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Abstract #4353

Practical correction of gradient nonlinearity bias for mean diffusion kurtosis model parameters  

Dariya Malyarenko1 and Thomas L Chenevert1
1University of Michigan, Ann Arbor, MI, United States

Quantitative tissue diffusion parameters derived from diffusion weighted imaging (DWI) models hold promise for diagnostic and prognostic clinical oncology applications. System-dependent spatial DW bias due to gradient nonlinearity (GNL) is known confounding factor for quantitative DWI metrics. Improved accuracy and multiplatform reproducibility was previously demonstrated for mono-exponential apparent diffusion coefficient with correction for platform-dependent GNL bias (GNC). Complex tumor microenvironment often exhibits multi-exponential diffusion described by isotropic kurtosis model. This study proposes analytical extension and demonstrates empirical confirmation for GNC of parametric maps derived from diffusion kurtosis model.

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