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Each subject underwent 10 runs of a multi\u2010band multi\u2010slice MRF\u2010ASL sequence for a total scan time of approximately 40 min. MRF\u2010ASL images were averaged across runs to yield a set of high\u2010fidelity data. Training of a fully connected artificial neural network (ANN) was then performed using these data. The results from ANN were compared to those of dictionary matching (DM), ANN trained with single\u2010run experimental data and with simulation data. Initial clinical performance of the technique was also demonstrated in a Moyamoya patient.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The use of ANN reduced the processing time of MRF\u2010ASL data to 3.6 s, compared to DM of 3 h 12 min. Parametric values obtained with ANN and DM were strongly correlated (R<jats:sup>2<\/jats:sup> between 0.84 and 0.96). Results obtained from high\u2010fidelity ANN were substantially more reliable compared to those from DM or single\u2010run ANN. Voxel\u2010wise coefficient of variation (CoV) of high\u2010fidelity ANN, DM, and single\u2010run ANN was 0.15 \u00b1 0.08, 0.41 \u00b1 0.20, 0.30 \u00b1 0.16, respectively, for cerebral blood flow and 0.11 \u00b1 0.06, 0.20 \u00b1 0.19, 0.15 \u00b1 0.10, respectively, for bolus arrival time. In vivo data trained ANN also outperformed ANN trained with simulation data. The superior performance afforded by ANN allowed more conspicuous depiction of hemodynamic abnormalities in Moyamoya patient.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Deep\u2010learning\u2010based parametric reconstruction improves the reliability of MRF\u2010ASL hemodynamic maps and reduces processing time.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mrm.28560","type":"journal-article","created":{"date-parts":[[2020,10,27]],"date-time":"2020-10-27T06:52:40Z","timestamp":1603781560000},"page":"1974-1985","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Multi\u2010band MR fingerprinting (MRF) ASL imaging using artificial\u2010neural\u2010network trained with high\u2010fidelity experimental data"],"prefix":"10.1002","volume":"85","author":[{"given":"Hongli","family":"Fan","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering Johns Hopkins School of Medicine Baltimore Maryland USA"},{"name":"The Russell H. 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