Kâmil Uludağ, PhD

Magnetic resonance imaging (MRI) at 1.5, 3 and 7 Tesla magnetic field strength is an excellent tool to image soft tissue in the brain and body for fundamental research and clinical applications. However, the relationship between MRI contrasts and the underlying biochemistry, connectivity and cognitive processes is often not clear and currently limits the interpretation of MRI data.

Dr. Uludağ’s laboratory combines artificial intelligence approaches with 1.5 and 3T MRI big data in order to answer fundamental neuroscience questions and to develop biomarkers for clinical applications. Furthermore, he uses deep learning methods and generative models to improve the effectivity of MR image acquisition and reconstruction, promising to advance our understanding of the physical and physiological basis of MRI. As Co-Director of the Slaight Family Centre for Advanced MRI at the Toronto Western Hospital, Dr. Uludağ advises the clinical research groups in their studies of the brain and spine. Also, he sits on the editorial board of five leading neuroimaging journals.  

In addition, the research interests of Dr. Uludağ’s laboratory include studying cognition and anatomy in the human brain using Ultra-High Field human MRI scanners (7 and 9.4 Tesla). Ultra-high field MRI is an enabling technology that is increasingly used by researchers and clinicians for human neuroimaging to ask novel questions about brain structure and function. His lab works on quantitative anatomical and functional MRI methods (for example, ASL, T1, T2*, and SWI) and applies these cutting-edge approaches on post mortem brains, healthy subjects and patients. Dr. Uludağ’s group will continue to work on 7T MRI data acquired at national and international MRI centres (specifically in Montreal, London (Ontario), Suwon (South Korea), Boston, Minneapolis and Maastricht) and perform advanced data analysis on this data, which is unprecedented in spatial resolution, contrast and information content.

Areas of investigation and development:
MRI Physics of novel contrasts
Physiological and physical basis of functional MRI
Application of artificial intelligence on MRI data of the human brain
Clinical applications using novel MRI contrasts and analysis methods (e.g. neurodegeneration, deep brain stimulation, diabetes, etc)
Methods development on ultra-high field MRI

Neuroimage. 2019 Sep 20;204:116209
Havlicek M, Uludağ K
Alzheimers Dement (Amst). 2019 Dec;11:538-549
Düzel E, Acosta-Cabronero J, Berron D, Biessels GJ, Björkman-Burtscher I, Bottlaender M, Bowtell R, Buchem MV, Cardenas-Blanco A, Boumezbeur F, Chan D, Clare S, Costagli M, de Rochefort L, Fillmer A, Gowland P, Hansson O, Hendrikse J, Kraff O, Ladd ME,...
Brain. 2019 Jul 20;:
Betts MJ, Kirilina E, Otaduy MCG, Ivanov D, Acosta-Cabronero J, Callaghan MF, Lambert C, Cardenas-Blanco A, Pine K, Passamonti L, Loane C, Keuken MC, Trujillo P, Lüsebrink F, Mattern H, Liu KY, Priovoulos N, Fliessbach K, Dahl MJ, Maaß A, Madelung CF,...
Sci Rep. 2018 Nov 20;8(1):17063
Kashyap S, Ivanov D, Havlicek M, Sengupta S, Poser BA, Uludağ K
Data Brief. 2018 Oct;20:415-418
van der Zwaag W, Buur PF, Fracasso A, van Doesum T, Uludağ K, Versluis M, Marques JP
Neuroimage. 2018 09;178:769-779
Huber L, Tse DHY, Wiggins CJ, Uludağ K, Kashyap S, Jangraw DC, Bandettini PA, Poser BA, Ivanov D
Neuroimage Clin. 2018;18:231-244
Haast RAM, Ivanov D, IJsselstein RJT, Sallevelt SCEH, Jansen JFA, Smeets HJM, de Coo IFM, Formisano E, Uludağ K
Neuroimage. 2018 08 01;176:41-55
van der Zwaag W, Buur PF, Fracasso A, van Doesum T, Uludağ K, Versluis MJ, Marques JP
Hum Brain Mapp. 2018 07;39(7):2812-2827
Marquardt I, Schneider M, Gulban OF, Ivanov D, Uludağ K
Hum Brain Mapp. 2018 06;39(6):2412-2425
Haast RAM, Ivanov D, Uludağ K


Koerner Scientist in MR Imaging, Joint Department of Medical Imaging and Krembil Brain Institute, University Health Network
Co-Director, Slaight Centre for Advanced MRI, University Health Network
Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Korea
Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Korea