David A. Jaffray
PhD, FCCPM, FASTRO
Professor and Senior Scientist; Former Chief Technology Officer, University Health Network
👥Biography 个人简介
David A. Jaffray is a Professor and Senior Scientist at Princess Margaret Cancer Centre and the University of Toronto, where he has spent his career as one of the world's foremost medical physicists in radiation oncology technology development. He is most widely recognized as the inventor and developer of kilovoltage cone-beam CT (CBCT) integrated with linear accelerators—a technology that revolutionized image-guided radiation therapy (IGRT) by enabling daily three-dimensional imaging of patient anatomy immediately before and after treatment delivery. The CBCT-on-linac system is now standard equipment on virtually every modern radiotherapy linear accelerator worldwide, directly enabling the geometric precision required for IMRT, SBRT, and stereotactic radiosurgery. Jaffray's research program at Princess Margaret has extended from CBCT development into a comprehensive program in adaptive radiotherapy, in which treatment plans are systematically modified throughout the treatment course in response to daily imaging evidence of anatomical change, tumor shrinkage, or organ motion. He was a founding scientific contributor to the MRI-linac concept, helping to establish the physical and engineering feasibility of integrating magnetic resonance imaging with therapeutic radiation delivery to achieve real-time soft-tissue image guidance. This vision has now materialized commercially in systems such as the ViewRay MRIdian and the Elekta Unity MR-linac, which are transforming adaptive radiotherapy in clinics worldwide. Beyond technology development, Jaffray has been a visionary leader in digital health and data science as applied to radiation oncology, advocating for the use of large electronic health record datasets, imaging repositories, and machine learning to optimize treatment quality and patient outcomes at the population level. He served as Chief Technology Officer of University Health Network, one of Canada's largest academic health systems, where he built cross-disciplinary programs linking radiation therapy innovation with biomedical engineering, data science, and digital pathology. He is a Fellow of both the Canadian College of Physicists in Medicine and ASTRO, and has received the ASTRO Science Council Award and numerous international prizes for medical physics innovation.
🧪Research Fields 研究领域
🎓Key Contributions 主要贡献
Kilovoltage Cone-Beam CT for IGRT
Invented and clinically translated kilovoltage CBCT integrated with the treatment linear accelerator, enabling three-dimensional volumetric imaging of patient anatomy immediately prior to radiotherapy delivery and fundamentally enabling the precision of modern IGRT.
MRI-Linac Technology Development
Pioneered the conceptual and engineering framework for combining MRI with radiation delivery in a single integrated system, enabling real-time soft-tissue visualization during treatment and on-table adaptive planning.
Adaptive Radiotherapy Systems
Developed automated adaptive radiotherapy workflows incorporating daily CBCT-based contour propagation, deformable image registration, and online plan adaptation, reducing geometric uncertainty and enabling dose escalation.
Digital Health and AI in Radiation Oncology
Led institutional and national initiatives to harness electronic health records, large imaging databases, and machine learning for quality assurance, treatment outcome prediction, and population-scale radiotherapy optimization.
Representative Works 代表性著作
Flat-panel cone-beam computed tomography for image-guided radiation therapy
International Journal of Radiation Oncology Biology Physics (2002)
Landmark paper describing the first implementation of kilovoltage CBCT integrated with a therapeutic linear accelerator, demonstrating volumetric imaging quality adequate for daily patient positioning.
Magnetic resonance-guided radiation therapy: The Elekta Atlantic project
Physics in Medicine and Biology (2014)
Described the engineering principles and clinical rationale for the hybrid MR-linac system, which enables real-time soft-tissue image guidance during radiation delivery.
Accurate automatic delineation of heterogeneous functional volumes in positron emission tomography for oncology applications
International Journal of Radiation Oncology Biology Physics (2011)
Developed automated PET-based tumor volume delineation methods to reduce interobserver variability and improve consistency in metabolic target definition.
Kilovoltage cone-beam CT: emerging technology for image-guided radiotherapy
Cancer/Radiothérapie (2006)
Comprehensive clinical review of kV-CBCT applications in image-guided radiotherapy, covering positioning accuracy, soft-tissue visibility, and integration into adaptive treatment workflows.
🏆Awards & Recognition 奖项与荣誉
📄Data Sources 数据来源
Last updated: 2026-04-05 | All information from publicly available academic sources
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