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Analytical Q-Ball Imaging with Optimal Regularization

Abstract : Diffusion MRI is a unique noninvasive imaging technique capable of quantifying and visualizing the angular distribution and the anisotropy of the white matter fibers. Several approaches such as diffusion tensor imaging, q-ball imaging (QBI), spherical deconvolution and many others high angular resolution diffusion imaging (HARDI) have been proposed to describe the angular distribution of the white matter fibers within a voxel. The analytical QBI technique [1] uses a predetermined regularization parameter [2] (λ = 0.006), which has been well adopted in many clinical studies. Although there are well-known strategies, e.g., the generalized cross-validation (GCV) [3-5] or the L-curve [6], for selecting the optimal regularization parameter λ, the predetermined regularization parameter was adopted for reasons related to practical and computational efficiency based on L-curve simulations [2]. Here, we incorporate the GCV technique into the analytical qball formalism. We compare and contrast the fixed λ-regularization parameter ("Fixed λ") and the automatic GCV-selected optimal λ-regularization ("GCV-based λ"), for estimating diffusion MRI data. We also discuss the potential consequences of our work on quantitative HARDI anisotropy measures and tractography studies.
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Submitted on : Tuesday, February 19, 2013 - 4:58:57 PM
Last modification on : Saturday, November 19, 2022 - 3:59:05 AM


  • HAL Id : hal-00789771, version 1


Maxime Descoteaux, Cheng Guan Koay, Peter J. Basser, Rachid Deriche. Analytical Q-Ball Imaging with Optimal Regularization. ISMRM 18th Scientific Meeting and Exhibition, 2010, Stockholm, Sweden. ⟨hal-00789771⟩



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