Dr Nicholas Hindley
Image X Institute
Sydney School of Health Sciences
Dr Nicholas Hindley as a postdoctoral researcher at the Image X Institute. In 2020, Dr Hindley won the prestigious Fulbright Scholarship which enabled him to work as a visiting researcher at Harvard Medical School. During his time at Harvard, Dr Hindley developed an artificial intelligence system that aids scientific discovery by approximating unknown physical laws.His research at Image X currently centres on applications of image processing and machine learning in image-guided radiotherapy.
Image-guided radiotherapy, lung cancer, motion tracking, image registration, machine learning
Statistics, data science, machine learning
VALKIM: Validation of Markerless Image Guidance using Intrafraction Kilovoltage X-ray Imaging
MAGIK: Markerless Image Guidance using Intrafraction Kilovoltage X-ray Imaging
Voxelmap: A deep learning framework for 2D-3D image registration and volumetric imaging during cancer radiation therapy
2023 Thesis Excellence Award, Faculty of Medicine and Health, University of Sydney
2021 Finalist, Worldwide Innovations in Medical Physics at the Winter Institute of Medical Physics
2020 New Entrant Stipend, International Society for Magnetic Resonance in Medicine
2020 Fulbright Future Scholarship, Australian-American Fulbright Commission and the Kinghorn Foundation
2019 Most Outstanding Presentation, MedPhys2019, Australasian College of Physical Scientists and Engineers in Medicine
2019 Most Outstanding Presentation, Clinical Research and Imaging, 2019 Postgraduate and ECR Cancer Research Symposium, University of Sydney
2019 Cancer Research Network Travel Grant, University of Sydney
2018 Postgraduate Research Support Scheme, University of Sydney
2018 Postgraduate Research Supplementary Scholarship in Deep Learning for Imaging the Human Anatomy, ACRF Image X Institute
2018 Research Training Program Stipend Scholarship, Australian Government
2011 Honours Scholarship, Children’s Cancer Institute Australia
2011 Scholarship for Innovation in Applied Science, Spruson and Ferguson
2011 Dean’s List Award for Outstanding Research, UNSW Faculty of Medicine
Publications
Journals
- Hindley, N., DeVience, S., Zhang, E., Cheng, L., Rosen, M. (2024). A statistical learning framework for mapping indirect measurements of ergodic systems to emergent properties. Journal of Magnetic Resonance Open, 19. [More Information]
- Reynolds, T., Dillon, O., Ma, Y., Hindley, N., Stayman, J., Bazalova-Carter, M. (2024). Investigating 4D respiratory cone-beam CT imaging for thoracic interventions on robotic C-arm systems: a deformable phantom study. Physical and Engineering Sciences in Medicine. [More Information]
- Hindley, N., Shieh, C., Keall, P. (2023). A patient-specific deep learning framework for 3D motion estimation and volumetric imaging during lung cancer radiotherapy. Physics in Medicine and Biology, 68(14). [More Information]
2024
- Hindley, N., DeVience, S., Zhang, E., Cheng, L., Rosen, M. (2024). A statistical learning framework for mapping indirect measurements of ergodic systems to emergent properties. Journal of Magnetic Resonance Open, 19. [More Information]
- Reynolds, T., Dillon, O., Ma, Y., Hindley, N., Stayman, J., Bazalova-Carter, M. (2024). Investigating 4D respiratory cone-beam CT imaging for thoracic interventions on robotic C-arm systems: a deformable phantom study. Physical and Engineering Sciences in Medicine. [More Information]
2023
- Hindley, N., Shieh, C., Keall, P. (2023). A patient-specific deep learning framework for 3D motion estimation and volumetric imaging during lung cancer radiotherapy. Physics in Medicine and Biology, 68(14). [More Information]
- Waddington, D., Hindley, N., Koonjoo, N., Chiu, C., Reynolds, T., Liu, P., Zhu, B., Bhutto, D., Paganelli, C., Keall, P., et al (2023). Real-time radial reconstruction with domain transform manifold learning for MRI-guided radiotherapy. The Journal of Experimental Medicine. [More Information]
2021
- Lydiard, S., Pontré, B., Hindley, N., Lowe, B., Sasso, G., Keall, P. (2021). MRI-guided cardiac-induced target motion tracking for atrial fibrillation cardiac radioablation: MRIg tracking: AF CR targets. Radiotherapy and Oncology, 164, 138-145. [More Information]
- Hindley, N., Lydiard, S., Shieh, C., Keall, P. (2021). Proof-of-concept for x-ray based real-time image guidance during cardiac radioablation. Physics in Medicine and Biology, 66(17), 175010. [More Information]
2019
- Hindley, N., Keall, P., Booth, J., Shieh, C. (2019). Real-time direct diaphragm tracking using kV imaging on a standard linear accelerator. Medical Physics, 46(10), 4481-4489. [More Information]
Selected Grants
2025
- Real-time target and organ tracking for cardiac-sparing adaptive radiotherapy, Hindley N, National Heart Foundation of Australia/Honorary Postdoctoral Fellowship
2024
- From relativity to respiration: How ideas from Einstein's general theory enable adaptative radiation therapy for lung cancer patients, Hindley N, Shieh C, Cancer Australia/Priority Driven Collaborative Cancer Research Scheme