Assessing diffusion kurtosis tensor estimation methods using a digital brain phantom derived from human connectome project data

作者：Olson Daniel V^*; Arpinar Volkan E; Muftuler L Tugan

来源：Magnetic Resonance Imaging, 2018, 48: 122-128.

DOI：10.1016/j.mri.2017.12.026

• Diffusion-weighted MRI
• Diffusion kurtosis imaging
• Diffusion kurtosis tensor reconstruction

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.

• 出版日期2018-5