Fully-automated deep learning-based flow quantification of 2D CINE phase contrast MRI.
| Publicado en: | European Radiology Vol. 33; no. 3; pp. 1707 - 1719 |
|---|---|
| Autores principales: | , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Mar2023
|
| 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=161963595&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 161963595 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Mar2023 vid: 33 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 161963595 159890974 10.1007/s00330-022-09179-3 161963595 ppf: 1707 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Fully-automated deep learning-based flow quantification of 2D CINE phase contrast MRI. aug: au: Pradella, Maurice Scott, Michael B. Omer, Muhammad Hill, Seth K. Lockhart, Lisette Yi, Xin Amir-Khalili, Alborz Sojoudi, Alireza Allen, Bradley D. Avery, Ryan Markl, Michael affil: Department of Radiology, Northwestern University, 737 N Michigan Ave, Suite 1600, 60611, Chicago, IL, USA sug: pubtype: Academic Journal doctype: Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
|---|