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.
Papers
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Transmit B1+ field inhomogeneity and T1 estimation errors in breast DCE-MRI at 3 tesla.
TL;DR: In this article, the authors showed that severe variation over the breasts can cause a substantial error in T1 estimation between the breasts, in VFA T1 maps at 3T, but that compensating for these variations can considerably improve accuracy of T1 measurements.
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In-Bore 3-T MR-guided Transrectal Targeted Prostate Biopsy: Prostate Imaging Reporting and Data System Version 2-based Diagnostic Performance for Detection of Prostate Cancer.
Nelly Tan,Wei-Chan Lin,Pooria Khoshnoodi,Nazanin H. Asvadi,Jeffrey Yoshida,Daniel Margolis,David S.K. Lu,Holden H. Wu,Kyunghyun Sung,David Y. Lu,Jaioti Huang,Steven S. Raman +11 more
TL;DR: In-bore 3-T MR-guided biopsy is safe and effective for prostate cancer diagnosis when stratified according to PI-RADS versions 1 and 2.
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Compressed‐Sensing multispectral imaging of the postoperative spine
TL;DR: To apply compressed sensing to in vivo multispectral imaging (MSI), which uses additional encoding to avoid magnetic resonance imaging (MRI) artifacts near metal, and demonstrate the feasibility of CS‐MSI in postoperative spinal imaging.
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Exploring Uncertainty Measures in Bayesian Deep Attentive Neural Networks for Prostate Zonal Segmentation.
Yongkai Liu,Guang Yang,Melina Hosseiny,Afshin Azadikhah,Sohrab Afshari Mirak,Qi Miao,Steven S. Raman,Kyunghyun Sung +7 more
TL;DR: A spatial attentive Bayesian deep learning network for the automatic segmentation of the peripheral zone (PZ) and transition zone (TZ) of the prostate with uncertainty estimation enabled the accuracy of the PZ and TZ segmentation, which outperformed the state-of-art methods.
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Deep transfer learning-based prostate cancer classification using 3 Tesla multi-parametric MRI
Xinran Zhong,Ruiming Cao,Sepideh Shakeri,Fabien Scalzo,Yeejin Lee,Dieter R. Enzmann,Holden H. Wu,Steven S. Raman,Kyunghyun Sung +8 more
TL;DR: A deep transfer learning (DTL)-based model to distinguish indolent from clinically significant prostate cancer (PCa) lesions and to compare the DTL-based model with a deep learning (DL) model without transfer learning and PIRADS v2 score on 3 Tesla multi-parametric MRI with whole-mount histopathology validation is proposed.