Jinho Kim
Professorship for Computational Imaging
Doctoral candidates
Contact
MR cholangiopancreatography (MRCP) is a sparse and motion-sensitive medical imaging technique that results in unpleasant image quality. To address the current problem, my research focuses on Deep Learning-based reconstruction and motion correction of highly accelerated MRCP.
- Since 10.2022
Ph.D. Candidate at Computational Imaging Lab and Siemens Healthineers AG - 09.2018 - 07.2021
M.Sc. in Medical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
Thesis: "Deep Learning-based Respiratory Motion Correction in Free-Breathing Abdominal Diffusion-Weighted Imaging," collaborated with Siemens Healthineers AG, Erlangen - 03.2011 - 08.2018
B.Sc. in Information, Communication, and Electronics Engineering, The Catholic University of Korea, Republic of Korea
Thesis:
- Cumulative- J. Kim, C. Lee, "Efficient Method for Real-time Implementation of Image Enhancement and Image Upscaling," Journal of IEIE, Vol.54, No.11, pp.146-153, Nov. 2017
- J. Kim, M. Gil, C. Lee, "Efficient Color Image Enhancement Technique using Saturation Components of Color Images," Journal of KIBME, Vol.20, No.5, pp.770-773, Aug. 2015
- Since 06.2026
AI Infrasturcutre Engineer at BMW Group, München - 10.2022 - 03.2026
PhD researcher at Computational Imaging Lab, FAU Erlangen-Nürnberg, Erlangen - 11.2020 - 07.2021
Intership at Siemens Healthineers AG, Erlangen - 04.2019 - 12.2020
Working student at Fraunhofer IISB, Erlangen- 06.2014 - 07.2018
Student research assistant at the Catholic University of Korea, Bucheon, South Korea
- 06.2014 - 07.2018
2026
Journal Articles
- , , :
Zero-Shot Self-Supervised Learning of Single Breath-Hold Magnetic Resonance Cholangiopancreatography (MRCP) Reconstruction
In: Magnetic Resonance in Medicine (2026)
ISSN: 0740-3194
DOI: 10.1002/mrm.70467
2025
Journal Articles
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Deep Learning-Based Accelerated MR Cholangiopancreatography Without Fully-Sampled Data
In: NMR in Biomedicine 38 (2025), p. e70002
ISSN: 0952-3480
DOI: 10.1002/nbm.70002
2024
Conference Contributions
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Assessment of Deep Learning-based Reconstruction with Imperfect Ground Truth for MRCP
International Federation of Information Processing (IFIP) Technical Committee - 7 (TC-7) "System Modeling and Optimization" conferences (Hamburg, Germany, 12. August 2024 - 16. August 2024)
In: Assessment of Deep Learning-based Reconstruction with Imperfect Ground Truth for MRCP 2024 - , , :
Deep Learning-based Reconstruction of Accelerated MR Cholangiopancreatography
2024 ISMRM & ISMRT Annual Meeting & Exhibition (Singapore, 4. May 2024 - 9. May 2024)
In: Deep Learning-based Reconstruction of Accelerated MR Cholangiopancreatography 2024
2023
Conference Contributions
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Analysis of Deep Learning-based Reconstruction Models for Highly Accelerated MR Cholangiopancreatography: to Fine-tune or not to Fine-tune
2023 ISMRM & ISMRT Annual Meeting & Exhibition (Toronto, ON, 3. June 2023 - 8. June 2023)
In: Analysis of Deep Learning-baed Reconstruction Models for Highly Accelerated MR Cholangiopancreatography: to Fine-tune or not to Fine-tune 2023 - , , :
Overview of Magnetic Resonance Imaging Reconstruction Methods Using Various Constraints to Solve the Ill-posed Problems
Europe-Korea Conference on Science and Technology (Munich, 14. August 2023 - 18. August 2023)
In: Overview of Magnetic Resonance Imaging Reconstruction Methods Using Various Constraints to Solve the Ill-posed Problems 2023
2022
Conference Contributions
- , , , , :
Deep Learning-Based Respiratory Motion Correction in Free-Breathing Abodiminal Diffusion-Weighted Imaging
Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting (London, 7. May 2022 - 12. May 2022)
| Student | Title | Type | Status |
| Md Salahuddin Parvez | Deep Learning-based MRCP Reconstruction using adjacent slice information | Project | Finished |
| Navaneeth Narayanan | Deep Convolutional Framelets for Deep Learning-based MRI Reconstruction | Master's Thesis | Finished |
| Javier Munoz | Self-Supervised Learning Model via Data Undersampling SSDU for MRI Reconstruction | Project | Finished |