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Auto-calibrating Spherical Deconvolution Based on ODF Sparsity
Thomas Schultz1 and Samuel Groeschel2
1University of Bonn, Germany
2Experimental Pediatric Neuroimaging and Department of Pediatric Neurology & Developmental Medicine, University Children’s Hospital Tübingen, Germany
Abstract. Spherical deconvolution models the diffusion MRI signal as the convolution of a fiber orientation density function (fODF) with a single fiber response. We propose a novel calibration procedure that automatically determines this fiber response. This has three advantages: First, the user no longer needs to provide an estimate of the response. Second, we estimate a per-voxel fiber response, which is more adequate for the analysis of patient data with focal white matter degeneration. Third, parameters of the estimated response reflect diffusion properties of the white matter tissue, and can be used for quantitative analysis.
Our method works by finding a tradeoff between a low fitting error and a sparse fODF. Results on simulated data demonstrate that auto-calibration successfully avoids erroneous fODF peaks that can occur with standard deconvolution, and that it resolves fiber crossings with better angular resolution than FORECAST, an alternative method. Parameter maps and tractography results corroborate applicability to clinical data.
LNCS 8149, p. 663 ff.
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© Springer-Verlag Berlin Heidelberg 2013