Open AccessDeforming to Best Practice: Key considerations for deformable image registration in radiotherapy
Author(s) -
Barber Jeffrey,
Yuen Johnson,
Jameson Michael,
Schmidt Laurel,
Sykes Jonathan,
Gray Alison,
Hardcastle Nicholas,
Choong Callie,
Poder Joel,
Walker Amy,
Yeo Adam,
ArchibaldHeeren Ben,
Harrison Kristie,
Haworth Annette,
Thwaites David
Publication year - 2020
Publication title -
journal of medical radiation sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.484
H-Index - 18
eISSN - 2051-3909
pISSN - 2051-3895
DOI - 10.1002/jmrs.417
Subject(s) - best practice , image registration , quality assurance , medical physics , computer science , process (computing) , radiation treatment planning , key (lock) , automation , segmentation , quality (philosophy) , radiation therapy , medicine , artificial intelligence , radiology , image (mathematics) , pathology , mechanical engineering , economics , operating system , philosophy , external quality assessment , computer security , management , epistemology , engineering
Abstract Image registration is a process that underlies many new techniques in radiation oncology – from multimodal imaging and contour propagation in treatment planning to dose accumulation throughout treatment. Deformable image registration (DIR) is a subset of image registration subject to high levels of complexity in process and validation. A need for local guidance to assist in high‐quality utilisation and best practice was identified within the Australian community, leading to collaborative activity and workshops. This report communicates the current limitations and best practice advice from early adopters to help guide those implementing DIR in the clinic at this early stage. They are based on the state of image registration applications in radiotherapy in Australia and New Zealand (ANZ), and consensus discussions made at the ‘Deforming to Best Practice’ workshops in 2018. The current status of clinical application use cases is presented, including multimodal imaging, automatic segmentation, adaptive radiotherapy, retreatment, dose accumulation and response assessment, along with uptake, accuracy and limitations. Key areas of concern and preliminary suggestions for commissioning, quality assurance, education and training, and the use of automation are also reported. Many questions remain, and the radiotherapy community will benefit from continued research in this area. However, DIR is available to clinics and this report is intended to aid departments using or about to use DIR tools now.