Review and recommendations on deformable image registration uncertainties for radiotherapy applications

Research output: Contribution to journalReview articleContributedpeer-review

Contributors

  • Lena Nenoff - , Massachusetts General Hospital, OncoRay - National Centre for Radiation Research in Oncology (Author)
  • Florian Amstutz - , ETH Zurich (Author)
  • Martina Murr - , University of Tübingen (Author)
  • Ben Archibald-Heeren - , Icon Cancer Centre (Author)
  • Marco Fusella - , Abano Terme Polyclinic (Author)
  • Mohammad Hussein - , Metrology for Medical Physics (Author)
  • Wolfgang Lechner - , Medical University of Vienna (Author)
  • Ye Zhang - , Paul Scherrer Institute (Author)
  • Greg Sharp - , Massachusetts General Hospital (Author)
  • Eliana Vasquez Osorio - , University of Manchester (Author)

Abstract

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.

Details

Original languageEnglish
Article number24TR01
Number of pages42
JournalPhysics in medicine and biology
Volume68
Issue number24
Publication statusPublished - 21 Dec 2023
Peer-reviewedYes

External IDs

PubMedCentral PMC10725576
Scopus 85180267306

Keywords

Keywords

  • Algorithms, Humans, Image Processing, Computer-Assisted/methods, Radiotherapy Dosage, Radiotherapy Planning, Computer-Assisted/methods, Uncertainty

Library keywords