Jinho Kim
Jinho Kim, M. Sc.
Department Artificial Intelligence in Biomedical Engineering (AIBE)
W3-Professur für Computational Imaging
Werner-von-Siemens Str. 61
91052 Erlangen
- Email: jinho.kim@fau.de
- Website: https://www.cil.tf.fau.de/person/kim/
Office hours
by appointment
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.
If you are interested in my research topics for your Bachelor’s or Master’s thesis, project, or collaboration, feel free to contact me via jinho.kim@fau.de
Academic CV
- 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
Publications
2024
Conference Contributions
- , , :
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
- , , , , :
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)
Teaching
- Computational Magnetic Resonance Imaging (WS/SS)
- Medizintechnik II (SS)
Student Projects
| Student | Title | Type | Status |
| Navaneeth Narayanan | Deep Convolutional Framelets for Deep Learning-based MRI Reconstruction | Master’s Thesis | Running |
| Javier Munoz | Self-Supervised Learning Model via Data Undersampling SSDU for MRI Reconstruction | Project | Finished |