Globally convergent 3D dynamic PET reconstruction with patch-based non-convex low rank regularization
- Authors
- Kim, K.S.; Son, Y.D.; Cho, Z.H.; Ra, J.B.; Ye, J.C.
- Issue Date
- Apr-2013
- Publisher
- IEEE
- Keywords
- concave-convex procedure; convex conjugate functions; Dynamic PET reconstruction; Legendre-Fenchel transform; low-rank; patch
- Citation
- Proceedings - International Symposium on Biomedical Imaging, pp.1158 - 1161
- Journal Title
- Proceedings - International Symposium on Biomedical Imaging
- Start Page
- 1158
- End Page
- 1161
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/14934
- DOI
- 10.1109/ISBI.2013.6556685
- ISSN
- 1945-7928
- Abstract
- Dynamic positron emission tomography (PET) is widely used to measure variations of radiopharmaceuticals within the organs over time. However, conventional reconstruction algorithm can produce a noisy reconstruction if there are not sufficient photon counts. Hence, the main goal of this paper is to develop a novel spatio-temporal regularization approach that exploits inherent similarities within intra- and interframes to overcome the limitation. One of the main contributions of this paper is to demonstrate that such correlations can be exploited using a low rank constraint of overlapping similarity blocks. The resulting optimization framework is, however, non-smooth and non Lipschitz due to the low-rank penalty terms and Poisson log-likelihood. Therefore, we propose a novel globally convergent optimization method using the concave-convex procedure (CCCP) by exploiting Legendre-Fenchel transform, which overcomes the memory and computational limitations. We confirm that the proposed algorithm can provide significantly improved image quality and extract accurate kinetic parameters. © 2013 IEEE.
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