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

arXiv: https://arxiv.org/abs/2211.14274 · HTML: https://mlsanity.com/paper/2211.14274/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2022.

## Built on by (most cited first, 3 of 3)

- [A Comprehensive Survey on Magnetic Resonance Image Reconstruction](https://mlsanity.com/paper/2503.07097/) (2025)
- [M-Gaussian: An Magnetic Gaussian Framework for Efficient Multi-Stack MRI Reconstruction](https://mlsanity.com/paper/2603.00145/) (2026)
- [Self-supervised Fetal MRI 3D Reconstruction Based on Radiation Diffusion Generation Model](https://mlsanity.com/paper/2310.10209/) (2023)

## Abstract (arXiv)

Tuning the regularization hyperparameter α in inverse problems has been a longstanding problem. This is particularly true in the case of fetal brain magnetic resonance imaging, where an isotropic high-resolution volume is reconstructed from motion-corrupted low-resolution series of two-dimensional thick slices. Indeed, the lack of ground truth images makes challenging the adaptation of α to a given setting of interest in a quantitative manner. In this work, we propose a simulation-based approach to tune α for a given acquisition setting. We focus on the influence of the magnetic field strength and availability of input low-resolution images on the ill-posedness of the problem. Our results show that the optimal α, chosen as the one maximizing the similarity with the simulated reference image, significantly improves the super-resolution reconstruction accuracy compared to the generally adopted default regularization values, independently of the selected pipeline. Qualitative validation on clinical data confirms the importance of tuning this parameter to the targeted clinical image setting.