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However, acquisition of the OEF map using positron emission tomography (PET) with oxygen-15 gas is uncomfortable for patients because of the long fixation time, invasive arterial sampling, and radiation exposure. We aimed to predict the OEF map from magnetic resonance (MR) and PET images using a deep convolutional neural network (CNN) and to demonstrate which PET and MR images are optimal as inputs for the prediction of OEF maps.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>Cerebral blood flow at rest (CBF) and during stress (sCBF), cerebral blood volume (CBV) maps acquired from oxygen-15 PET, and routine MR images (T1-, T2-, and T2*-weighted images) for 113 patients with steno-occlusive disease were learned with U-Net. MR and PET images acquired from the other 25 patients were used as test data. We compared the predicted OEF maps and intraclass correlation (ICC) with the real OEF values among combinations of MRI, CBF, CBV, and sCBF.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Among the combinations of input images, OEF maps predicted by the model learned with MRI, CBF, CBV, and sCBF maps were the most similar to the real OEF maps (ICC: 0.597\u2009\u00b1\u20090.082). However, the contrast of predicted OEF maps was lower than that of real OEF maps.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>These results suggest that the deep CNN learned useful features from CBF, sCBF, CBV, and MR images and predict qualitatively realistic OEF maps. These findings suggest that the deep CNN model can shorten the fixation time for <jats:sup>15<\/jats:sup>O PET by skipping <jats:sup>15<\/jats:sup>O<jats:sub>2<\/jats:sub> scans. Further training with a larger data set is required to predict accurate OEF maps quantitatively.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s11548-021-02356-7","type":"journal-article","created":{"date-parts":[[2021,4,5]],"date-time":"2021-04-05T19:02:35Z","timestamp":1617649355000},"page":"1865-1874","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Prediction of an oxygen extraction fraction map by convolutional neural network: validation of input data among MR and PET images"],"prefix":"10.1007","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1823-1651","authenticated-orcid":false,"given":"Keisuke","family":"Matsubara","sequence":"first","affiliation":[]},{"given":"Masanobu","family":"Ibaraki","sequence":"additional","affiliation":[]},{"given":"Yuki","family":"Shinohara","sequence":"additional","affiliation":[]},{"given":"Noriyuki","family":"Takahashi","sequence":"additional","affiliation":[]},{"given":"Hideto","family":"Toyoshima","sequence":"additional","affiliation":[]},{"given":"Toshibumi","family":"Kinoshita","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,4,5]]},"reference":[{"key":"2356_CR1","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1093\/brain\/awf047","volume":"125","author":"CP Derdeyn","year":"2002","unstructured":"Derdeyn CP, Videen TO, Yundt KD, Fritsch SM, Carpenter DA, Grubb RL, Powers WJ (2002) Variability of cerebral blood volume and oxygen extraction: stages of cerebral haemodynamic impairment revisited. 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It was conducted in accordance with the Ethical Guideline for Clinical Research, issued by the Ministry of Health, Labor and Welfare, Japanese Government (2008).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Informed consent was waived for this study by the ethical committee of Akita Cerebrospinal and Cardiovascular Center.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Consent for publication was waived for this study by the ethical committee of Akita Cerebrospinal and Cardiovascular Center.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}