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SIENA is state-of-the-art for BVL measurement, but limited by long computation time. Here we propose \u201cBrainLossNet\u201d, a convolutional neural network (CNN)-based method for BVL-estimation.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>BrainLossNet uses CNN-based non-linear registration of baseline(BL)\/follow-up(FU) 3D-T1w-MRI pairs. BVL is computed by non-linear registration of brain parenchyma masks segmented in the BL\/FU scans. The BVL estimate is corrected for image distortions using the apparent volume change of the total intracranial volume. BrainLossNet was trained on 1525 BL\/FU pairs from 83 scanners. Agreement between BrainLossNet and SIENA was assessed in 225 BL\/FU pairs from 94 MS patients acquired with a single scanner and 268 BL\/FU pairs from 52 scanners acquired for various indications. Robustness to short-term variability of 3D-T1w-MRI was compared in 354 BL\/FU pairs from a single healthy men acquired in the same session without repositioning with 116 scanners (Frequently-Traveling-Human-Phantom dataset, FTHP).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Processing time of BrainLossNet was 2\u20133\u00a0min. The median [interquartile range] of the SIENA-BrainLossNet BVL difference was 0.10% [\u2212\u00a00.18%, 0.35%] in the MS dataset, 0.08% [\u2212\u00a00.14%, 0.28%] in the various indications dataset. The distribution of apparent BVL in the FTHP dataset was narrower with BrainLossNet (<jats:italic>p<\/jats:italic>\u2009=\u20090.036; 95th percentile: 0.20% vs 0.32%).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>BrainLossNet on average provides the same BVL estimates as SIENA, but it is significantly more robust, probably due to its built-in distortion correction. Processing time of 2\u20133\u00a0min makes BrainLossNet suitable for clinical routine. This can pave the way for widespread clinical use of BVL estimation from intra-scanner BL\/FU pairs.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s11548-024-03201-3","type":"journal-article","created":{"date-parts":[[2024,6,16]],"date-time":"2024-06-16T09:01:44Z","timestamp":1718528504000},"page":"1763-1771","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["BrainLossNet: a fast, accurate and robust method to estimate brain volume loss from longitudinal MRI"],"prefix":"10.1007","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9911-5478","authenticated-orcid":false,"given":"Roland","family":"Opfer","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Julia","family":"Kr\u00fcger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Buddenkotte","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lothar","family":"Spies","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Finn","family":"Behrendt","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sven","family":"Schippling","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0945-0724","authenticated-orcid":false,"given":"Ralph","family":"Buchert","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,6,16]]},"reference":[{"key":"3201_CR1","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1136\/jnnp-2019-321652","volume":"91","author":"JT O'Brien","year":"2020","unstructured":"O\u2019Brien JT, Firbank MJ, Ritchie K, Wells K, Williams GB, Ritchie CW, Su L (2020) Association between midlife dementia risk factors and longitudinal brain atrophy: the PREVENT-Dementia study. 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