2013 | OriginalPaper | Buchkapitel
Diffusion Propagator Estimation from Sparse Measurements in a Tractography Framework
verfasst von : Yogesh Rathi, Borjan Gagoski, Kawin Setsompop, Oleg Michailovich, P. Ellen Grant, Carl-Fredrik Westin
Erschienen in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013
Verlag: Springer Berlin Heidelberg
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Estimation of the diffusion propagator from a sparse set of diffusion MRI (dMRI) measurements is a field of active research. Sparse reconstruction methods propose to reduce scan time and are particularly suitable for scanning un-coperative patients. Recent work on reconstructing the diffusion signal from very few measurements using compressed sensing based techniques has focussed on propagator (or signal) estimation at each voxel independently. However, the goal of many neuroscience studies is to use tractography to study the pathology in white matter fiber tracts. Thus, in this work, we propose a joint framework for robust estimation of the diffusion propagator from
sparse measurements
while simultaneously tracing the white matter tracts. We propose to use a novel multi-tensor model of diffusion which incorporates the bi-exponential radial decay of the signal. Our preliminary results on in-vivo data show that the proposed method produces consistent and reliable fiber tracts from very few gradient directions while simultaneously estimating the bi-exponential decay of the diffusion propagator.