Accelerating Diffusion Kurtosis Acquisition using SIR and Model-Based Reconstruction
Christopher Lee Welsh 1 , Edward W Hsu 1 , and Edward VR DiBella 2
Department of Bioengineering, University of
Utah, Salt Lake City, UT, United States,
UCAIR, University of Utah, Salt Lake City, UT, United
Diffusion kurtosis imaging (DKI) is a way to model
tissue microstructure that is more realistic than DTI
since it measures the degree of non-Gaussian diffusion.
However, DKI requires a long scan time. A model-based
strategy is presented to estimate diffusion and kurtosis
tensors directly from accelerated k-space data. The
accuracy of the model-based method with an acceleration
factor of 3 was compared to using all acquired data. The
findings suggest the proposed strategy can be used to
reduce DKI scan time if used in conjunction with SIR,
while still characterizing non-Gaussian diffusion and
neural fiber crossings without loss of accuracy.
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