Keywords: Diffusion Modeling, Modelling, Quantitative Imaging, Diffusion
Motivation: Estimating IVIM diffusion MRI (dMRI) parameters through non-linear fitting is challenging due to the inherent ill-posed mathematical problem, causing large uncertainty and poor reproducibility.
Goal(s): Understand IVIM-dMRI model properties and derive a more reliable metric than individual IVIM parameters.
Approach: Given a dMRI protocol, we investigated diffusion decay signal distributions for various IVIM parameters via numerical simulations. Employing dimension reduction, we defined a new parameter that captured the largest decay signal variation and is hence most robust against noise.
Results: A new metric was proposed to have the best achievable robustness for IVIM model parameter fitting.
Impact: We proposed a new metric that attains the best achievable robustness for IVIM model parameter fitting. This study addressed the large uncertainty and poor reproducibility issue in IVIM fitting.
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