Tae Hyung Kim
Tae Hyung Kim
Assistant Professor, Computer Engineering, Hongik University
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Zitiert von
Zitiert von
Improving parallel imaging by jointly reconstructing multi‐contrast data
B Bilgic, TH Kim, C Liao, MK Manhard, LL Wald, JP Haldar, K Setsompop
Magnetic resonance in medicine 80 (2), 619-632, 2018
LORAKS makes better SENSE: Phase‐constrained partial fourier SENSE reconstruction without phase calibration
TH Kim, K Setsompop, JP Haldar
Magnetic resonance in medicine 77 (3), 1021-1035, 2017
Navigator-free EPI ghost correction with structured low-rank matrix models: New theory and methods
RA Lobos, TH Kim, WS Hoge, JP Haldar
IEEE transactions on medical imaging 37 (11), 2390-2402, 2018
LORAKI: Autocalibrated recurrent neural networks for autoregressive MRI reconstruction in k-space
TH Kim, P Garg, JP Haldar
arXiv preprint arXiv:1904.09390, 2019
Wave‐LORAKS: Combining wave encoding with structured low‐rank matrix modeling for more highly accelerated 3D imaging
TH Kim, B Bilgic, D Polak, K Setsompop, JP Haldar
Magnetic resonance in medicine 81 (3), 1620-1633, 2019
Analyzing incentives for protocol compliance in complex domains: A case study of introduction-based routing
MP Wellman, TH Kim, Q Duong
arXiv preprint arXiv:1306.0388, 2013
LORAKS Software Version 2.0: Faster Implementation and Enhanced Capabilities
TH Kim, JP Haldar
USC-SIPI Technical Report, 443, 2018
The Fourier radial error spectrum plot: A more nuanced quantitative evaluation of image reconstruction quality
TH Kim, JP Haldar
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 61-64, 2018
Navigator-free EPI ghost correction using low-rank matrix modeling: Theoretical insights and practical improvements
RA Lobos, TH Kim, WS Hoge, JP Haldar
Proc. Int. Soc. Magn. Reson. Med, 0449, 2017
SMS-LORAKS: Calibrationless simultaneous multislice MRI using low-rank matrix modeling
TH Kim, JP Haldar
2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), 323-326, 2015
Computational imaging with LORAKS: Reconstructing linearly predictable signals using low-rank matrix regularization
JP Haldar, TH Kim
2017 51st Asilomar Conference on Signals, Systems, and Computers, 1870-1874, 2017
LORAKI: Reconstruction of undersampled k-space data using scan-specific autocalibrated recurrent neural networks
TH Kim, P Garg, JP Haldar
Proc. Int. Soc. Magn. Reson. Med 4647, 50-63, 2019
Wave-LORAKS for faster wave-CAIPI MRI
TH Kim, B Bilgic, D Polak, K Setsompop, JP Haldar
Proc. Int. Soc. Magn. Reson. Med 25, 1037, 2017
Scan-specific recurrent neural network for image reconstruction
TH Kim, J Haldar
US Patent App. 16/939,535, 2021
Learning-based computational MRI reconstruction without big data: from linear interpolation and structured low-rank matrices to recurrent neural networks
TH Kim, JP Haldar
Wavelets and Sparsity XVIII 11138, 346-352, 2019
Assessing MR image reconstruction quality using the fourier radial error spectrum plot
TH Kim, JP Haldar
Proceedings 26th Annual Meeting International Society for Magnetic Resonance …, 2018
Efficient iterative solutions to complex-valued nonlinear least-squares problems with mixed linear and antilinear operators
TH Kim, JP Haldar
Optimization and engineering 23 (2), 749-768, 2022
Rapid reconstruction of Blip up-down circular EPI (BUDA-cEPI) for distortion-free dMRI using an Unrolled Network with U-Net as Priors
U Yarach, I Chatnuntawech, C Liao, S Teerapittayanon, SS Iyer, TH Kim, ...
proceedings of the Joint Annual Meeting ISMRM-ESMRMB, London, United Kindom …, 2022
High-fidelity submillimeter-isotropic-resolution diffusion MRI through gSlider-BUDA and circular EPI with S-LORAKS reconstruction
C Liao, U Yarach, X Cao, SS Iyer, N Wang, TH Kim, B Bilgic, A Kerr, ...
ISMRM, London, 2022
Robust Multi-shot EPI with Untrained Artiêcial Neural Networks: Unsupervised Scan-speciêc Deep Learning for Blip Up-Down Acquisition (BUDA)
TH Kim, Z Zhang, J Cho, B Gagoski, J Haldar, B Bilgic
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