Abstract
Ill-posed image recovery requires regularisation to ensure stability. The presented open-source regularisation toolkit consists of state-of-the-art variational algorithms which can be embedded in a plug-and-play fashion into the general framework of proximal splitting methods. The packaged regularisers aim to satisfy various prior expectations of the investigated objects, e.g., their structural characteristics, smooth or non-smooth surface morphology. The flexibility of the toolkit helps with the design of more advanced model-based iterative reconstruction methods for different imaging modalities while operating with simpler building blocks. The toolkit is written for CPU and GPU architectures and wrapped for Python/MATLAB. We demonstrate the functionality of the toolkit in application to Positron Emission Tomography (PET) and X-ray synchrotron computed tomography (CT).
| Original language | English |
|---|---|
| Title of host publication | 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine |
| Editors | Samuel Matej, Scott D. Metzler |
| Place of Publication | U. S. A. |
| Publisher | SPIE |
| Pages | 1-6 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781510628373 |
| DOIs | |
| Publication status | Published - 28 May 2019 |
| Event | 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, Fully3D 2019 - Philadelphia, USA United States Duration: 2 Jun 2019 → 6 Jun 2019 |
Publication series
| Name | Proceedings of SPIE - The International Society for Optical Engineering |
|---|---|
| Volume | 11072 |
| ISSN (Print) | 0277-786X |
| ISSN (Electronic) | 1996-756X |
Conference
| Conference | 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, Fully3D 2019 |
|---|---|
| Country | USA United States |
| City | Philadelphia |
| Period | 2/06/19 → 6/06/19 |
Keywords
- Iterative methods
- Model-based
- PET
- Proximal-dual
- Regularization
- X-ray CT
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering
Cite this
Versatile regularisation toolkit for iterative image reconstruction with proximal splitting algorithms. / Kazantsev, Daniil; Pasca, Edoardo; Basham, Mark; Turner, Martin; Ehrhardt, Matthias J.; Thielemans, Kris; Thomas, Benjamin A.; Ovtchinnikov, Evgueni; Withers, Philip J.; Ashton, Alun W.
15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine. ed. / Samuel Matej; Scott D. Metzler. U. S. A. : SPIE, 2019. p. 1-6 110722D (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11072).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Versatile regularisation toolkit for iterative image reconstruction with proximal splitting algorithms
AU - Kazantsev, Daniil
AU - Pasca, Edoardo
AU - Basham, Mark
AU - Turner, Martin
AU - Ehrhardt, Matthias J.
AU - Thielemans, Kris
AU - Thomas, Benjamin A.
AU - Ovtchinnikov, Evgueni
AU - Withers, Philip J.
AU - Ashton, Alun W.
PY - 2019/5/28
Y1 - 2019/5/28
N2 - Ill-posed image recovery requires regularisation to ensure stability. The presented open-source regularisation toolkit consists of state-of-the-art variational algorithms which can be embedded in a plug-and-play fashion into the general framework of proximal splitting methods. The packaged regularisers aim to satisfy various prior expectations of the investigated objects, e.g., their structural characteristics, smooth or non-smooth surface morphology. The flexibility of the toolkit helps with the design of more advanced model-based iterative reconstruction methods for different imaging modalities while operating with simpler building blocks. The toolkit is written for CPU and GPU architectures and wrapped for Python/MATLAB. We demonstrate the functionality of the toolkit in application to Positron Emission Tomography (PET) and X-ray synchrotron computed tomography (CT).
AB - Ill-posed image recovery requires regularisation to ensure stability. The presented open-source regularisation toolkit consists of state-of-the-art variational algorithms which can be embedded in a plug-and-play fashion into the general framework of proximal splitting methods. The packaged regularisers aim to satisfy various prior expectations of the investigated objects, e.g., their structural characteristics, smooth or non-smooth surface morphology. The flexibility of the toolkit helps with the design of more advanced model-based iterative reconstruction methods for different imaging modalities while operating with simpler building blocks. The toolkit is written for CPU and GPU architectures and wrapped for Python/MATLAB. We demonstrate the functionality of the toolkit in application to Positron Emission Tomography (PET) and X-ray synchrotron computed tomography (CT).
KW - Iterative methods
KW - Model-based
KW - PET
KW - Proximal-dual
KW - Regularization
KW - X-ray CT
UR - http://www.scopus.com/inward/record.url?scp=85074265606&partnerID=8YFLogxK
U2 - 10.1117/12.2534289
DO - 10.1117/12.2534289
M3 - Conference contribution
T3 - Proceedings of SPIE - The International Society for Optical Engineering
SP - 1
EP - 6
BT - 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine
A2 - Matej, Samuel
A2 - Metzler, Scott D.
PB - SPIE
CY - U. S. A.
ER -