Meeting Banner
Abstract #2121

A Separable Approximation to DSI Deconvolution

Michael Paquette1, Maxime Descoteaux2

1SCIL, Universite de Sherbrooke, Sherbrooke, Quebec, Canada; 2SCIL, Universit de Sherbrooke, Sherbrooke, Quebec, Canada

The q-space truncation induce in the classical acquisition scheme of Diffusion Spectrum Imaging leads to a modified version of the diffusion propagator. That modification can be inverted by deconvolution methods. This work present a separable deconvolution approximation to get better propagator. Those better propagators leads to more physically meaningful diffusion space metric and sharper orientation distribution functions.

Abstract PDF Poster PDF

Keywords

accurate acquisition advanced allowing analytical angular anisotropy applicable applied approximate approximated approximating approximation arced artifacts axes axis backgrounds becoming best better binary bottom building bundle candidate central classical clear closer closest comes composed compute computed constrain convolution convolved correct crossings cubic dataset decays decomposition deconvolution diffusion discretized ensemble events expressed fiber field filter finally five fold fractional full function future generalized grid ground hence implement imposing inverse involved known largest lattice lead leading least look looking lying maps mask matrices matrix meaningless measured metrics model modified monotone monotonic much multiplication multiplying normalized numerical operator optimal outer physically practice problem processing product propagator propagators propose pseudo questions radially radius rank reconstruction regularized relationship repeat samples sampling scheme seem seems separable several share sharp simply simulated since singular solution space sphere square squared static straight structure studied symmetric system taking tensor terms theorem third transform true truncated truth underlying various visible young zero