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A U-net was trained to reconstruct the denoised and distortion corrected\n \n \n $$T_1$$<\/jats:tex-math>\n \n \n T<\/mml:mi>\n 1<\/mml:mn>\n <\/mml:msub>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n and\n \n \n $${T_2}^*$$<\/jats:tex-math>\n \n \n \n \n T<\/mml:mi>\n 2<\/mml:mn>\n <\/mml:msub>\n <\/mml:mrow>\n \u2217<\/mml:mo>\n <\/mml:msup>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n maps, and to additionally generate NAWM-, GM-, and WM lesion probability maps.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n WM lesions were predicted with a dice coefficient of\n \n \n $$0.61\\pm 0.09$$<\/jats:tex-math>\n \n \n 0.61<\/mml:mn>\n \u00b1<\/mml:mo>\n 0.09<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n and a lesion detection rate of\n \n \n $$0.85\\pm 0.25$$<\/jats:tex-math>\n \n \n 0.85<\/mml:mn>\n \u00b1<\/mml:mo>\n 0.25<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n for a threshold of 33%. The network jointly enabled accurate\n \n \n $$T_1$$<\/jats:tex-math>\n \n \n T<\/mml:mi>\n 1<\/mml:mn>\n <\/mml:msub>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n and\n \n \n $${T_2}^*$$<\/jats:tex-math>\n \n \n \n \n T<\/mml:mi>\n 2<\/mml:mn>\n <\/mml:msub>\n <\/mml:mrow>\n \u2217<\/mml:mo>\n <\/mml:msup>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n times with relative deviations of 5.2% and 5.1% and average dice coefficients of\n \n \n $$0.92\\pm 0.04$$<\/jats:tex-math>\n \n \n 0.92<\/mml:mn>\n \u00b1<\/mml:mo>\n 0.04<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n and\n \n \n $$0.91\\pm 0.03$$<\/jats:tex-math>\n \n \n 0.91<\/mml:mn>\n \u00b1<\/mml:mo>\n 0.03<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n for NAWM and GM after binarizing with a threshold of 80%.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n DL is a promising tool for the prediction of lesion probability maps in a fraction of time. These might be of clinical interest for the WM lesion analysis in MS patients.<\/jats:p>\n <\/jats:sec>","DOI":"10.1186\/s12880-021-00636-x","type":"journal-article","created":{"date-parts":[[2021,7,8]],"date-time":"2021-07-08T06:03:12Z","timestamp":1625724192000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Lesion probability mapping in MS patients using a regression network on MR fingerprinting"],"prefix":"10.1186","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6379-5299","authenticated-orcid":false,"given":"Ingo","family":"Hermann","sequence":"first","affiliation":[]},{"given":"Alena K.","family":"Golla","sequence":"additional","affiliation":[]},{"given":"Eloy","family":"Mart\u00ednez-Heras","sequence":"additional","affiliation":[]},{"given":"Ralf","family":"Schmidt","sequence":"additional","affiliation":[]},{"given":"Elisabeth","family":"Solana","sequence":"additional","affiliation":[]},{"given":"Sara","family":"Llufriu","sequence":"additional","affiliation":[]},{"given":"Achim","family":"Gass","sequence":"additional","affiliation":[]},{"given":"Lothar R.","family":"Schad","sequence":"additional","affiliation":[]},{"given":"Frank G.","family":"Z\u00f6llner","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,7,8]]},"reference":[{"issue":"6","key":"636_CR1","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1177\/1352458510391342","volume":"17","author":"Z Kincses","year":"2011","unstructured":"Kincses Z, Ropele S, Jenkinson M, Khalil M, Petrovic K, Loitfelder M, Langkammer C, Aspeck E, Wallner-Blazek M, Fuchs S, Jehna M, Schmidt R, V\u00e9csei L, Fazekas F, Enzinger C. 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