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While high\u2010field Magnetic Resonance Imaging (HF\u2010MRI) is the standard in research and clinical settings, its relatively limited availability hinders widespread use. Low\u2010field MRI (LF\u2010MRI), particularly portable systems, offers a cost\u2010effective and accessible alternative. However, existing cortical surface analysis tools, such as FreeSurfer, are optimized for high\u2010resolution HF\u2010MRI and struggle with the lower signal\u2010to\u2010noise ratio (SNR) and resolution of LF\u2010MRI. In this work, we present a machine learning method for 3D reconstruction and analysis of portable LF\u2010MRI scans over a range of contrasts and resolutions. Our method works \u201cout of the box\u201d and does not require retraining. It leverages a 3D U\u2010Net trained on synthetic LF\u2010MRI 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\u2010\/LF\u2010MRI scans of the same 15 subjects and 50 subjects from the ULF\u2010EnC dataset. The results show that our method robustly recovers surfaces across LF\u2010MRI acquisitions, with accuracy depending on MRI contrast mechanism (T1 vs. T2), slice anisotropy (axial vs. isotropic), and resolution. A 3\u2009mm isotropic T2\u2010weighted scan acquired in under 4\u2009min, which is comparable in duration to typical HF\u2010MRI acquisitions, yields strong agreement with HF\u2010derived 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\u2010mm precision with ~3\u2009\u00d7\u20093\u2009\u00d7\u20093\u2009mm voxels. Our results also show that\n                    <jats:italic>recon\u2010any<\/jats:italic>\n                    performs robustly across other sequences and contrasts, though thickness estimates are particularly sensitive and degrade substantially with anisotropic or low\u2010resolution scans. We also validate our method on challenging postmortem LF\u2010MRI scans, further illustrating its robustness. Our method represents a significant step toward making cortical surface analysis feasible for portable LF\u2010MRI systems. The tool is publicly available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/surfer.nmr.mgh.harvard.edu\/fswiki\/ReconAny\">https:\/\/surfer.nmr.mgh.harvard.edu\/fswiki\/ReconAny<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1002\/hbm.70515","type":"journal-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T04:11:59Z","timestamp":1777435919000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["From Low Field to High Value: Robust Cortical Mapping From Low\u2010Field\n                    <scp>MRI<\/scp>"],"prefix":"10.1002","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1949-0711","authenticated-orcid":false,"given":"Karthik","family":"Gopinath","sequence":"first","affiliation":[{"name":"Massachusetts General Hospital Harvard Medical School  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Annabel","family":"Sorby\u2010Adams","sequence":"additional","affiliation":[{"name":"Massachusetts General Hospital Harvard Medical School  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan","family":"Williams\u2010Ramirez","sequence":"additional","affiliation":[{"name":"Massachusetts General Hospital Harvard Medical School  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dina","family":"Zemlyanker","sequence":"additional","affiliation":[{"name":"Massachusetts General Hospital Harvard Medical School  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jennifer","family":"Guo","sequence":"additional","affiliation":[{"name":"Massachusetts General Hospital Harvard Medical School  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Hunt","sequence":"additional","affiliation":[{"name":"University of Washington  Seattle Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christine L.","family":"Mac Donald","sequence":"additional","affiliation":[{"name":"University of Washington  Seattle Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"C. 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