Preprint

1. T. H. Kim, P. Garg, J. P. Haldar. LORAKI: Autocalibrated Recurrent Neural Networks for Autoregressive Reconstruction in k-Space. arXiv:1904.09390 [link] 

Journal 

(† : equal contribution)

1. J. Lee, J. Kim, K. Ryu, M. A. Al-masni, T. H. Kim, D. Han, H. G. Kim, D. Kim, JUST‐Net: Jointly unrolled cross‐domain optimization based spatio‐temporal reconstruction network for accelerated 3D myelin water imaging. Magnetic Resonance in Medicine (In Press), 2024 [link]

2. A. Heydari, A. Ahmadi, T. H. Kim, B. Bilgic. Joint MAPLE: Accelerated joint T1 and T2* mapping with scan-specific self-supervised networks. Magnetic Resonance in Medicine (In Press), 2024 [link]

3. J. Cho, B. Gagoski, T. H. Kim, F. Wang, M. K. Manhard, D. Dean III, S. Kecskemeti, A. Caprihan, W. Lo, D. N. Splitthoff, W. Liu, D. Polak, S. Cauley, K. Setsompop, P. E. Grant, B. Bilgic. Time‐efficient, high‐resolution 3T whole‐brain relaxometry using 3D‐QALAS with wave‐CAIPI readouts. Magnetic Resonance in Medicine, 91: 630-639, 2024 [link]

4. C. Liao, U. Yarach, X. Cao, S. S. Iyer, N. Wang, T. H. Kim, Q. Tian, B. Bilgic, A. B. Kerr, K. Setsompop. High-fidelity mesoscale in-vivo diffusion MRI through gSlider-BUDA and circular EPI with S-LORAKS reconstruction. NeuroImage, 275:120168, 2023. [link]

5. G. Ramos-Llorden, R. A. Lobos, T. H. Kim, Q. Tian, T. Witzel, H.-H. Lee, A. Scholz, B. Keil, A. Yendiki, B. Bilgic, J. P. Haldar, S. Y. Huang. High-fidelity, high-spatial-resolution diffusion MRI of the ex vivo whole human brain at ultra-high gradient strength with structured low-rank EPI ghost correction. NMR in Biomedicine, 36:e4831, 2023. [link]

6. J. Cho, B. Gagoski, T. H. Kim, Q. Tian, S. R. Frost, I. Chatnuntawech, B. Bilgic. Wave-Encoded Model-based Deep Learning for Highly Accelerated Imaging with Joint Reconstruction. Bioengineering, 9:736, 2022. [link]

   • Editor's Choice Articles [link]

7. T. H. Kim, J. P. Haldar. Efficient Iterative Solutions to Complex-Valued Nonlinear Least-Squares Problems with Mixed Linear and Antilinear Operators. Optimization and Engineering, 23:749-768, 2022. [link]

8. T. H. Kim, B. Bilgic, D. Polak, K. Setsompop, J. P. Haldar. Wave-LORAKS: Combining Wave Encoding with Structured Low-Rank Matrix Modeling for More Highly Accelerated 3D Imaging. Magnetic Resonance in Medicine, 81:1620-1633, 2019. [link]

9. R. A. Lobos, T. H. Kim, W. S. Hoge, J. P. Haldar. Navigator-free EPI Ghost Correction with Structured Low-Rank Matrix Models: New Theory and Methods. IEEE Transactions on Medical Imaging, 37:2390-2402, 2018. [link]

10. B. Bilgic†, T. H. Kim†, C. Liao, M. K. Manhard, L. L. Wald, J. P. Haldar, K. Setsompop. Improving Parallel Imaging by Jointly Reconstructing Multi-Contrast Data. Magnetic Resonance in Medicine, 80: 619-632, 2018. Editor's Pick, August 2018. [link]

    • Editor's Pick, August 2018 [Highlights Interview] [Audioslides] 

    • 20 most read paper in 2017-2018 [certificate]

11. T. H. Kim, K. Setsompop, J. P. Haldar. LORAKS Makes Better SENSE: Phase-Constrained Partial Fourier SENSE Reconstruction without Phase Calibration. Magnetic Resonance in Medicine, 77:1021-1035, 2017. [link]

Conference

1. S. H. Kang, J. Kim, J. Cho, C. Z. Cooley, B. Bilgic, T. H. Kim, Robust Partial Fourier Reconstruction with Zero-shot Deep Untrained Generative Prior, International Society for Magnetic Resonance in Medicine Annual Meeting, Singapore, 2024, (Abstract)

2. T. H. Kim, J. Cho, B. Gagoski, Z. Dong, F. Wang, S. H. Kang, and B. Bilgic. Multi-echo MRI Reconstruction with Iteratively Refined Zero-shot Spatio-Temporal Deep Generative Prior, International Society for Magnetic Resonance in Medicine Annual Meeting, Toronto, 2023, P. 0828. (Abstract, Power Pitch Oral Presentation)

3. T. H. Kim, J. Cho, B. Gagoski, Z. Dong, F. Wang, S. H. Kang, B. Bilgic, Zero-shot Prior Learning of Spatio-temporal Multi-echo/contrast MRI Reconstruction with Iterative Refinement, ISMRM Workshop on Data Sampling & Image Reconstruction, 2023. (abstract) 

4. T. H. Kim, J. Cho, B. Zhao, B. Bilgic, Accelerated MR Parameter Mapping with Scan-specific Unsupervised Networks, The 10th International Congress on Magnetic Resonance Imaging & 27th Annual Scientific Meeting of KSMRM (ICMRI 2022). (abstract, Invited Presentation)

5. T. H. Kim, J. Cho, B. Zhao, B. Bilgic. Accelerated MR Parameter Mapping With Scan-Specific Unsupervised Networks. International Society for Magnetic Resonance in Medicine Annual Meeting, London, 2022, p. 4402. (abstract, online gather town power pitch)

   • Recipient of a Magna Cum Laude ISMRM Merit Award

6. K.-S. Chan, T. H. Kim, B. Bilgic, J. P Marques. Semi-Supervised Learning For Fast Multi-Compartment Relaxometry Myelin Water Imaging (MCR-MWI). International Society for Magnetic Resonance in Medicine Annual Meeting, London, 2022, p. 1639. (abstract)

7. M. Gao, H. Ye, T. H. Kim, Z. Zhang, S. So, B. Bilgic. Accurate Parameter Estimation Using Scan-Specific Unsupervised Deep Learning For Relaxometry And MR Fingerprinting. International Society for Magnetic Resonance in Medicine Annual Meeting, London, 2022, p. 4320. (abstract)

8. J. Cho , B. Gagoski , T. H. Kim, Q. Tian, R. Frost, I. Chatnuntawech, B. Bilgic. Rapid Quantitative Imaging Using Wave-Encoded Model-Based Deep Learning For Joint Reconstruction. International Society for Magnetic Resonance in Medicine Annual Meeting, London, 2022, p. 0435. (abstract)

9. J. Cho, T. H. Kim, B. Gagoski, Z. Zhang, A. Berman, C. Liao, B. Bilgic. Variable Flip, Blip-Up And -Down Undersampling (VUDU) Enables Motion-Robust, Distortion-Free Multi-Shot EPI. International Society for Magnetic Resonance in Medicine Annual Meeting, London, 2022, p. 0757. (abstract)

10. G. Varela-Mattatall, T. H. Kim, J. Cho , W.-C. Lo, B. A. Gagoski, R. S. Menon, B. Bilgic. Rapid CS-Wave MPRAGE Acquisition With Automated Parameter Selection. International Society for Magnetic Resonance in Medicine Annual Meeting, London, 2022, p. 1604. (abstract)

11. C. Liao, U. Yarach, X. Cao, S. S. Iyer, N. Wang, T. H. Kim, B. Bilgic, A. Kerr, K. Setsompop. High-Fidelity Submillimeter-Isotropic-Resolution Diffusion MRI Through GSlider-BUDA And Circular EPI With S-LORAKS Reconstruction. International Society for Magnetic Resonance in Medicine Annual Meeting, London, 2022, p. 0042. (abstract)

12. Y. Rathi, J.-F. Nielsen, M. Zaitsev, L. Ning, J.-Y. Chiou, T. H. Kim, W. Grissom, B. Gagoski, B. Bilgic. A Vendor-Neutral 3D MP-RAGE Implementation In Pulseq For Reduced Inter-Site Variability. International Society for Magnetic Resonance in Medicine Annual Meeting, London, 2022, p. 1618. (abstract)

13. H.-G. Shin, J. Chen, J. Lee, T. H. Kim, J. Cho, G. Varela-Mattatall, B. Gagoski, C. Liu, B. Bilgic. QALAS + QSM: Efficient Multi-Parameter Mapping Allows Disentangling Para- And Dia-Magnetic Contributions In Brain Tissue. International Society for Magnetic Resonance in Medicine Annual Meeting, London, 2022, p. 2465. (abstract)

14. U. Yarach, I. Chatnuntawech, C. Liao, S. Teerapittayanon, S. S. Iyer, T. H. Kim, J. Cho, B. Bilgic, Y. Hu, B. Hargreaves, K. Setsompop. Rapid Reconstruction Of Blip Up-Down Circular EPI (BUDA-CEPI) For Distortion-Free DMRI Using An Unrolled Network With U-Net As Priors. International Society for Magnetic Resonance in Medicine Annual Meeting, London, 2022, p. 4348. (abstract)

15. B. Gagoski, J. Cho, Z Zhang, T. H. Kim, W.-C. Lo, D. Polak, M. Warntjes, S. Cauley, K. Setsompop, P. E. Grant, B. Bilgic. Time-Efficient, High Resolution 3T Whole Brain Relaxometry Using 3D-QALAS With Wave-CAIPI Readouts. International Society for Magnetic Resonance in Medicine Annual Meeting, London, 2022, p. 4388. (abstract)

16. T. H. Kim, Z. Zhang, J. Cho, B. Gagoski, J. P. Haldar, B. Bilgic. Robust multi-shot EPI with untrained artificial neural networks: Unsupervised scan-specific deep learning for blip up-down acquisition (BUDA). International Society for Magnetic Resonance in Medicine Annual Meeting, 2021, p. 224. (Abstract, Oral Presentation) [link]

    • Recipient of a Magna Cum Laude ISMRM Merit Award

17. Y. Arefeen, T. H. Kim, J. Haldar, E. Grant, B. Gagoski, B. Bilgic, E. Adalsteinsson. Rapid fetal HASTE imaging using variable flip angles and simultaneous multislice wave-LORAKS. International Society for Magnetic Resonance in Medicine Annual Meeting, 2021, p. 347. (Abstract, Oral Presentation) [link]

    • Recipient of a Summa Cum Laude ISMRM Merit Award

18. G. Ramos-Llorden, R. A. Lobos, T. H. Kim, Q. Tian, S. Tounetki, T. Witzel, B. Keil, A. Yendiki, B. Bilgic, J. P. Haldar, S. Huang. Improved multi-shot EPI ghost correction for high gradient strength diffusion MRI using structured low-rank modeling k-space reconstruction. International Society for Magnetic Resonance in Medicine Annual Meeting, 2021, p. 1346. (Abstract) [link]

19. Z. Zhang, L. Wang, H. G. Shin, J. Cho, T. H. Kim, J. Lee, J. Xu, T. Zhang, H. Liu, and B. Bilgic. BUDA-SAGE with unsupervised denoising enables fast, distortion-free, high-resolution T2, T2*, iron and myelin susceptibility mapping. International Society for Magnetic Resonance in Medicine Annual Meeting, 2021, p. 3062. (abstract) [link]

20. R. A. Lobos, T. H. Kim, K. Setsompop, J. P. Haldar. Advanced new linear predictive reconstruction methods for simultaneous multislice imaging. International Society for Magnetic Resonance in Medicine Virtual Conference & Exhibition, 2020, p. 3437. (Abstract) [link] 

21. T. H. Kim, J. P. Haldar. Learning how to interpolate Fourier data with unknown autoregressive structure. Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, 2019. (Invited Presentation) [link] 

22. T. H. Kim, J. P. Haldar. Learning-based computational MRI reconstruction without big data: From structured low-rank matrices to recurrent neural networks. Wavelets and Sparsity XVIII, Proceedings of SPIE, San Diego, 2019. (Invited Presentation) [link]

23. T. H. Kim, P. Garg, J. P. Haldar. LORAKI: Reconstruction of Undersampled k-space Data using Scan-Specific Autocalibrated Recurrent Neural Networks. International Society for Magnetic Resonance in Medicine 27th Annual Meeting, Montreal, 2019. (abstract) [link]  

24. T. H. Kim, J. P. Haldar. Assessing MR image reconstruction quality using the Fourier Radial Error Spectrum plot. Joint Annual Meeting ISMRM-ESMRMB, Paris, 2018, p. 249. (Abstract, Oral Presentation) [link]

25. B. Bilgic, T. H. Kim, C. Liao, M. K. Manhard, L. L. Wald, J. P. Haldar, K. Setsompop. Improving parallel imaging by jointly reconstructing multi-contrast data. Joint Annual Meeting ISMRM-ESMRMB, Paris, 2018, p. 3505. (Abstract) [link]

26. T. H. Kim, J. P. Haldar. The Fourier Radial Error Spectrum Plot: A more nuanced quantitative evaluation of image reconstruction quality. IEEE International Symposium on Biomedical Imaging, Washington, DC, 2018. pp. 61–64. (Oral Presentation) [link]

27. J. P. Haldar, T. H. Kim. Computational imaging with LORAKS: Reconstructing linearly predictable signals using low-rank matrix regularization. Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, 2017. [link]

28. T. H. Kim, B. Bilgic, D. Polak, K. Setsompop, J. P. Haldar. Wave-LORAKS for faster Wave-CAIPI MRI. International Society for Magnetic Resonance in Medicine 25th Annual Meeting, Honolulu, 2017, p. 1037. (Abstract, Oral Presentation) [link]

    • Recipient of a Magna Cum Laude ISMRM Merit Award

29. R. A. Lobos, T. H. Kim, W. S. Hoge, J. P. Haldar. Navigator-free EPI ghost correction using low-rank matrix modeling: Theoretical insights and practical improvements. International Society for Magnetic Resonance in Medicine 25th Annual Meeting, Honolulu, 2017, p. 449. (Abstract) [link]

    • Recipient of a Summa Cum Laude ISMRM Merit Award.  Featured with a Power Pitch presentation (hand-selected as one of the 220 most interesting abstracts out of 6,780 submissions to the conference).

30. T. H. Kim, K. Setsompop, J. P. Haldar. SENSE-LORAKS: Phase-Constrained Parallel MRI without Phase Calibration. International Society for Magnetic Resonance in Medicine 24rd Annual Meeting, Singapore, 2016. (Abstract, Oral Presentation) [link]

    • Recipient of a Magna Cum Laude ISMRM Merit Award

31. T. H. Kim, K. Setsompop, J. P. Haldar. Partial Fourier SENSE Reconstruction without Phase Calibration. ISMRM Workshop on Data Sampling & Image Reconstruction, Sedona, 2016. (Abstract) [link (ISMRM Login Required)]

32. V. L. Landes, T. H. Kim, J. P. Haldar, K. S. Nayak. Experimental Validation of SMS-LORAKS. ISMRM Workshop on Simultaneous Multi-Slice Imaging: Neuroscience & Clinical Applications, Pacific Grove, 2015. (Abstract) [link (ISMRM Login Required)]

33. T. H. Kim, J. P. Haldar. Simultaneous Multi-slice MRI Reconstruction using LORAKS. International Society for Magnetic Resonance in Medicine 23rd Annual Meeting, Toronto, 2015, p. 78. (Abstract, Oral Presentation) [link]

    • Recipient of a Magna Cum Laude ISMRM Merit Award

34. T. H. Kim, J. P. Haldar. SMS-LORAKS: Calibrationless Simultaneous Multislice MRI using Low-Rank Matrix Modeling. IEEE International Symposium on Biomedical Imaging: From Nano to Macro, New York City, 2015, pp. 323–326. (Oral Presentation) [link]

35. M. Wellman, T. H. Kim, Q.Duong. Analyzing Incentives for Protocol Compliance in Complex Domains: A Case Study of Introduction-Based Routing. Workshop on the Economics of Information Security, Washington, DC, 2013 [link]

Technical Report

1. T. H. Kim, J. P. Haldar. LORAKS Software Version 2.0: Faster Implementation and Enhanced Capabilities. University of Southern California, Los Angeles, CA, Technical Report USC-SIPI-443, May 2018. [link]