From Low Field to High Value: Robust Cortical Mapping From Low‐Field MRI

ABSTRACT Three‐dimensional reconstruction of cortical surfaces from MRI for subsequent morphometric analysis is fundamental for understanding brain structure. While high‐field Magnetic Resonance Imaging (HF‐MRI) is the standard in research and clinical settings, its relatively limited availability hinders widespread use. Low‐field MRI (LF‐MRI), particularly portable systems, offers a cost‐effective and accessible alternative. However, existing cortical surface analysis tools, such as FreeSurfer, are optimized for high‐resolution HF‐MRI and struggle with the lower signal‐to‐noise ratio (SNR) and resolution of LF‐MRI. In this work, we present a machine learning method for 3D reconstruction and analysis of portable LF‐MRI scans over a range of contrasts and resolutions. Our method works “out of the box” and does not require retraining. It leverages a 3D U‐Net trained on synthetic LF‐MRI data to predict signed distance functions of the cortical surfaces, followed by geometric processing to ensure topologically accurate reconstructions. We evaluate our approach using paired HF‐/LF‐MRI scans of the same 15 subjects and 50 subjects from the ULF‐EnC dataset. The results show that our method robustly recovers surfaces across LF‐MRI acquisitions, with accuracy depending on MRI contrast mechanism (T1 vs. T2), slice anisotropy (axial vs. isotropic), and resolution. A 3 mm isotropic T2‐weighted scan acquired in under 4 min, which is comparable in duration to typical HF‐MRI acquisitions, yields strong agreement with HF‐derived surfaces: surface area correlates at , cortical parcellations reach a Dice coefficient of , and gray matter volume achieves . Cortical thickness remains more challenging but achieves correlations up to , reflecting the difficulties of achieving sub‐mm precision with ~3 × 3 × 3 mm voxels. Our results also show that recon‐any performs robustly across other sequences and contrasts, though thickness estimates are particularly sensitive and degrade substantially with anisotropic or low‐resolution scans. We also validate our method on challenging postmortem LF‐MRI scans, further illustrating its robustness. Our method represents a significant step toward making cortical surface analysis feasible for portable LF‐MRI systems. The tool is publicly available at https://surfer.nmr.mgh.harvard.edu/fswiki/ReconAny .