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Intravoxel incoherent motion (IVIM) imaging allows contrast-agent free invivo perfusion quantification with magnetic resonance imaging (MRI). However,its use is limited by typically low accuracy due to low signal-to-noise ratio(SNR) at large gradient encoding magnitudes as well as dephasing artefactscaused by subject motion, which is particularly challenging in fetal MRI. Tomitigate this problem, we propose an implicit IVIM signal acquisition modelwith which we learn full posterior distribution of perfusion parameters usingartificial neural networks. This posterior then encapsulates the uncertainty ofthe inferred parameter estimates, which we validate herein via numericalexperiments with rejection-based Bayesian sampling. Compared tostate-of-the-art IVIM estimation method of segmented least-squares fitting, ourproposed approach improves parameter estimation accuracy by 65% on syntheticanisotropic perfusion data. On paired rescans of in vivo fetal MRI, our methodincreases repeatability of parameter estimation in placenta by 46%.

Implicit Modeling with Uncertainty Estimation for Intravoxel Incoherent Motion Imaging