Abstract
Combined positron emission tomography (PET) and magnetic resonance imaging (MRI) scanners acquire simultaneously functional PET and anatomical or functional MRI data. As the data of both modalities are likely to show similar structures we aim to exploit this by joint reconstruction of PET and MRI. In a Bayesian formulation, this can be achieved by adding prior information encoding that the images of the two modalities are not independent. Structural similarity can be modeled by the alignment of the image gradients or equivalently their level sets being parallel. Therefore we can combine the objective functions of both modalities and penalize image pairs which do not have parallel level sets. Our results show that combining the reconstruction from heavily under-sampled MRI and noisy PET data can lead to less under-sampling artifacts in MRI images and better defined PET images.
| Original language | English |
|---|---|
| Title of host publication | 2014 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC |
| Place of Publication | U. S. A. |
| Publisher | IEEE |
| ISBN (Electronic) | 9781479960972 |
| DOIs | |
| Publication status | Published - 10 Mar 2016 |
| Event | IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2014 - Seattle, USA United States Duration: 8 Nov 2014 → 15 Nov 2014 |
Conference
| Conference | IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2014 |
|---|---|
| Country | USA United States |
| City | Seattle |
| Period | 8/11/14 → 15/11/14 |
Keywords
- compressed sensing
- image reconstruction
- magnetic resonance imaging
- positron emission tomography
ASJC Scopus subject areas
- Nuclear and High Energy Physics
- Radiology Nuclear Medicine and imaging
Cite this
Joint reconstruction of PET-MRI by parallel level sets. / Ehrhardt, Matthias J.; Thielemans, Kris; Pizarro, Luis; Markiewicz, Pawel; Atkinson, David; Ourselin, Sebastien; Hutton, Brian F.; Arridge, Simon R.
2014 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC . U. S. A. : IEEE, 2016. 7430895.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Joint reconstruction of PET-MRI by parallel level sets
AU - Ehrhardt, Matthias J.
AU - Thielemans, Kris
AU - Pizarro, Luis
AU - Markiewicz, Pawel
AU - Atkinson, David
AU - Ourselin, Sebastien
AU - Hutton, Brian F.
AU - Arridge, Simon R.
PY - 2016/3/10
Y1 - 2016/3/10
N2 - Combined positron emission tomography (PET) and magnetic resonance imaging (MRI) scanners acquire simultaneously functional PET and anatomical or functional MRI data. As the data of both modalities are likely to show similar structures we aim to exploit this by joint reconstruction of PET and MRI. In a Bayesian formulation, this can be achieved by adding prior information encoding that the images of the two modalities are not independent. Structural similarity can be modeled by the alignment of the image gradients or equivalently their level sets being parallel. Therefore we can combine the objective functions of both modalities and penalize image pairs which do not have parallel level sets. Our results show that combining the reconstruction from heavily under-sampled MRI and noisy PET data can lead to less under-sampling artifacts in MRI images and better defined PET images.
AB - Combined positron emission tomography (PET) and magnetic resonance imaging (MRI) scanners acquire simultaneously functional PET and anatomical or functional MRI data. As the data of both modalities are likely to show similar structures we aim to exploit this by joint reconstruction of PET and MRI. In a Bayesian formulation, this can be achieved by adding prior information encoding that the images of the two modalities are not independent. Structural similarity can be modeled by the alignment of the image gradients or equivalently their level sets being parallel. Therefore we can combine the objective functions of both modalities and penalize image pairs which do not have parallel level sets. Our results show that combining the reconstruction from heavily under-sampled MRI and noisy PET data can lead to less under-sampling artifacts in MRI images and better defined PET images.
KW - compressed sensing
KW - image reconstruction
KW - magnetic resonance imaging
KW - positron emission tomography
UR - http://www.scopus.com/inward/record.url?scp=84965022893&partnerID=8YFLogxK
U2 - 10.1109/NSSMIC.2014.7430895
DO - 10.1109/NSSMIC.2014.7430895
M3 - Conference contribution
BT - 2014 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC
PB - IEEE
CY - U. S. A.
ER -