Validation of Multi-modality Image Registration for Glioblastoma Treatments on the 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.
Authors
- Angus Lau (ORCID: https://orcid.org/0000-0002-9604-394X)
- Ryan T. Oglesby (ORCID: https://orcid.org/0000-0002-6972-8214)
- Liam Lawrence (ORCID: https://orcid.org/0000-0003-2070-8308)
- Greg J. Stanisz (ORCID: https://orcid.org/0000-0002-9488-7612)
- Brige Chugh
- James Stewart (ORCID: https://orcid.org/0000-0002-3134-3415)
- Tom Gribilas
Publication Details
- Journal
- Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
- Published
- 2025-09-16
- DOI
- https://doi.org/10.58530/2025/2209
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00