From Low Field to High Value: Robust Cortical Mapping From Low-Field MRI.

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