Odette Cancer Program

Administrative Assistant: Miranda Lau
Phone: 416-480-4622
Email: Miranda.Lau@sunnybrook.ca

Education:

• B.Sc., 1998, medical and health physics, McMaster University, Hamilton, Canada
• M.Sc., 2002, medical biophysics, University of Toronto, Canada
• PhD, 2006, medical radiation physics, Lund University, Lund, Sweden
• MCCPM, 2011, Canadian College of Physicists in Medicine, Canada

Appointments and Affiliations:

• Affiliate scientist, Physical Sciences, Odette Cancer Research Program, Sunnybrook Research Institute
• Medical physicist, department of medical physics, Sunnybrook Health Sciences Centre
• Assistant professor, department of radiation oncology, U of T
• Assistant professor, department of mechanical and industrial engineering, U of T
• Adjunct professor, department of biophysics, Ryerson University

Research Foci:

• Image-guided precision radiosurgery and radiotherapy
• Patient-centred technology innovation in radiosurgery
• MR-linac
• Automated planning using optimization and artificial intelligence

Research Summary:

The focus of Dr. Ruschin’s research is image-guided precision radiosurgery and radiotherapy, as well as automation for patient-centred care. The research has revolved around three themes.

Gamma Knife Icon

This brain tumour ablating device has traditionally relied on surgically placed frames for target localization. Dr. Ruschin was part of the team that developed the technology to enable an integrated cone-beam CT imaging system on the Gamma Knife. The addition of a cone-beam CT system enables nonsurgically placed frames, multi-session treatments and increased precision in target localization. Research has led to advanced image-processing routines to enhance the visualization of brain tissue, and research into dual-energy X-ray imaging ultimately may lead to direct tumour visualization.

MR-linac

The MR-linac combines 1.5T diagnostic quality MRI with linear accelerator technology for the ultimate in tumour visualization during radiotherapy. Dr. Ruschin’s focus has been to use MRI to track tumour response during treatment, and to contribute to the international understanding of how we can use MRI to adapt treatment on the MR-linac.

Automation using optimization and artificial intelligence

The role of automation in personalization of health care is a rapidly expanding field. Dr. Ruschin has investigated optimization algorithms to generate high quality treatment plans in radiosurgery. With the high volume of cases being treated annually at the Odette Cancer Centre, there is access to thousands of treatment plans, which can be harnessed with machine learning to generate sophisticated models of automated planning, and ultimately patient outcomes modelling that would lead to individualized plans for patients.

Selected Publications:

See current publications list at PubMed.

1. MacDonald L, Lee Y, Schasfoort J, Soliman H, Sahgal A, Ruschin M. Real-time infrared motion tracking analysis for patients treated with gated frameless image-guided stereotactic radiosurgery. Int J Radiat Oncol Biol Phys. 2019 Oct 23. pii: S0360-3016(19)33913-6. doi: 10.1016/j.ijrobp.2019.10.030.
2. Cevik M, Aleman DM, Lee Y, Berdyshev A, Nordstrom H, Riad S, Sahgal A, Ruschin M. Simultaneous optimization of isocenter locations and sector duration in radiosurgery. Phys Med Biol. 2018 Dec 11.
3. Ruschin M, Sahgal A, Tseng CL, Sonier M, Keller B, Lee Y. Dosimetric impact of using a virtual couch shift for correcting translational setup errors for brain patients on an integrated high-field MRI-Linac (MRL). Int J Radiat Oncol Biol Phys. 2017 Jul 1;98(3):699–708.
4. Hashemi M, Song W, Sahgal A, Lee Y, Huynh C, Grouza V, Nordstrom H, Eriksson M, Dorenlot A, Regis J, Mainprize J, Ruschin M. Simultaneous deblurring and iterative reconstruction of CBCT for image guided brain radiosurgery. Phys. Med. Biol. 2017 Apr 7;62(7):2521–2541.
5. Ruschin M, Komljenovic P, Ansell S, Cho YB, Bootsma G, Chung C, Ménard C, Jaffray D. Cone-beam CT image guidance system for a dedicated intra-cranial radiosurgery treatment unit. Int J Radiat Oncol Biol Phys. 2013 Jan 1;85(1):243–50.

Related Links:

• U of T profile, radiation oncology