A motion-corrected deep-learning reconstruction framework for accelerating whole-heart MRI in patients with congenital heart disease

Research output: Contribution to journal › Article › peer-review

Original language	English
Journal	Journal of Cardiovascular Magnetic Resonance
Publication status	Accepted/In press - 14 Mar 2024

Cite this

@article{c415e4f3992e43f6b3713d25f948945d,

title = "A motion-corrected deep-learning reconstruction framework for accelerating whole-heart MRI in patients with congenital heart disease",

author = "Andrew Phair and Anastasia Fotaki and Lina Felsner and Thomas Fletcher and Haikun Qi and Rene Botnar and {Prieto Vasquez}, Claudia",

year = "2024",

month = mar,

day = "14",

language = "English",

journal = "Journal of Cardiovascular Magnetic Resonance",

issn = "1097-6647",

publisher = "BioMed Central",

}

TY - JOUR

T1 - A motion-corrected deep-learning reconstruction framework for accelerating whole-heart MRI in patients with congenital heart disease

AU - Phair, Andrew

AU - Fotaki, Anastasia

AU - Felsner, Lina

AU - Fletcher, Thomas

AU - Qi, Haikun

AU - Botnar, Rene

AU - Prieto Vasquez, Claudia

PY - 2024/3/14

Y1 - 2024/3/14

M3 - Article

SN - 1097-6647

JO - Journal of Cardiovascular Magnetic Resonance

JF - Journal of Cardiovascular Magnetic Resonance

ER -