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Scientist profiles A-F

SRI Profiles

Brige Chugh
Brige Chugh, PhD, DABR

Affiliate scientist

Sunnybrook Health Sciences Centre
2075 Bayview Ave., Room TG 217
Toronto, ON
M4N 3M5

Phone: 416-480-6100 ext. 685380
Fax: 416-480-6801

Administrative Assistant: Miranda Lau
Phone:
416-480-6170
Email:
miranda.lau@sunnybrook.ca 

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 and Affiliations:

  • Affiliate scientist, Physical SciencesOdette Cancer Research Program, Sunnybrook Research Institute
  • Medical physicist, Odette Cancer Centre, Sunnybrook Health Sciences Centre
  • Assistance professor, department of radiation oncology, Faculty of Medicine, University of Toronto
  • Adjunct professor, department of physics, Faculty of Science, Ryerson University

Research Foci:

  • Image-guided radiation therapy
  • Quality Assurance (QA) phantom design
  • Machine learning

Research Summary:

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.

Selected Publications:

See current publications list at PubMed.

  1. Lawrence, LSP, Chan RW, Chen H, Keller B, Stewart J, Ruschin M, Chugh B, Campbell M, Theriault A, Stanisz GJ, MacKenzie S, Myrehaug S, Detsky J, Maralani PJ, Tseng CL, Czarnota GJ, Sahgal A, Lau AZ. Accuracy and precision of apparent diffusion coefficient measurements on a 1.5T MRLinac in central nervous system tumour patients. Radiotherapy and Oncology. 2021. doi:10.1016/j.radonc.2021.09.020
  2. Chan RW, Lawrence LSP, Oglesby RT, Chen H, Stewart J, Theriault A, Campbell M, Ruschin M, Myrehaug S, Atenafu EG, Keller B, Chugh B, MacKenzie S, Tseng CL, Detsky J, Maralani PJ, Czarnota GJ, Stanisz GJ, Sahgal A, Lau AZ. Chemical Exchange Saturation Transfer MRI in Central Nervous System Tumours on a 1.5T MR-Linac. Radiotherapy and Oncology. 162: 140-149 (2021) doi:10.1016/j.radonc.2021.07.010
  3. Hemsley, M., Chugh, B., Ruschin, M., Lee, Y., Chia-Lin, T., Stanisz, G., Lau, A. Z. (2020). Deep Generative Model for Synthetic-CT Generation with Uncertainty Predictions. In: Martel A.L. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science, vol 12261. Springer, Cham. doi:10.1007/978-3-030-59710-8_81 
  4. Lim‐Reinders S, Keller BM, Sahgal A, Chugh B, Kim A. Measurement of Surface Dose in an MR-Linac with Optically Stimulated Luminescence Dosimeters for IMRT Beam Geometries. Med Phys. 2020 Jul;47(7):3133-3142. doi: 10.1002/mp.14185 
  5. Karami E, Soliman H, Ruschin M, Sahgal A, Myrehaug SD, Tseng CL, Czarnota GJ, Jabehdar-Maralani P, Chugh BP, Lau A, Stanisz GJ, Sadeghi-Naini A. Quantitative MRI Biomarkers of Stereotactic Radiotherapy Outcome in Brain Metastasis. Sci Rep 9, 19830 (2019) doi:10.1038/s41598-019-56185-5 

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