mDixon-Based Synthetic CT Generation for PET Attenuation Correction on Abdomen and Pelvis Jointly Using Transfer Fuzzy Clustering and Active Learning-Based Classification
- Submitting institution
-
The University of Leicester
- Unit of assessment
- 12 - Engineering
- Output identifier
- 2189
- Type
- D - Journal article
- DOI
-
10.1109/TMI.2019.2935916
- Title of journal
- IEEE Transactions on Medical Imaging
- Article number
- -
- First page
- 819
- Volume
- 39
- Issue
- 4
- ISSN
- 0278-0062
- Open access status
- Not compliant
- Month of publication
- August
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
https://doi.org/10.1109/TMI.2019.2935916
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
18
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The first to propose a transfer fuzzy clustering and active learning-based classification (TFC-ALC) which is capable of better synthetic CT generation than methods currently in use on the challenging abdomen using only common Dixon-based scanning. The study overcomes the obstacle that synthetic CT does not provide fine resolution in segmenting fat, bone, air and soft tissue. This paper has stimulated work on synthetic CT e.g. by W Huang (J Med Syst, 2020), and partially helped the work e.g. by J Zhang et al (JAIHC, 2020), P Qian (ACM TOMM, 2020), Y Zhang (IEEE TCSS, 2020), E Wallsten (EJNMMI Physics, 2020).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
