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Review and recommendations on deformable image registration uncertainties for radiotherapy applications.

Lena NenoffFlorian AmstutzMartina MurrBen Archibald-HeerenMarco FusellaMohammad HusseinWolfgang LechnerYe ZhangGregory C SharpEliana Vasquez Osorio
Published in: Physics in medicine and biology (2023)
Deformable image registration (DIR) is a versatile tool used in many applications in radiotherapy (RT). DIR algorithms have been implemented in many commercial treatment planning systems providing accessible and easy-to-use solutions. However, the geometric uncertainty of DIR can be large and difficult to quantify, resulting in barriers to clinical practice. Currently, there is no agreement in the RT community on how to quantify these uncertainties and determine thresholds that distinguish a good DIR result from a poor one. This review summarises the current literature on sources of DIR uncertainties and their impact on RT applications. Recommendations are provided on how to handle these uncertainties for patient-specific use, commissioning, and research. Recommendations are also provided for developers and vendors to help users to understand DIR uncertainties and make the application of DIR in RT safer and more reliable.
Keyphrases
  • clinical practice
  • deep learning
  • early stage
  • radiation therapy
  • healthcare
  • systematic review
  • locally advanced
  • mental health
  • squamous cell carcinoma
  • radiation induced