Assessing diffusion kurtosis tensor estimation methods using a digital brain phantom derived from human connectome project data. Magn Reson Imaging 2018 May;48:122-128
Date
01/07/2018Pubmed ID
29305126DOI
10.1016/j.mri.2017.12.026Scopus ID
2-s2.0-85044356410 (requires institutional sign-in at Scopus site) 2 CitationsAbstract
PURPOSE: Diffusion kurtosis imaging (DKI) has gained popularity in recent years as an advanced diffusion-weighted MRI technique. This work aims to quantitatively compare the performance and accuracy of four DKI processing algorithms. For this purpose, a digital DKI brain phantom is developed.
METHODS: Data from the Human Connectome Project database were used to generate a DKI digital phantom. In a Monte Carlo Rician noise simulation, four DKI processing algorithms were compared based on their mean squared error, squared bias, and variance.
RESULTS: Algorithm performance was region-dependent and differed for each diffusion metric and noise level. Crossover between variance and squared bias error occurred between signal-to-noise ratios of 30 and 40.
CONCLUSION: Through the framework presented here, DKI algorithms can be quantitatively compared via a ground truth data set. Error maps are critical as algorithm performance varies spatially. Bias-plus-variance decomposition provides a more complete picture than MSE alone. In combination with refinements in acquisition in future studies, the accuracy and efficiency of DKI will continue to improve promoting clinical adoption.
Author List
Olson DV, Arpinar VE, Muftuler LTAuthor
Lutfi Tugan Muftuler PhD Professor in the Neurosurgery department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
AlgorithmsBrain
Connectome
Databases, Factual
Diffusion Magnetic Resonance Imaging
Diffusion Tensor Imaging
Humans
Male
Monte Carlo Method
Phantoms, Imaging
Reproducibility of Results
Signal-To-Noise Ratio
