leow;Siwei Zhu;Ming-Chang Chiang;Marina Barysheva;Arthur W. Toga;Katie L. McMahon;Greig I. de Zubicaray;Margaret J. Wright;Paul M. Thompson

  • Affiliations:

  • Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, UCLA, Los Angeles, CA;Laboratory of Neuro Imaging, Department of Neurology and Neuropsychiatric Institute, UCLA School of Medicine, Los Angeles, CA;Department of Mathematics, UCLA, Los Angeles, CA;Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, UCLA, Los Angeles, CA;Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, UCLA, Los Angeles, CA;Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, UCLA, Los Angeles, CA;Functional MRI Laboratory, Centre for Magnetic Resonance, University of Queensland, Brisbane, Australia;Functional MRI Laboratory, Centre for Magnetic Resonance, University of Queensland, Brisbane, Australia;Queensland Institute of Medical Research, Brisbane, Australia;Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, UCLA, Los Angeles, CA

  • Venue:
  • ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

High-angular resolution diffusion imaging (HARDI) can reconstruct fiber pathways in the brain with extraordinary detail, identifying anatomical features and connections not seen with conventional MRI. HARDI overcomes several limitations of standard diffusion tensor imaging, which fails to model diffusion correctly in regions where fibers cross or mix. As HARDI can accurately resolve sharp signal peaks in angular space where fibers cross, we studied how many gradients are required in practice to compute accurate orientation density functions, to better understand the tradeoff between longer scanning times and more angular precision. We computed orientation density functions analytically from tensor distribution functions (TDFs) which model the HARDI signal at each point as a unit-mass probability density on the 6D manifold of symmetric positive definite tensors. In simulated two-fiber systems with varying Rician noise, we assessed how many diffusion-sensitized gradients were sufficient to (1) accurately resolve the diffusion profile, and (2) measure the exponential isotropy (EI), a TDF-derived measure of fiber integrity that exploits the full multidirectional HARDI signal. At lower SNR, the reconstruction accuracy, measured using the Kullback-Leibler divergence, rapidly increased with additional gradients, and EI estimation accuracy plateaued at around 70 gradients.