Accelerated high-resolution photoacoustic tomography via compressed sensing.
- Submitting institution
-
University College London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 14122
- Type
- D - Journal article
- DOI
-
10.1088/1361-6560/61/24/8908
- Title of journal
- Phys Med Biol
- Article number
- -
- First page
- 8908
- Volume
- 61
- Issue
- 24
- ISSN
- 1361-6560
- Open access status
- Compliant
- Month of publication
- December
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
https://dl.acm.org/action/downloadSupplement?doi=10.1145%2F3290370&file=a57-gorogiannis.webm&download=true
- 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
-
7
- Research group(s)
-
-
- Citation count
- 64
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Variational methods are the main tool for solution of incomplete data problems ubiquitous in tomography in the absence of analytical inversion. Building on the adjoint operator (Arridge et al’16), this paper is the first to solve sparse data problem in PAT using variational methods with TV regularisation. Up to 8-fold speed up in acquisition time is demonstrated on real data in 3d. Instrumental for work on dynamic tomography Lucka et al’18, PhD project B.Pan “Curvelets in photoacoustic tomography”, PAT and ultrasound small animal imaging grant EP/T014369/1 (£1,233,566). Presented in keynotes at IMA Inverse Problems’17, SIAM UKIE Annual Meeting’17.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -