Literature DB >> 28376237

Use of image registration and fusion algorithms and techniques in radiotherapy: Report of the AAPM Radiation Therapy Committee Task Group No. 132.

Kristy K Brock1, Sasa Mutic2, Todd R McNutt3, Hua Li2, Marc L Kessler4.   

Abstract

Image registration and fusion algorithms exist in almost every software system that creates or uses images in radiotherapy. Most treatment planning systems support some form of image registration and fusion to allow the use of multimodality and time-series image data and even anatomical atlases to assist in target volume and normal tissue delineation. Treatment delivery systems perform registration and fusion between the planning images and the in-room images acquired during the treatment to assist patient positioning. Advanced applications are beginning to support daily dose assessment and enable adaptive radiotherapy using image registration and fusion to propagate contours and accumulate dose between image data taken over the course of therapy to provide up-to-date estimates of anatomical changes and delivered dose. This information aids in the detection of anatomical and functional changes that might elicit changes in the treatment plan or prescription. As the output of the image registration process is always used as the input of another process for planning or delivery, it is important to understand and communicate the uncertainty associated with the software in general and the result of a specific registration. Unfortunately, there is no standard mathematical formalism to perform this for real-world situations where noise, distortion, and complex anatomical variations can occur. Validation of the software systems performance is also complicated by the lack of documentation available from commercial systems leading to use of these systems in undesirable 'black-box' fashion. In view of this situation and the central role that image registration and fusion play in treatment planning and delivery, the Therapy Physics Committee of the American Association of Physicists in Medicine commissioned Task Group 132 to review current approaches and solutions for image registration (both rigid and deformable) in radiotherapy and to provide recommendations for quality assurance and quality control of these clinical processes.
© 2017 American Association of Physicists in Medicine.

Entities:  

Keywords:  adaptive radiotherapy; image fusion; image registration; quality assurance

Mesh:

Year:  2017        PMID: 28376237     DOI: 10.1002/mp.12256

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  145 in total

1.  Quadratic penalty method for intensity-based deformable image registration and 4DCT lung motion recovery.

Authors:  Edward Castillo
Journal:  Med Phys       Date:  2019-03-14       Impact factor: 4.071

2.  The VAMPIRE challenge: A multi-institutional validation study of CT ventilation imaging.

Authors:  John Kipritidis; Bilal A Tahir; Guillaume Cazoulat; Michael S Hofman; Shankar Siva; Jason Callahan; Nicholas Hardcastle; Tokihiro Yamamoto; Gary E Christensen; Joseph M Reinhardt; Noriyuki Kadoya; Taylor J Patton; Sarah E Gerard; Isabella Duarte; Ben Archibald-Heeren; Mikel Byrne; Rick Sims; Scott Ramsay; Jeremy T Booth; Enid Eslick; Fiona Hegi-Johnson; Henry C Woodruff; Rob H Ireland; Jim M Wild; Jing Cai; John E Bayouth; Kristy Brock; Paul J Keall
Journal:  Med Phys       Date:  2019-02-01       Impact factor: 4.071

3.  A Novel method to generate on-board 4D MRI using prior 4D MRI and on-board kV projections from a conventional LINAC for target localization in liver SBRT.

Authors:  Wendy Harris; Chunhao Wang; Fang-Fang Yin; Jing Cai; Lei Ren
Journal:  Med Phys       Date:  2018-06-13       Impact factor: 4.071

4.  Guidance on the use of PET for treatment planning in radiotherapy clinical trials.

Authors:  Lucy C Pike; Christopher M Thomas; Teresa Guerrero-Urbano; Andriana Michaelidou; Tony Greener; Elizabeth Miles; David Eaton; Sally F Barrington
Journal:  Br J Radiol       Date:  2019-08-23       Impact factor: 3.039

5.  Automatic large quantity landmark pairs detection in 4DCT lung images.

Authors:  Yabo Fu; Xue Wu; Allan M Thomas; Harold H Li; Deshan Yang
Journal:  Med Phys       Date:  2019-08-07       Impact factor: 4.071

6.  Multimodality image registration in the head-and-neck using a deep learning-derived synthetic CT as a bridge.

Authors:  Elizabeth M McKenzie; Anand Santhanam; Dan Ruan; Daniel O'Connor; Minsong Cao; Ke Sheng
Journal:  Med Phys       Date:  2020-01-02       Impact factor: 4.071

7.  Technical Note: Density correction to improve CT number mapping in thoracic deformable image registration.

Authors:  Jinzhong Yang; Yongbin Zhang; Zijian Zhang; Lifei Zhang; Peter Balter; Laurence Court
Journal:  Med Phys       Date:  2019-04-01       Impact factor: 4.071

8.  LungRegNet: An unsupervised deformable image registration method for 4D-CT lung.

Authors:  Yabo Fu; Yang Lei; Tonghe Wang; Kristin Higgins; Jeffrey D Bradley; Walter J Curran; Tian Liu; Xiaofeng Yang
Journal:  Med Phys       Date:  2020-02-26       Impact factor: 4.071

9.  Determination of reproducibility of end-exhaled breath-holding in stereotactic body radiation therapy.

Authors:  Motoharu Sasaki; Hitoshi Ikushima; Kanako Sakuragawa; Michihiro Yokoishi; Akira Tsuzuki; Wataru Sugimoto
Journal:  J Radiat Res       Date:  2020-11-16       Impact factor: 2.724

10.  4D-CT deformable image registration using multiscale unsupervised deep learning.

Authors:  Yang Lei; Yabo Fu; Tonghe Wang; Yingzi Liu; Pretesh Patel; Walter J Curran; Tian Liu; Xiaofeng Yang
Journal:  Phys Med Biol       Date:  2020-04-20       Impact factor: 3.609

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