A hybrid patient-specific biomechanical model based image registration method for the motion estimation of lungs
- Submitting institution
-
Manchester Metropolitan University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2337
- Type
- D - Journal article
- DOI
-
10.1016/j.media.2017.04.003
- Title of journal
- Medical Image Analysis
- Article number
- -
- First page
- 87
- Volume
- 39
- Issue
- -
- ISSN
- 1361-8415
- Open access status
- Compliant
- Month of publication
- April
- Year of publication
- 2017
- URL
-
https://e-space.mmu.ac.uk/618426/
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
5
- Research group(s)
-
A - Data Science
- Citation count
- 13
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work proposes a novel hybrid image registration approach for lung respiration motion prediction resulting from collaborations in the UK (d.hawkes@ucl.ac.uk) and China (donghua@tongji.edu.cn). The generalisability of this work provides added value in medical image analysis with potential applications in adaptive radiotherapy. This work was referenced as the ‘state-of-the art’ method in a number of articles for comparison study (Peng et al. Radiotherapy and Oncology 2020, Fu et al. Medical Physics 2018, Chassagnon et al. Radiology 2019). It led to funded projects (Academy of Medical Sciences- NAF\R1\180371, Newton Fund Institutional Links grant-ID:33243891).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -