Abstract
In SPECT/PET, the maximum-likelihood expectation-maximization (ML-EM) algorithm is getting more attention as the speed of computers increases. This is because it can incorporate various physical aspects into the reconstruction process leading to a more accurate reconstruction than other analytical methods such as filtered-backprojection algorithms. However, the convergence rate of the ML-EM algorithm is very slow. Several methods have been developed to speed it up, such as the ordered-subset expectation-maximization (OS-EM) algorithm. Even though OS-type algorithms can bring about significant acceleration in the iterative reconstruction, it is generally believed that ML-EM produces better images, in terms of statistical noise in the reconstruction. In this paper, we present an accelerated ML-EM algorithm with bigger step size and show its convergence characteristics in terms of variance noise and log-likelihood values. We also show some advantages of our method over other accelerating methods using additive forms.
| Original language | English |
|---|---|
| Pages (from-to) | 237-252 |
| Number of pages | 16 |
| Journal | Physics in medicine and biology |
| Volume | 51 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2006 Jan 21 |
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All Science Journal Classification (ASJC) codes
- Radiological and Ultrasound Technology
- Radiology Nuclear Medicine and imaging
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Convergence study of an accelerated ML-EM algorithm using bigger step size. / Hwang, Do Sik; Zeng, Gengsheng L.
In: Physics in medicine and biology, Vol. 51, No. 2, 21.01.2006, p. 237-252.Research output: Contribution to journal › Article
TY - JOUR
T1 - Convergence study of an accelerated ML-EM algorithm using bigger step size
AU - Hwang, Do Sik
AU - Zeng, Gengsheng L.
PY - 2006/1/21
Y1 - 2006/1/21
N2 - In SPECT/PET, the maximum-likelihood expectation-maximization (ML-EM) algorithm is getting more attention as the speed of computers increases. This is because it can incorporate various physical aspects into the reconstruction process leading to a more accurate reconstruction than other analytical methods such as filtered-backprojection algorithms. However, the convergence rate of the ML-EM algorithm is very slow. Several methods have been developed to speed it up, such as the ordered-subset expectation-maximization (OS-EM) algorithm. Even though OS-type algorithms can bring about significant acceleration in the iterative reconstruction, it is generally believed that ML-EM produces better images, in terms of statistical noise in the reconstruction. In this paper, we present an accelerated ML-EM algorithm with bigger step size and show its convergence characteristics in terms of variance noise and log-likelihood values. We also show some advantages of our method over other accelerating methods using additive forms.
AB - In SPECT/PET, the maximum-likelihood expectation-maximization (ML-EM) algorithm is getting more attention as the speed of computers increases. This is because it can incorporate various physical aspects into the reconstruction process leading to a more accurate reconstruction than other analytical methods such as filtered-backprojection algorithms. However, the convergence rate of the ML-EM algorithm is very slow. Several methods have been developed to speed it up, such as the ordered-subset expectation-maximization (OS-EM) algorithm. Even though OS-type algorithms can bring about significant acceleration in the iterative reconstruction, it is generally believed that ML-EM produces better images, in terms of statistical noise in the reconstruction. In this paper, we present an accelerated ML-EM algorithm with bigger step size and show its convergence characteristics in terms of variance noise and log-likelihood values. We also show some advantages of our method over other accelerating methods using additive forms.
UR - http://www.scopus.com/inward/record.url?scp=32544441568&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=32544441568&partnerID=8YFLogxK
U2 - 10.1088/0031-9155/51/2/004
DO - 10.1088/0031-9155/51/2/004
M3 - Article
C2 - 16394336
AN - SCOPUS:32544441568
VL - 51
SP - 237
EP - 252
JO - Physics in Medicine and Biology
JF - Physics in Medicine and Biology
SN - 0031-9155
IS - 2
ER -