Western Sydney University
About
Western Sydney University’s (WSU) Biomedical Magnetic Resonance Facility houses world-class preclinical imaging capabilities, including state-of-the-art 11.7 T and 14.1 T Bruker spectrometers, a Fast Field Cycling NMR, and a Quantum GX MicroCT, co-located with PC2 facilities and a comprehensive large animal house.
We offer unique provisions for small animal imaging under anaesthesia, utilising specialised equipment with respiratory monitoring and cardiac gating.
Our world-leading experts in diffusion MRI, in both conventional and q-space methods, enable advanced insights, analysis and interpretation.
Capabilities
Location
Biomedical Magnetic Resonance Facility
Western Sydney University, Campbelltown Campus
Narellan Rd and Gilchrist Dr
Campbelltown NSW 2560
Node Director
Latest news
National Imaging Facility receives $1.6m NSW Government funding to advance medical research
24 June 2024
National Imaging Facility granted $28m NCRIS funding to strengthen Australia’s advanced imaging network and bolster health research
19 October 2023
World-first: Cancer Council supports Western Sydney Uni’s AI enhanced cancer research
14 July 2022
Publications
Targeting of C-ROS-1 Activity Using a Controlled Release Carrier to Treat Craniosynostosis in a Preclinical Model of Saethre-Chotzen Syndrome
Esther Camp, Laura Gonzalez Garcia, Clara Pribadi, Sharon Paton, Krasimir Vasilev, Peter Anderson, Stan Gronthos
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A first-in-class dual-chelator theranostic agent designed for use with imaging-therapy radiometal pairs of different elements
James L. Wood, Saikat Ghosh, Zachary H. Houston, Nicholas L. Fletcher, James Humphries, Karine Mardon, Dewan T. Akhter, William Tieu, Alesia Ivashkevich, Michael P. Wheatcroft, Kristofer J. Thurecht and Rachel Codd
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Novel formylpeptide receptor 1/2 agonist limits hypertension-induced cardiovascular damage
Jaideep Singh, Kristy L Jackson, Haoyun Fang, Audrey Gumanti, Bethany Claridge, Feng Shii Tang, Helen Kiriazis, Ekaterina Salimova, Alex M Parker, Cameron Nowell, Owen L Woodman, David W Greening, Rebecca H Ritchie, Geoffrey A Head, Cheng Xue Qin
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Recovering high-quality fiber orientation distributions from a reduced number of diffusion-weighted images using a model-driven deep learning architecture
Joseph J. Bartlett, Catherine E. Davey, Leigh A. Johnston, Jinming Duan
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