Dr Nyoman Kurniawan

Research Fellow

Centre for Advanced Imaging

Overview

Dr Nyoman Kurniawan is a Senior Research Fellow in the Centre for Advanced Imaging and the Facility Manager for Preclincal 16.4T Microimaging 9.4T MRI scanners.

Dr Kurniawan’s research areas are:

  • Diffusion magnetic resonance imaging of mouse neuroanatomy, with view to study neurological disease model, including:
    • developmental abnormalities
    • spinal cord diseases
  • Development of 3D mouse brain, human spinal cord and cephalopod brain atlases using high resolution structural and diffusion MRI
  • Development of MR methods for nephron imaging
  • Application of MRI for agriculture imaging

Research Interests

  • Spinal cord MRI
    Using MRI to study degeneration and recovery in animal models of MS (EAE) and translation to clinical settings.
  • Kidney imaging
    Development of new MRI methods to image nephrons
  • Development of structural connectome for animal model
    Using diffusion MRI and fibre tracking to analyse brain wiring in animal models (rodents, fish, chepalopods)
  • Development of MRI methods for agriculture imaging
    Using MRI to characterise fruit maturation/ripening and root responses in crops.

Research Impacts

Dr Kurniawan research focus is development of new MRI approaches for characterising animal models and ex vivo tissue specimens with the aim of relating the MRI signal to underlying tissue microstructures and processes. Insights gained with this approach have the potential to translate into improved use of MRI in humans. For example, he is working with clinicians at the Jamieson Trauma Institute to develop diffusion methods to measure brachial plexus injury.

Dr Kurniawan has a number of national and international collaborations, in the area of tumour microimaging (University of Sydney); multimodal MR imaging of Alzheimer brain (University of Southern California, USA), developing human spinal cord atlas (Polytechnique Montreal, Canada), and nephron imaging (Monash and University of Nottingham, UK).

Dr Kurniawan also works tangentialy in the applications of MRI to characterise agricultural products. He has been working with Uniquely Australian Foods at QAFFI, using MRI to characterise native fruit maturation and ripening process for commercialisation. Recently, he has obtained research infrastructure funding to establish Agriculture MRI facility at the Gatton Campus.

Qualifications

  • Doctor of Philosophy, The University of Queensland
  • Bachelor of Science, The University of Queensland
  • Bachelor of Science (Honours), The University of Queensland

Publications

  • Zhou, Xiaoqing Alice, Blackmore, Daniel G., Zhuo, Junjie, Nasrallah, Fatima A., To, XuanVinh, Kurniawan, Nyoman D., Carlisle, Alison, Vien, King-Year, Chuang, Kai-Hsiang, Jiang, Tianzi and Bartlett, Perry F. (2021). Neurogenic-dependent changes in hippocampal circuitry underlie the pro-cognitive effect of exercise in ageing mice. iScience, 24 (12) 103450, 103450. doi: 10.1016/j.isci.2021.103450

  • Chung, Wen-Sung, Kurniawan, Nyoman D. and Marshall, N. Justin (2021). Comparative brain structure and visual processing in octopus from different habitats. Current Biology, 32 (1), 97-110.e4. doi: 10.1016/j.cub.2021.10.070

  • Cohen-Adad, Julien, Alonso-Ortiz, Eva, Abramovic, Mihael, Arneitz, Carina, Atcheson, Nicole, Barlow, Laura, Barry, Robert L., Barth, Markus, Battiston, Marco, Büchel, Christian, Budde, Matthew, Callot, Virginie, Combes, Anna J. E., De Leener, Benjamin, Descoteaux, Maxime, de Sousa, Paulo Loureiro, Dostál, Marek, Doyon, Julien, Dvorak, Adam, Eippert, Falk, Epperson, Karla R., Epperson, Kevin S., Freund, Patrick, Finsterbusch, Jürgen, Foias, Alexandru, Fratini, Michela, Fukunaga, Issei, Gandini Wheeler-Kingshott, Claudia A. M., Germani, Giancarlo ... Xu, Junqian (2021). Author Correction: Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers. Scientific Data, 8 (1) 251, 251. doi: 10.1038/s41597-021-01044-0

View all Publications

Grants

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Supervision

  • Doctor Philosophy

  • Doctor Philosophy

  • Doctor Philosophy

View all Supervision

Available Projects

  • Nephrons are the basic functional unit of the kidney, an organ with a central role in maintaining homeostasis in the body. The number of nephrons in the kidneys and their microstructure reflect the success of renal development and the trajectory of renal health through life. Low nephron number increases the risk of chronic kidney disease, hypertension and cardiovascular disease.

    Current methods for nephron quantitation are limited to ex vivo methods which are labour intensive, affected by shrinkage or use contrast agents. Magnetic Resonance Imaging (MRI) has strong potential to characterise kidney microstructures, but in vivo it suffers from low image resolution and motion artefacts. This PhD project aims to develop novel methods for kidney MR image acquisitions and analyses using artificial intelligence (such as Deep Learning and super resolution methods) to allow characterisation of key components of nephrons, the glomeruli and tubules. It is expected that these new methods will play important roles in future kidney research and contribute to reducing Australia’s epidemic of chronic kidney disease.

    This is an opportunity to work with researchers at the Centre for Advanced Imaging, a leading imaging research facility in Australia, and The University of Queensland School of Information Technology and Electrical Engineering.

    The project will suit an enthusiastic and highly motivated student with a background in computer science, physics or engineering.

View all Available Projects

Publications

Book Chapter

  • Fry, B. G., Undheim, E. A. B., Jackson, T. N. W., Georgieva, D., Vetter, I., Calvete, J. J., Schieb, H., Cribb, B. W., Yang, D. C., Daly, N. L., Manchadi, M. L. Roy, Gutierrez, J. M., Roelants, K., Lomonte, B., Nicholson, G. M., Dziemborowicz, S., Lavergne, V., Ragnarsson, L., Rash, L. D., Mobli, M., Hodgson, W. C., Casewell, N. R., Nouwens, A., Wagstaff, S. C., Ali, S. A., Whitehead, D. L., Herzig, V., Monagle, P., Kurniawan, N. D. ... Sunagar, K. (2015). Research methods. Venomous reptiles and their toxins: evolution, pathophysiology and biodiscovery. (pp. 153-214) New York, NY, United States: Oxford University Press.

  • Boswell, Emma J., Kurniawan, Nyoman D. and Downing, A. Kristina (2011). Calcium-Binding EGF-Like Domains. Encyclopedia of Inorganic and Bioinorganic Chemistry. (pp. 1-19) Chichester, United Kingdom: John Wiley & Sons. doi: 10.1002/9781119951438.eibc0513

  • Peng, Hui, Thurecht, Kristofer, Hsu, Steven, Blakey, Idriss, Squires, Oliver, Kurniawan, Nyoman, Rose, Stephen and Whittaker, Andrew (2011). Effect of molecular architecture on the performance of (19)F NMR imaging agents. NMR Spectroscopy of Polymers: Innovative Strategies for Complex Macromolecules. (pp. 459-472) edited by H. N. Cheng, Tetsuo Asakur and Alan D. English. Washington, DC, United States: American Chemical Society. doi: 10.1021/bk-2011-1077.ch028

  • Boswell, Emma J., Kurniawan, Nyoman D. and Downing, A. Kristina (2006). Calcium-Binding EGF-Like Domains. Handbook of Metalloproteins. (pp. 1-18) Chichester, United Kingdom: John Wiley & Sons. doi: 10.1002/0470028637.met048

Journal Article

Conference Publication

Other Outputs

  • Egan, Gary F., Reutens, David, Galloway, Graham J., Watson, Charles, Andrew Janke, Kurniawan, Nyoman D. and Ullmann, Jeremy F. (2015). Average wild-type C57BL/6J mouse 3D MRI Basal Ganglia labels. The University of Queensland. (Dataset) doi: 10.14264/uql.2015.867

  • Egan, Gary F., Reutens, David, Bartlett, Perry F., Galloway, Graham J., Paxinos, George, Petrou, Steven, Watson, Charles, Keller, Marianne D., Andrew Janke and Kurniawan, Nyoman D. (2015). Average wild-type C57BL/6J mouse 3D MRI brain image. The University of Queensand. (Dataset) doi: 10.14264/uql.2015.864

  • Egan, Gary F., Reutens, David, Galloway, Graham J., Watson, Charles, Andrew Janke, Kurniawan, Nyoman D. and Ullmann, Jeremy F. (2015). Average wild-type C57BL/6J mouse 3D MRI cerebellum labels. The University of Queensand. (Dataset) doi: 10.14264/uql.2015.866

  • Egan, Gary F., Reutens, David, Galloway, Graham J., Watson, Charles, Andrew Janke, Kurniawan, Nyoman D. and Ullmann, Jeremy F. (2015). Average wild-type C57BL/6J mouse 3D MRI cortex labels. The University of Queensand. (Dataset) doi: 10.14264/uql.2015.865

  • Egan, Gary F., Reutens, David, Bartlett, Perry F., Galloway, Graham J., Petrou, Steven, Watson, Charles, Keller, Marianne D., Andrew Janke, Kurniawan, Nyoman D. and Ullmann, Jeremy F. (2015). Average wild-type C57BL/6J mouse 3D MRI non-symmetric brain image. Australian Mouse Brain Mapping Consortium. (Dataset) doi: 10.14264/uql.2015.863

  • Janke, Andrew, Kurniawan, Nyoman D., Ullmann, Jeremy F., Reutens, David, Galloway, Graham J. and Watson, Charles (2013). Average wild-type C57BL/6J mouse 3D MRI hippocampus labels. The University of Queensland. (Dataset) doi: 10.14264/uql.2015.868

  • Kurniawan, Nyoman, Dana. (2002). Dissection of the human low-density lipoprotein receptor structure using NMR spectroscopy. PhD Thesis, School of Molecular and Microbial Sciences, The University of Queensland.

PhD and MPhil Supervision

Current Supervision

  • Doctor Philosophy — Principal Advisor

  • Doctor Philosophy — Principal Advisor

    Other advisors:

  • Doctor Philosophy — Principal Advisor

    Other advisors:

Completed Supervision

Possible Research Projects

Note for students: The possible research projects listed on this page may not be comprehensive or up to date. Always feel free to contact the staff for more information, and also with your own research ideas.

  • Nephrons are the basic functional unit of the kidney, an organ with a central role in maintaining homeostasis in the body. The number of nephrons in the kidneys and their microstructure reflect the success of renal development and the trajectory of renal health through life. Low nephron number increases the risk of chronic kidney disease, hypertension and cardiovascular disease.

    Current methods for nephron quantitation are limited to ex vivo methods which are labour intensive, affected by shrinkage or use contrast agents. Magnetic Resonance Imaging (MRI) has strong potential to characterise kidney microstructures, but in vivo it suffers from low image resolution and motion artefacts. This PhD project aims to develop novel methods for kidney MR image acquisitions and analyses using artificial intelligence (such as Deep Learning and super resolution methods) to allow characterisation of key components of nephrons, the glomeruli and tubules. It is expected that these new methods will play important roles in future kidney research and contribute to reducing Australia’s epidemic of chronic kidney disease.

    This is an opportunity to work with researchers at the Centre for Advanced Imaging, a leading imaging research facility in Australia, and The University of Queensland School of Information Technology and Electrical Engineering.

    The project will suit an enthusiastic and highly motivated student with a background in computer science, physics or engineering.