Minsuok has multidisciplinary study and research experience including science, engineering and healthcare technology area.

He obtained his PhD at the State University of New York at Buffalo for Mechanical and Aerospace Engineering in 2007. His thesis research topic was the computational modelling of neurovascular haemodynamics and endovascular intervention devices.

His research interest in Biomedical Engineering led him to join a European research project, @neuIST. In this project, he developed a computational model to simulate the neurovascular haemodynamics and endovascular intervention devices to treat brain aneurysms.

Later, he came to the University of Oxford (Computer Science, 2011-2014) to join multi-scale respiratory system modelling projects, Synergy-COPD and AirPROM. These projects allowed him to study respiratory dynamics and consequently introduce a new medical image-based full-scale airway flow model. His modelling technique was advanced while he was working at the University of Warwick (Engineering, 2014-2017) and the University of Oxford (Engineering Science, 2017-2019). The latest outcomes of his modelling research enabled to assess the functional assessment of a lung utilizing conventional structure imaging CT data.

His current research interests lie in medical image-based respiratory system dynamics modelling of diseased lungs and its application to develop a medical image-based artificial intelligence algorithm to support the respiratory disease diagnosis.

Qualifications:

• PhD, Mechanical and Aerospace Engineering, State University of New York at Buffalo, NY, USA
• MS, Environmental and Health Science, University of Michigan, MI, USA
• MS, Civil Engineering, Seoul National University, South Korea

Medical image-based airway flow modelling

Lung disease is a leading cause of mortality and morbidity with a substantial worldwide economic and social burden. Thoracic computed tomography (CT) is a clinically established diagnostic technique to detect structural pulmonary abnormalities. Although CT is routinely performed clinically, it provides no functional pulmonary information. Furthermore, the structural evaluation from CT does not predict the rate of decline of an individual’s health or their COPD prognosis. My research interests include the development of medical image-based computational modelling technique to simulate the respiratory flow dynamics and to improve clinical diagnosis by providing accurate assessments of lung function.

Artificial intelligence system to support medical image analysis

Artificial intelligence (AI) is one of the main factors driving future technology development. Application of AI in clinical assessment is expected to improve diagnostic and post-operative outcomes drastically. In this mainstream, I would like to apply the current computational modelling techniques to introduce an advanced AI system. This approach will facilitate the development of a smarter AI algorithm, analysing medical images to elucidate the disease-related factors beyond human capability.

Grants and contracts:

• 2015-2016 Warwick Impact Fund, University of Warwick

Recent publications:

Journal articles

• M. Kim, O. Doganay, T Matin, T. Povey, F. V. Gleeson, “CT-based Airway Flow Model to Assess Ventilation in Chronic Obstructive Pulmonary Disease: A Pilot Study”, Radiology, 2019 (in print).
• O. Doganay, T. Matin, M. Chen, M. Kim, A. McIntyre, D. McGowan, T. Povey, F. V. Gleeson, "Time-series hyperpolarized Xenon-129 MRI of lobar lung ventilation of COPD in comparison to V/Q-SPECT and CT", Eur Radiol, 29(8): 4058-4967, 2018. doi: 10.1007/s00330-018-5888-y.
• M. Kim, G. J. Collier, J. M. Wild, Y. Chung. “Effect of upper airway on tracheobronchial fluid dynamics”, Int J Numer Meth Biomed Eng, 34(9):e3112, 2018. doi: 10.1002/cnm.3112.  
• G. J. Collier, M. Kim, Y. Chung, J. M. Wild, “3D phase contrast MRI in models of human airways - validation of computational fluid dynamics simulations of steady inspiratory flow”, J Magn Reson Imaging, 48(5): 1400-1409, 2018. doi: 10.1002/jmri.26039.
• K. S. Burrowes, T. Doel, M. Kim, C. Vargas, J. Roca, V. Grau, D. Kay, “A combined image-modelling approach assessing the impact of hyperinflation due to emphysema on regional ventilation perfusion matching” Comput Methods Biomech Biomed Eng: Imaging & Visualization, 5(2): 110-126, 2017. doi: 10.1080/21681163.2015.1023358.

Book chapter

• M. C. Villa-Uriol, I. Larrabide, J. M. Pozo, M. Kim, M. de Craene, O. Camara, C. Zhang, A. J. Geers, H. Morales, H. Bogunović, A. F. Frangi, “Cerebral Aneurysms: A Patient-Specific and Image-Based Management Pipeline”, Computational Vision and Medical Image Processing. Computational Methods in Applied Sciences, 19: 327-349, 2010. Springer, Dordrecht. Doi: 10.1007/978-94-007-0011-6_19.

Conference presentations

• M. Kim, O. Doganay, T. Matin, T. Povey, F. Gleeson, "Comparison of the thoracic CT-based computational model with hyperpolarized Xenon-129 MRI and SPECT images to assess pulmonary ventilation in COPD patients", ERS 2019, Madrid, Spain, 28 September–2 October 2019.
• O. Doganay, M. Kim, M. Chen, T. Matin, F. Gleeson, "Gas-exchange and ventilation imaging of COPD in comparison to a healthy cohort using hyperpolarized Xenon-129 MRI", ERS 2019, Madrid, Spain, 28 September–2 October 2019.
• M. Kim, O. Doganay, T. N. H. Matin, T. Povey, F. V. Gleeson, “CT-based computational model to assess pulmonary ventilation in COPD patients: Comparison with 129Xe-MRI and SPECT images”, ESTI 2019, Paris, France 9-11, May 2019.

Selected publications:

• M. Kim, O. Doganay, T Matin, T. Povey, F. V. Gleeson, “CT-based Airway Flow Model to Assess Ventilation in Chronic Obstructive Pulmonary Disease: A Pilot Study”, Radiology, 2019 (in print).
• M. Kim, G. J. Collier, J. M. Wild, Y. Chung. “Effect of upper airway on tracheobronchial fluid dynamics”, Int J Numer Meth Biomed Eng, 34(9):e3112, 2018. doi: 10.1002/cnm.3112.  
• M. Kim, R. Bordas, W. Vos, R. A. Hartley, C.E. Brightling, D. Kay, V. Grau, K.S. Burrowes, “Dynamic flow characteristics in normal and asthmatic lungs”, Int J Numer Meth Biomed Eng, 31(12), 2015. doi: 10.1002/cnm.2730.
• M. Kim, S. Cirovic, “A computational model of the cerebrospinal fluid system incorporating lumped parameter cranial compartment and one-dimensional distributed spinal compartment”, J BioRheol, 25(1-2): 78-87, 2011. doi: 10.1007/s12573-011-0041-4.
• H. G. Morales, M. Kim, E. E. Vivas, M. C. Villa-Uriol, I. Larrabide, T. Sola, L. Guimaraens, A. F. Frangi, “How do coil configuration and packing density influence intra-aneurysmal hemodynamics?”, Am J Neuroradiol, 32(10): 1935-1941, 2011. doi: 10.3174/ajnr.A2635.
• M. Kim, M. Tremmel, D. B. Taulbee, H. Meng, “Comparison of Two Stents in Modifying Cerebral Aneurysm Hemodynamics”, Ann Biomed Eng, 36(5):726-41, 2008. doi: 10.1007/s10439-008-9449-4. 

• University of Oxford, Oxford, UK
• University of Warwick, Coventry, UK
• Asan Hospital, Seoul, South Korea