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Abstract #2209

Validation of Multi-modality Image Registration for Glioblastoma Treatments on the MR-Linac

Tom Gribilas1, Liam S.P. Lawrence2, Angus Z Lau2, Ryan Oglesby3, James Stewart4, Greg Stanisz2, and Brige Paul Chugh5
1Mathematics and Statistics, McMaster University, Hamilton, ON, Canada, 2Medical Biophysics, University of Toronto, Toronto, ON, Canada, 3Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins Medicine, Baltimore, MD, United States, 4Radiation Oncology, , Sunnybrook Health Sciences Centre, Toronto, ON, Canada, 5Sunnybrook Health Sciences Centre, Toronto, ON, Canada

Synopsis

Keywords: MR-Guided Radiotherapy, Radiotherapy, Multi-modality Image Registration, MR-Linac

Motivation: Improve consistency, efficiency and accuracy of verifying image registrations, which are a potential major failure mode in MR-Linac treatments.

Goal(s): Develop a quantitative method for automatically verifying image co-registration.

Approach: Brain MR and CT image registrations were assessed using five different similarity metrics. Validation of registration involved comparing the vendor-provided registration algorithm with an independent algorithm to identify the most useful image similarity metrics.

Results: A process for automated registration analysis was developed. SSIM and MSE were the most promising metrics for measuring registration accuracy.

Impact: A new tool was developed for verifying the accuracy of registration of MR images, which could be used to automatically detect inaccurate registrations during the treatment workflow on the MR-Linac.

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