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However, normal apparent diffusion coefficient (ADC) maps change rapidly in early childhood, making abnormality detection difficult. In this article, we explored clinical PACS and electronic healthcare records (EHR) to create age\u2010specific ADC atlases for clinical radiology reference. Using the EHR and three rounds of multiexpert reviews, we found ADC maps from 201 children 0\u20136 years of age scanned between 2006 and 2013 who had brain MRIs with no reported abnormalities and normal clinical evaluations 2+ years later. These images were grouped in 10 age bins, densely sampling the first 1 year of life (5 bins, including neonates and 4 quarters) and representing the 1\u20136 year age range (an age bin per year). Unbiased group\u2010wise registration was used to construct ADC atlases for 10 age bins. We used the atlases to quantify (a) cross\u2010sectional normative ADC variations; (b) spatiotemporal heterogeneous ADC changes; and (c) spatiotemporal heterogeneous volumetric changes. The quantified age\u2010specific whole\u2010brain and region\u2010wise ADC values were compared to those from age\u2010matched individual subjects in our study and in multiple existing independent studies. The significance of this study is that we have shown that clinically acquired images can be used to construct normative age\u2010specific atlases. These first of their kind age\u2010specific normative ADC atlases quantitatively characterize changes of myelination\u2010related water diffusion in the first 6 years of life. The quantified voxel\u2010wise spatiotemporal ADC variations provide standard references to assist radiologists toward more objective interpretation of abnormalities in clinical images. Our atlases are available at<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/www.nitrc.org\/projects\/mgh_adcatlases\">https:\/\/www.nitrc.org\/projects\/mgh_adcatlases<\/jats:ext-link>.<jats:italic>Hum Brain Mapp 38:3052\u20133068, 2017<\/jats:italic>. \u00a9<jats:bold>2017 Wiley Periodicals, Inc.<\/jats:bold><\/jats:p>","DOI":"10.1002\/hbm.23573","type":"journal-article","created":{"date-parts":[[2017,3,31]],"date-time":"2017-03-31T16:38:28Z","timestamp":1490978308000},"page":"3052-3068","source":"Crossref","is-referenced-by-count":37,"title":["Using clinically acquired MRI to construct age\u2010specific ADC atlases: Quantifying spatiotemporal ADC changes from birth to 6\u2010year old"],"prefix":"10.1002","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7726-6208","authenticated-orcid":false,"given":"Yangming","family":"Ou","sequence":"first","affiliation":[{"name":"Psychiatric Neuroimaging, Department of Psychiatry Massachusetts General Hospital, Harvard Medical School Charlestown Massachusetts"},{"name":"Laboratory for Computational Neuroimaging Athinoula A. 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