Dr. Chugh’s research is focused on developing methods for accurate Image-Guided Radiation Therapy (IGRT) using Magnetic Resonance Imaging (MRI). MRI provides invaluable structural and functional information that can help tailor treatments to patient-specific tumour biology. With the advent of integrated MRI-RT technologies such as MRI simulators and MR-linacs, it is now possible to incorporate a wide variety of MRI parameters for IGRT. The scope of application is vast, including tumour detection and staging, treatment simulation, guiding positioning, monitoring treatment response, providing data for adaptive radiation therapy (ART) as well as surveillance and prognosis.
Education
- B.Sc., 2002, Mathematics and Physics, University of Toronto, Canada
- M.Sc., 2004, Physics, University of Toronto, Canada
- Ph.D., 2012, Medical Biophysics, University of Toronto, Canada
Appointments & Affiliations
- Affiliate scientist, Physical Sciences, Odette Cancer Research Program, Sunnybrook Research Institute
- Medical physicist, Odette Cancer Centre, Sunnybrook Health Sciences Centre
- Assistant professor, Department of Radiation Oncology, Faculty of Medicine, University of Toronto
- Adjunct professor, Department of physics, Faculty of Science, Toronto Metropolitan University
Research Foci
- Image-guided radiation therapy
- Machine learning
- Quality Assurance (QA) phantom design
Affiliated Labs & Programs
Selected Publications
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Moore-Palhares, D., Ho, L., Lu, L., Chugh, B., Vesprini, D., Karam, I., ... & Czarnota, G. J. (2023). Clinical implementation of magnetic resonance imaging simulation for radiation oncology planning: 5 year experience. radiation oncology, 18(1), 27.
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Oglesby, R. T., Lam, W. W., Ruschin, M., Holden, L., Sarfehnia, A., Yeboah, C., ... & Chugh, B. P. (2022). Skull phantom‐based methodology to validate MRI co‐registration accuracy for Gamma Knife radiosurgery. Medical Physics, 49(11), 7071-7084.
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Hemsley, M., Chugh, B., Ruschin, M., Lee, Y., Tseng, C. L., Stanisz, G., & Lau, A. (2020). Deep generative model for synthetic-CT generation with uncertainty predictions. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part I 23 (pp. 834-844). Springer International Publishing.
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Tijssen, R. H., Philippens, M. E., Paulson, E. S., Glitzner, M., Chugh, B., Wetscherek, A., ... & van der Heide, U. A. (2019). MRI commissioning of 1.5 T MR-linac systems–a multi-institutional study. Radiotherapy and Oncology, 132, 114-120.
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Soliman, A. S., Burns, L., Owrangi, A., Lee, Y., Song, W. Y., Stanisz, G., & Chugh, B. P. (2017). A realistic phantom for validating MRI‐based synthetic CT images of the human skull. Medical physics, 44(9), 4687-4694.