4.5 Article

Optimal Model Mapping for Intravoxel Incoherent Motion MRI

Journal

FRONTIERS IN HUMAN NEUROSCIENCE
Volume 15, Issue -, Pages -

Publisher

FRONTIERS MEDIA SA
DOI: 10.3389/fnhum.2021.617152

Keywords

IVIM-MRI; perfusion; diffusion; modeling; gaussian; kurtosis; gamma; AIC

Funding

  1. Management Expenses Grants of Human Brain Research Center, Kyoto University Graduate School of Medicine

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The study proposed an optimal model mapping method to improve the reliability of perfusion parameter estimation in IVIM study, finding Gaussian, Kurtosis, and Gamma models optimal for different brain tissues. Using the optimal model mapping method resulted in more reliable estimations of perfusion fraction and pseudo diffusion coefficient compared to conventional methods, potentially providing additional information for clinical diagnosis.
In general, only one diffusion model would be applied to whole field-of-view voxels in the intravoxel incoherent motion-magnetic resonance imaging (IVIM-MRI) study. However, the choice of the applied diffusion model can significantly influence the estimated diffusion parameters. The quality of the diffusion analysis can influence the reliability of the perfusion analysis. This study proposed an optimal model mapping method to improve the reliability of the perfusion parameter estimation in the IVIM study. Six healthy volunteers (five males and one female; average age of 38.3 +/- 7.5 years). Volunteers were examined using a 3.0 Tesla scanner. IVIM-MRI of the brain was applied at 17 b-values ranging from 0 to 2,500 s/mm(2). The Gaussian model, the Kurtosis model, and the Gamma model were found to be optimal for the CSF, white matter (WM), and gray matter (GM), respectively. In the mean perfusion fraction (f(p)) analysis, the GM/WM ratios were 1.16 (Gaussian model), 1.80 (Kurtosis model), 1.94 (Gamma model), and 1.54 (Optimal model mapping); in the mean pseudo diffusion coefficient (D*) analysis, the GM/WM ratios were 1.18 (Gaussian model), 1.19 (Kurtosis model), 1.56 (Gamma model), and 1.24 (Optimal model mapping). With the optimal model mapping method, the estimated f(p) and D* were reliable compared with the conventional methods. In addition, the optimal model maps, the associated products of this method, may provide additional information for clinical diagnosis.

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