{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T09:21:01Z","timestamp":1770456061290,"version":"3.49.0"},"reference-count":0,"publisher":"Society for Imaging Science & Technology","issue":"15","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ei"],"DOI":"10.2352\/issn.2470-1173.2021.15.coimg-290","type":"journal-article","created":{"date-parts":[[2021,1,23]],"date-time":"2021-01-23T01:29:09Z","timestamp":1611365349000},"page":"290-1-290-7","source":"Crossref","is-referenced-by-count":8,"title":["Deep Learning Approach for Dynamic Sparse Sampling for High-Throughput Mass Spectrometry Imaging"],"prefix":"10.2352","volume":"33","author":[{"given":"David","family":"Helminiak","sequence":"first","affiliation":[]},{"given":"Hang","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Julia","family":"Laskin","sequence":"additional","affiliation":[]},{"given":"Dong","family":"Hye Ye","sequence":"additional","affiliation":[]}],"member":"1209","published-online":{"date-parts":[[2021,1,18]]},"container-title":["Electronic Imaging"],"original-title":[],"link":[{"URL":"https:\/\/library.imaging.org\/ei\/articles\/33\/15\/art00007","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,7]],"date-time":"2022-03-07T12:33:21Z","timestamp":1646656401000},"score":1,"resource":{"primary":{"URL":"https:\/\/library.imaging.org\/ei\/articles\/33\/15\/art00007"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,18]]},"references-count":0,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2021,1,18]]}},"URL":"https:\/\/doi.org\/10.2352\/issn.2470-1173.2021.15.coimg-290","relation":{},"ISSN":["2470-1173"],"issn-type":[{"value":"2470-1173","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,18]]}}}