Diagnostic Performance of Fully Automated Pixel-Wise Quantitative Myocardial Perfusion Imaging by Cardiovascular Magnetic Resonance | Zendy

LiYueh Hsu | Zendy; Matthew Jacobs | Zendy; Mitchel Benovoy | Zendy; Allison Ta | Zendy; Hannah Conn | Zendy; Susanne Winkler | Zendy; Anders M. Greve | Zendy; Marcus Y. Chen | Zendy; Sujata M. Shanbhag | Zendy; W. Patricia Bandettini | Zendy; Andrew E. Arai | Zendy

Open Access

Diagnostic Performance of Fully Automated Pixel-Wise Quantitative Myocardial Perfusion Imaging by Cardiovascular Magnetic Resonance

Author(s) -

LiYueh Hsu,

Matthew Jacobs,

Mitchel Benovoy,

Allison Ta,

Hannah Conn,

Susanne Winkler,

Anders M. Greve,

Marcus Y. Chen,

Sujata M. Shanbhag,

W. Patricia Bandettini,

Andrew E. Arai

Publication year - 2018

Publication title -

jacc. cardiovascular imaging

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 5.79

H-Index - 120

eISSN - 1936-878X

pISSN - 1876-7591

DOI - 10.1016/j.jcmg.2018.01.005

Subject(s) - perfusion , medicine , coronary artery disease , stenosis , magnetic resonance imaging , blood flow , cardiac magnetic resonance , myocardial perfusion imaging , radiology , coronary angiography , perfusion scanning , cardiology , nuclear medicine , myocardial infarction

The authors developed a fully automated framework to quantify myocardial blood flow (MBF) from contrast-enhanced cardiac magnetic resonance (CMR) perfusion imaging and evaluated its diagnostic performance in patients.

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