Image quality and lesion detectability of deep learning-accelerated T2-weighted Dixon imaging of the cervical spine.
| Publicado en: | Skeletal Radiology Vol. 52; no. 12; pp. 2451 - 2460 |
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| Autores principales: | , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Dec2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=173034393&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 173034393 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03642348 O14 jtl: Skeletal Radiology issn: 03642348 maglogo: N pubinfo: dt: Dec2023 vid: 52 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 173034393 163908863 10.1007/s00256-023-04364-x 173034393 ppf: 2451 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Image quality and lesion detectability of deep learning-accelerated T2-weighted Dixon imaging of the cervical spine. aug: au: Seo, Geojeong Lee, Sun Joo Park, Dae Hyun Paeng, Sung Hwa Koerzdoerfer, Gregor Nickel, Marcel Dominik Sung, Jaekon affil: https://ror.org/04xqwq985 Department of Radiology, Busan Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea sug: pubtype: Academic Journal doctype: Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
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