Effects of Compressed Sensing Reconstruction on Kurtosis Tensor Fitting in Diffusion Spectrum Imaging

Keywords

acceleration accuracy acquisition affected although analytically angular anisotropy apparent applied approaches artificially available bias biased bottom brain channel clearly clinical clinically coding coil complex complicated compressed computation computing constrained context cube definiteness derived deviation deviations diffusion diffusional diffusivity directly done eigenvalues elements enabling enclosed encoding ensure equipped errors estimation exemplary expected experiment fast features feedback fiber fibers finally fitted fitting fully generation global gradients grid ground hand hardy head healthy hence illustrated imaginary included incorporated influence initial instances investigation iterative kelvin king kurtosis least like likelihood linear logarithm long magnitudes marker math matrix measures metrics mixture model needed neurology noise operator optionally overestimation particular pattern patterns positive programming properties pseudo random randomly real realistically reciprocal reconstructed reconstruction reduce reduced reflect replace robust sampled samples scalar sclerosis sensing shrinkage significantly simulation slice solution space spectrum squares step strengths subsequently suggest tensor tensors theory thereby transform transformed traumatic truth variation volunteers yielding yields