K
Kyunghyun Sung
Researcher at University of California, Los Angeles
Publications - 74
Citations - 2307
Kyunghyun Sung is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Medicine & Flip angle. The author has an hindex of 19, co-authored 65 publications receiving 1788 citations. Previous affiliations of Kyunghyun Sung include Ronald Reagan UCLA Medical Center & University of Southern California.
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Journal ArticleDOI
Free-breathing liver fat quantification using a multiecho 3D stack-of-radial technique.
Tess Armstrong,Isabel Dregely,Isabel Dregely,Alto Stemmer,Fei Han,Yutaka Natsuaki,Kyunghyun Sung,Holden H. Wu +7 more
TL;DR: A novel free‐breathing 3D stack‐of‐radial (FB radial) liver fat quantification technique is developed and evaluated in a preliminary study.
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Design and use of tailored hard‐pulse trains for uniformed saturation of myocardium at 3 Tesla
Kyunghyun Sung,Krishna S. Nayak +1 more
TL;DR: This study proposes the use of trains of weighted hard pulses that are optimized for the measured variation of B0 and B1 fields in the myocardium, which are simple to design, and require substantially lower RF power when compared with BIR‐4 pulses.
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Measuring human placental blood flow with multidelay 3D GRASE pseudocontinuous arterial spin labeling at 3T
Xingfeng Shao,Dapeng Liu,Thomas Martin,Teresa Chanlaw,Sherin U. Devaskar,Carla Janzen,Aisling Murphy,Daniel Margolis,Kyunghyun Sung,Danny J.J. Wang +9 more
TL;DR: Maternal blood supply to placenta can be measured noninvasively using arterial spin labeling (ASL) to measure the health of both a woman and her fetus during pregnancy.
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Reducing Artifacts during Arterial Phase of Gadoxetate Disodium–enhanced MR Imaging: Dilution Method versus Reduced Injection Rate
Young Kon Kim,Wei-Chan Lin,Kyunghyun Sung,Steven S. Raman,Daniel Margolis,Yaeji Lim,Seonhye Gu,David S.K. Lu +7 more
TL;DR: Two contrast material-administration protocols (dilution vs slow injection) in terms of their effectiveness in arterial phase artifact reduction at gadoxetic acid-enhanced magnetic resonance (MR) imaging were compared.
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Deep learning-based radiomic features for improving neoadjuvant chemoradiation response prediction in locally advanced rectal cancer.
Jie Fu,Xinran Zhong,Ning Li,Ritchell van Dams,John H. Lewis,Kyunghyun Sung,Ann C. Raldow,Jing Jin,S Qi +8 more
TL;DR: DL-based features extracted from pre-treatment DWIs achieved significantly better classification performance compared with handcrafted features for predicting nCRT response in LARC patients.