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Cited by 344

The DOI matches its Crossref record; 145 of the 145 references it cites were found in a registry or an index.

Perfusion MRI: The Five Most Frequently Asked Clinical Questions

Open access
Essig, Marco, Nguyen, Thanh Binh, Shiroishi, Mark S., Saake, Marc, Provenzale, James M., Enterline, David S., Anzalone, Nicoletta, Dörfler, Arnd, Rovira, Àlex, Wintermark, Max, Law, Meng·2013·American Journal of Roentgenology

Abstract

OBJECTIVE: This article addresses questions that radiologists frequently ask when planning, performing, processing, and interpreting MRI perfusion studies in CNS imaging. CONCLUSION: Perfusion MRI is a promising tool in assessing stroke, brain tumors, and neurodegenerative diseases. Most of the impediments that have limited the use of per-fusion MRI can be overcome to allow integration of these methods into modern neuroimaging protocols.

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Registry

Crossref · 10.2214/ajr.12.9544

Cites

145 of 145 references verified0 citation errors · 0 not checkable

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Citations

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Metadata

Authors
Essig, M., Nguyen, T. B., Shiroishi, M. S., Saake, M., Provenzale, J. M., Enterline, D. S., Anzalone, N., Dörfler, A., Rovira, À., Wintermark, M., & Law, M.
Venue
American Journal of Roentgenology
Published
2013-09
Volume
201
Issue
3
Pages
W495-W510
ISSN
  • 0002-9580 (ISSN-L)
  • 0361-803X
  • 1546-3141
OpenAlex
W2099242902
Cited by
344
References
145
FWCI
23.77
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Perfusion MRI: The Five Most Frequently Asked Clinical Questions. https://app.cochat.ai/papers/10.2214/ajr.12.9544
The paper
Essig, M., Nguyen, T. B., Shiroishi, M. S., Saake, M., Provenzale, J. M., Enterline, D. S., Anzalone, N., Dörfler, A., Rovira, À., Wintermark, M., & Law, M. (2013). Perfusion MRI: The Five Most Frequently Asked Clinical Questions. American Journal of Roentgenology. https://doi.org/10.2214/ajr.12.9544

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.