Learning-based MRI response predictions from OCT microvascular models to replace simulation-based frameworks

Computational quantification of magnetic resonance imaging (MRI) response from neurovascular structures is used to investigate potential biomarkers for different types of cerebrovascular deteriorations at the microscopic scale. Simulation-based MRI requires fully resolved microvascular structures, with geometric and physiological parameters, from tissue volumes captured using microscopic imaging modalities, e.g., optical coherence tomography (OCT). The preparation of such input models hinders large cohort studies and requires extensive manual effort. Here, we propose using 3D neural networks as an alternative learning-based solution over MRI simulation schemes. We trained state-of-the-art 3D neural networks to predict the spin echo (SE) MRI response from OCT microvascular volumes. By validating against simulated signals, our result demonstrates that the 3D ResNet-based regression network achieves a high accuracy to predict MRI signals with an average mean square error (MSE) <1%, R2 of 82.8% and explained variance score of 82.9%.