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Deforming to Best Practice: Key considerations for deformable image registration in radiotherapy

journal contribution
posted on 2024-11-02, 14:21 authored by Jeffrey Barber, Johnson Yuen, Michael Jameson, Adam Yeo
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.

History

Related Materials

  1. 1.
    DOI - Is published in 10.1002/jmrs.417
  2. 2.
    ISSN - Is published in 20513895

Journal

Journal of Medical Radiation Sciences

Volume

67

Issue

4

Start page

318

End page

332

Total pages

15

Publisher

John Wiley and Sons

Place published

United Kingdom

Language

English

Copyright

© 2020 The Authors. open access article under the terms of the Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)

Former Identifier

2006102615

Esploro creation date

2022-11-25

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