Accelerated list-mode EM algorithm

List-mode data preserves all sampling information in three-dimensional (3-D) PET imaging and can reduce storage requirements for short-time frame acquisitions. List-mode expectation maximization-maximum likelihood (EM-ML), which has been implemented in a number of forms (such as the EM algorithm for...

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Bibliographic Details
Published in:IEEE transactions on nuclear science 2002-02, Vol.49 (1), p.42-49
Main Authors: Reader, A.J., Manavaki, R., Zhao, S., Julyan, P.J., Hastings, D.L., Zweit, J.
Format: Article
Language:English
Subjects:
Acceleration
Algorithms
Calculations
Cancer
Counting
Graph theory
Image reconstruction
Image storage
Instruments
Iterative methods
Maximum likelihood estimation
Memory
Positron emission tomography
Preserves
Probability
Real time
Reconstruction
Reconstruction algorithms
Sampling
Sampling methods
Statistical methods
Statistics
Three dimensional
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Description
Summary:List-mode data preserves all sampling information in three-dimensional (3-D) PET imaging and can reduce storage requirements for short-time frame acquisitions. List-mode expectation maximization-maximum likelihood (EM-ML), which has been implemented in a number of forms (such as the EM algorithm for list-mode maximum likelihood, the FAIR algorithm and COSEM), is an obvious choice to reconstruct from such data sets when the statistics are low. However, these methods can be slow for large quantities of list-mode data and it is desirable to accelerate them. This work investigates the use of subsets in combination with a relaxation parameter for 3-D list-mode EM reconstructions. Results show that two iterations through the list-mode data are sufficient to yield good quality reconstructions. Furthermore, if counting statistics are good, just one iteration may prove sufficient, opening the way for real-time iterative reconstruction.
ISSN:0018-9499
1558-1578
DOI:10.1109/TNS.2002.998679