Simulation-based parameter optimization for fetal brain MRI super-resolution reconstruction

In utero fetal brain magnetic resonance images are inherently limited in spatial resolution due to stochastic motion of the fetus. Super-resolution reconstruction methods have become the go-to approach to compute an isotropic motion-free volume of the fetal brain from low-resolution series of 2D thick slices. Such pipelines often rely on an optimization problem with a data fidelity and a regularization term, balanced by a hyperparameter $\alpha$. The lack of ground truth images makes it difficult to adapt $\alpha$ to a given setting of interest in a quantitative manner. In this work, we propose a simulation-based approach to tune $\alpha$ for a given acquisition setting. We focus on two key aspects: the magnetic field strength (1.5T and 3T) and number of LR series used for reconstruction. Our results show that the optimal $\alpha$ significantly improves the performance compared to the default value, across two commonly used SR pipelines. Qualitative validation on clinical data confirms the importance of tuning this parameter to the setting of interest.