Abstract
Background and objective: The absolute quantification of dynamic myocardial perfusion (MP) PET imaging is challenged by the limited spatial resolution of individual frame images due to division of the data into shorter frames. This study aims to develop a method for restoration and enhancement of dynamic PET images. Methods: We propose that the image restoration model should be based on multiple constraints rather than a single constraint, given the fact that the image characteristic is hardly described by a single constraint alone. At the same time, it may be possible, but not optimal, to regularize the image with multiple constraints simultaneously. Fortunately, MP PET images can be decomposed into a superposition of background vs. dynamic components via low-rank plus sparse (L + S) decomposition. Thus, we propose an L + S decomposition based MP PET image restoration model and express it as a convex optimization problem. An iterative soft thresholding algorithm was developed to solve the problem. Using realistic dynamic 82Rb MP PET scan data, we optimized and compared its performance with other restoration methods. Results: The proposed method resulted in substantial visual as well as quantitative accuracy improvements in terms of noise versus bias performance, as demonstrated in extensive 82Rb MP PET simulations. In particular, the myocardium defect in the MP PET images had improved visual as well as contrast versus noise tradeoff. The proposed algorithm was also applied on an 8-min clinical cardiac 82Rb MP PET study performed on the GE Discovery PET/CT, and demonstrated improved quantitative accuracy (CNR and SNR) compared to other algorithms. Conclusions: The proposed method is effective for restoration and enhancement of dynamic PET images.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 57-69 |
| Number of pages | 13 |
| Journal | Computer Methods and Programs in Biomedicine |
| Volume | 154 |
| DOIs | |
| State | Published - Feb 1 2018 |
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Keywords
- Low-rank
- Myocardial perfusion
- Pet imaging
- Sparse
ASJC Scopus subject areas
- Software
- Computer Science Applications
- Health Informatics
Cite this
Enhancement of dynamic myocardial perfusion PET images based on low-rank plus sparse decomposition. / Lu, Lijun; Ma, Xiaomian; Mohy-ud-Din, Hassan; Ma, Jianhua; Feng, Qianjin; Rahmim, Arman; Chen, Wufan.
In: Computer Methods and Programs in Biomedicine, Vol. 154, 01.02.2018, p. 57-69.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Enhancement of dynamic myocardial perfusion PET images based on low-rank plus sparse decomposition
AU - Lu, Lijun
AU - Ma, Xiaomian
AU - Mohy-ud-Din, Hassan
AU - Ma, Jianhua
AU - Feng, Qianjin
AU - Rahmim, Arman
AU - Chen, Wufan
PY - 2018/2/1
Y1 - 2018/2/1
N2 - Background and objective: The absolute quantification of dynamic myocardial perfusion (MP) PET imaging is challenged by the limited spatial resolution of individual frame images due to division of the data into shorter frames. This study aims to develop a method for restoration and enhancement of dynamic PET images. Methods: We propose that the image restoration model should be based on multiple constraints rather than a single constraint, given the fact that the image characteristic is hardly described by a single constraint alone. At the same time, it may be possible, but not optimal, to regularize the image with multiple constraints simultaneously. Fortunately, MP PET images can be decomposed into a superposition of background vs. dynamic components via low-rank plus sparse (L + S) decomposition. Thus, we propose an L + S decomposition based MP PET image restoration model and express it as a convex optimization problem. An iterative soft thresholding algorithm was developed to solve the problem. Using realistic dynamic 82Rb MP PET scan data, we optimized and compared its performance with other restoration methods. Results: The proposed method resulted in substantial visual as well as quantitative accuracy improvements in terms of noise versus bias performance, as demonstrated in extensive 82Rb MP PET simulations. In particular, the myocardium defect in the MP PET images had improved visual as well as contrast versus noise tradeoff. The proposed algorithm was also applied on an 8-min clinical cardiac 82Rb MP PET study performed on the GE Discovery PET/CT, and demonstrated improved quantitative accuracy (CNR and SNR) compared to other algorithms. Conclusions: The proposed method is effective for restoration and enhancement of dynamic PET images.
AB - Background and objective: The absolute quantification of dynamic myocardial perfusion (MP) PET imaging is challenged by the limited spatial resolution of individual frame images due to division of the data into shorter frames. This study aims to develop a method for restoration and enhancement of dynamic PET images. Methods: We propose that the image restoration model should be based on multiple constraints rather than a single constraint, given the fact that the image characteristic is hardly described by a single constraint alone. At the same time, it may be possible, but not optimal, to regularize the image with multiple constraints simultaneously. Fortunately, MP PET images can be decomposed into a superposition of background vs. dynamic components via low-rank plus sparse (L + S) decomposition. Thus, we propose an L + S decomposition based MP PET image restoration model and express it as a convex optimization problem. An iterative soft thresholding algorithm was developed to solve the problem. Using realistic dynamic 82Rb MP PET scan data, we optimized and compared its performance with other restoration methods. Results: The proposed method resulted in substantial visual as well as quantitative accuracy improvements in terms of noise versus bias performance, as demonstrated in extensive 82Rb MP PET simulations. In particular, the myocardium defect in the MP PET images had improved visual as well as contrast versus noise tradeoff. The proposed algorithm was also applied on an 8-min clinical cardiac 82Rb MP PET study performed on the GE Discovery PET/CT, and demonstrated improved quantitative accuracy (CNR and SNR) compared to other algorithms. Conclusions: The proposed method is effective for restoration and enhancement of dynamic PET images.
KW - Low-rank
KW - Myocardial perfusion
KW - Pet imaging
KW - Sparse
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U2 - 10.1016/j.cmpb.2017.10.020
DO - 10.1016/j.cmpb.2017.10.020
M3 - Article
C2 - 29249347
AN - SCOPUS:85033402498
VL - 154
SP - 57
EP - 69
JO - Computer Methods and Programs in Biomedicine
JF - Computer Methods and Programs in Biomedicine
SN - 0169-2607
ER -