Placental Blood-Flow Velocity Quantification From Diffusion MRI

Altered placental capillary blood flow is closely linked to obstetric complications, yet quantifying capillary-scale blood velocity remains challenging with existing imaging methods. This is partially because capillary networks form disordered microvascular beds at the voxel scale, rather than coherent, directional vessels. Here, we combine Monte Carlo (MC) simulations with machine learning to estimate placental capillary blood velocity directly from diffusion MRI (dMRI) data.

MC simulations incorporating perfusion and diffusion were performed to generate signal dictionaries for supervised machine learning regressors (random forest [RF] and multilayer perceptron [MLP]) to estimate velocity, perfusion fraction and diffusivity. The trained regressors were applied to simulated and in vivo dMRI data, with intravoxel incoherent motion (IVIM)-based estimates as baselines for comparison.

Our approach outperformed IVIM-based methods on simulated test data in recovering ground-truth parameters. Among the evaluated models, the MC-based MLP approach produced physiologically reasonable estimates of velocity in in vivo placentas, in agreement with previously reported measures and exhibited greater sensitivity than IVIM-generated methods.

This study presents an approach to characterize capillary perfusion that alternates conventional IVIM models, particularly in organs such as the placenta, where pseudo-diffusion coefficient can be similar to tissue diffusion coefficient, limiting reliable IVIM parameter separation. Our method could enable detection of capillary blood-flow alterations, which could help identify pathological placental blood-flow conditions and potentially diagnosis relevant diseases, such as preeclampsia and fetal growth restriction. More broadly, the approach may translate to other organs where microscale blood-flow changes are key indicators of underlying pathologies.