Review and recommendations on deformable image registration uncertainties for radiotherapy applications

Publikation: Beitrag in FachzeitschriftÜbersichtsartikel (Review)BeigetragenBegutachtung

Beitragende

  • Lena Nenoff - , Massachusetts General Hospital, OncoRay - National Centre for Radiation Research in Oncology (Autor:in)
  • Florian Amstutz - , Department of Radiation Physics (Autor:in)
  • Martina Murr - , Eberhard Karls Universität Tübingen (Autor:in)
  • Ben Archibald-Heeren - , Icon Cancer Centres (Autor:in)
  • Marco Fusella - , Policlinico Abano Terme (Autor:in)
  • Mohammad Hussein - , Metrology for Medical Physics (Autor:in)
  • Wolfgang Lechner - , Medizinische Universität Wien (Autor:in)
  • Ye Zhang - , Paul Scherrer Institute (Autor:in)
  • Greg Sharp - , Massachusetts General Hospital (Autor:in)
  • Eliana Vasquez Osorio - , University of Manchester (Autor:in)

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

OriginalspracheEnglisch
FachzeitschriftPhysics in medicine and biology
Jahrgang68
Ausgabenummer24
PublikationsstatusVeröffentlicht - 13 Dez. 2023
Peer-Review-StatusJa

Externe IDs

PubMedCentral PMC10725576
Scopus 85180267306

Schlagworte

Schlagwörter

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