{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T23:14:37Z","timestamp":1768864477470,"version":"3.49.0"},"reference-count":26,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T00:00:00Z","timestamp":1768780800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Automated AI-based brain volumetry is increasingly used in clinical practice. T1-weighted sequences (e.g., MPRAGE) are considered the current state-of-the art. However, due to faster acquisition and higher in-plane resolution, 2D anisotropic sequences are often preferred in clinical routine. However, these sequences cannot be processed with currently available AI-volumetry software. Thus, we here aimed to evaluate volumetric data from synthetic MPRAGE-like sequences (mprAIge). Methods: We analyzed 412 datasets (206 conventional MPRAGE and 206 T2w\/FLAIR) from healthy volunteers (n = 36) and patients with multiple sclerosis (n = 140). Synthetic mprAIge was generated using SynthSR-CNN and assessed via assemblyNET on the volBrain platform. Total brain volume (TBV), gray and white matter volume (GMV\/WMV), and key substructures were compared between mprAIge and conventional MPRAGE. Average volume differences (AVDs) and correlations were calculated. Results: Synthetic mprAIge was generated successfully in all 206 cases. Quantitative analysis demonstrated strong correlation and high agreement for key substructures. TBV showed excellent agreement (AVD: 2.75% for controls, 3.90% for MS patients; r = 0.99 and 0.97, respectively). White matter volume exhibited excellent agreement (AVD: \u22121.92% for controls, 0.28% for MS patients; r = 0.95). Hippocampal volume also demonstrated good to excellent agreement (AVD: 1.13% for controls, \u22121.92% for MS patients; r = 0.91 and 0.89, respectively). Conclusions: Synthetic mprAIge enables AI-volumetry software application without limitations. Its volumetric assessments align well with conventional MPRAGE, opening new opportunities for volumetric post-processing and mapping of disease progression.<\/jats:p>","DOI":"10.3390\/diagnostics16020317","type":"journal-article","created":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T09:57:02Z","timestamp":1768816622000},"page":"317","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-Based Brain Volumetry Without MPRAGE? Evaluation of Synthetic T1-MPRAGE from 2D T2\/FLAIR"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-7817-4823","authenticated-orcid":false,"given":"Ludwig","family":"Singer","sequence":"first","affiliation":[{"name":"Institute of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-N\u00fcrnberg, 91054 Erlangen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tim Alexius","family":"M\u00f6hle","sequence":"additional","affiliation":[{"name":"Institute of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-N\u00fcrnberg, 91054 Erlangen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6795-5627","authenticated-orcid":false,"given":"Angelika","family":"Mennecke","sequence":"additional","affiliation":[{"name":"Institute of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-N\u00fcrnberg, 91054 Erlangen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6965-2117","authenticated-orcid":false,"given":"Konstantin","family":"Huhn","sequence":"additional","affiliation":[{"name":"Department of Neurology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-N\u00fcrnberg, 91054 Erlangen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Veit","family":"Rothhammer","sequence":"additional","affiliation":[{"name":"Department of Neurology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-N\u00fcrnberg, 91054 Erlangen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8560-2582","authenticated-orcid":false,"given":"Manuel Alexander","family":"Schmidt","sequence":"additional","affiliation":[{"name":"Institute of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-N\u00fcrnberg, 91054 Erlangen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arnd","family":"Doerfler","sequence":"additional","affiliation":[{"name":"Institute of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-N\u00fcrnberg, 91054 Erlangen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefan","family":"Lang","sequence":"additional","affiliation":[{"name":"Institute of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-N\u00fcrnberg, 91054 Erlangen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,19]]},"reference":[{"key":"ref_1","first-page":"e44658","article-title":"Reimagining Healthcare: Unleashing the Power of Artificial Intelligence in Medicine","volume":"15","author":"Iqbal","year":"2023","journal-title":"Cureus"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1016\/j.gie.2020.06.040","article-title":"History of artificial intelligence in medicine","volume":"92","author":"Kaul","year":"2020","journal-title":"Gastrointest. 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