Original article

Appropriate parameters of the ordered-subset expectation
maximization algorithm on measurement of myocardial
blood flow and oxygen consumption with 11C-acetate PET
Masato Kobayashia,b, Tetsuya Moria, Yasushi Kiyonoa, Tetsuya Tsujikawaa,
Rikiya Maruyamaa, Yusuke Higakib, Naoto Shikanoc, Ryuichi Nishiid,
Keiichi Kawaia,b, Takashi Kudoa,e and Hidehiko Okazawaa
Objective The purpose of this study was to evaluate
the appropriate parameters of a filter and of subsets (S)and iterations (I) of the ordered-subset expectation
maximization (OSEM) algorithm in 11C-acetate PET.
Methods A Hanning filter (HF) and a Gaussian filter (GF)
were selected for filtered back-projection (FBP) and the
OSEM algorithm, respectively. After evaluation of the
optimal HF size, the GF size was optimized using healthy
volunteers (HV). Myocardial blood flow (MBF) andoxygen consumption (kmono) values were calculated by
combining 4S, 16S, or 28S with 2I, 4I, 6I, or 8I of the
OSEM (MBFOSEM and kmonoOSEM, respectively) in eight HVand eight coronary artery disease (CAD) patients.
These MBFOSEM and kmonoOSEM values were comparedwith those obtained using FBP (MBFFBP and kmonoFBP,
respectively).
Results Optimal HF and GF (10.0GF) sizes for the FBPand OSEM algorithms, respectively, were 10.0 mm
full-width resolution at half-maximum. MBFOSEM waschanged by modifying the parameters of the OSEM
algorithm. The best correlations were between MBFFBP and
MBFOSEM, with 28S6I and 10.0GF for HV patients and 28S8I
for CAD patients. However, the MBFOSEM with 28S8I
was significantly different from MBFFBP at the global

Introduction
Image reconstruction processing is an essential step toobtain high image quality in PET. Reconstruction
algorithms usually fall into one of two main categories:
analytic image reconstruction methods, such as thefiltered back-projection (FBP) algorithm, and iterative
image reconstruction methods, such as the orderedsubset expectation maximization (OSEM) algorithm [1].
The FBP algorithm is generally used as the gold standard
for quantitative PET, whereas the OSEM algorithm
improves the image signal-to-noise ratio and eliminates
streak artifacts in the PET images because of its more
optimal handling of Poisson’s noise in the sinogram data.
In cardiac PET studies, the OSEM algorithm yielded
accurate quantitative data and improved image quality incomparison with the FBP algorithm when used formeasurement of the glucose metabolic rate in 18F-20143-3636 c 2012 Wolters Kluwer Health | Lippincott Williams & Wilkins

myocardium in HV. The kmonoOSEM with 28S6I was not
significantly different from kmonoFBP in HV or CAD patients.
Conclusion Appropriate parameters are 28S6I with a
10.0GF on the MBFOSEM and kmonoOSEM measurement
using 11C-acetate. Diagnostic performance will improveusing noiseless, artifact-reduction images, and accuratequantitative values that are provided by the OSEM
algorithm with the appropriate parameters. Nucl Med
Commun 33:130–138 c 2012 Wolters Kluwer Health |Lippincott Williams & Wilkins.
Nuclear Medicine Communications 2012, 33:130–138
Keywords: 11C-acetate PET, cardiology, myocardial blood flow, myocardial
oxygen consumption, ordered-subset expectation maximizationaBiomedical Imaging Research Center, University of Fukui, Fukui, bSchool of
Health Sciences, College of Medical, Pharmaceutical and Health Sciences,
Kanazawa University, Kanazawa, cDepartment of Radiological Sciences, Ibaraki
Prefectural University of Health Sciences, Ibaraki, dDepartment of Radiology,
School of Medicine, Miyazaki University, Miyazaki and eAtomic Bomb Disease
Institute, Nagasaki University, Nagasaki, Japan
Correspondence to Masato Kobayashi, PhD, School of Health Sciences,
College of Medical, Pharmaceutical and Health Sciences, Kanazawa University,
5-11-80 Kodatsuno, Kanazawa 920-0942, Japan
Tel: + 81 76 265 2531; fax: + 81 76 234 4351;
e-mail: kobayasi@mhs.mp.kanazawa-u.ac.jp
Received 3 August 2011 Revised 19 October 2011 Accepted 24 October 2011

fluoro-2-deoxy-D-glucose (18F-FDG) PET [2–4] and formeasurement of myocardial blood flow (MBF) in 15Nammonia PET [4–6]. However, the OSEM algorithm has
not been applied to the measurement of MBF [7–12] oroxygen consumption (kmono) [13–22] in 11C-acetate PET.
In this study, we evaluated the parameters of an OSEM
algorithm, including optimization of a smoothing Gaussian post-filter, which were appropriate for MBF and kmono
measurements in 11C-acetate PET. The values of MBF
and kmono calculated using OSEM with different parameters were compared with those calculated using FBP
in PET studies of healthy volunteers (HV) and coronary
artery disease (CAD) patients.

Methods
Participants

Sixteen participants, eight young male HV (age range:
21–36 years; mean: 28.9 ± 6.2 years) and eight CAD
DOI: 10.1097/MNM.0b013e32834e7f5c

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Quantification of OSEM algorithm Kobayashi et al. 131

patients with known myocardial ischemia (four men, four
women; age range: 60–87 years; mean: 73.0 ± 10.3 years),
were studied. All patients fasted for more than 6 h before
PET examination. The study was approved by the Ethics
Committee of Fukui University Hospital and the experiments have therefore been performed in accordance withthe ethical standards laid down in the 1964 Declaration of
Helsinki. Written informed consent was obtained fromeach participant.
Positron emission tomography procedures

All participants underwent PET scanning using a wholebody tomography scanner (ADVANCE, General Electric
Medical System, Milwaukee, Wisconsin, USA), whichallowed the simultaneous acquisition of 35 image slices
in a two-dimensional (2D) acquisition mode with aninterslice spacing of 4.25 mm [23]. Performance tests
showed the intrinsic resolution of the scanner to be
4.6–5.7 mm in the transaxial direction and 4.0–5.3 mm in
the axial direction. A 10 min transmission scan was
performed using 68Ge/68Ga for attenuation correction before
radiotracer administration. Approximately 740 MBq of
11
C-acetate was slowly administered intravenously over
30 s and dynamic data were acquired (5-s  18 frame,
7-s  30 frame, 60-s  5 frame, and 120-s  5 frame). Transaxial slices were collected in a matrix size of 128  128
pixels.
Image processing

A Hanning filter (HF) and a Gaussian filter (GF) wereselected as smoothing filters for the FBP and OSEM
algorithms, respectively. The projection data werereconstructed using the FBP algorithm with the HF,
and the OSEM algorithm, which was combined withseveral different subset (S) and iteration (I) numbers,

with a full-width resolution at half-maximum (FWHM)
GF in the transaxial direction. To optimize these filters,
the size of the HF was changed to 4.5, 6.0, 8.0, 10.0, or
15.0 mm FWHM (4.5HF, 6.0HF, 8.0HF, 10.0HF, and15.0HF, respectively) and that of the GF was changed to
5.0, 10.0, or 15.0 mm FWHM (5.0GF, 10.0GF, and 15.0GF,
respectively). We determined the effect of the filters on
MBF calculation using a summed image of the early
dynamic phase in the HV study. The parameters of the
OSEM algorithm tested were combinations of 4S and 2I,
4I, 6I, or 8I (4S2I, 4S4I, 4S6I, or 4S8I) of 16S and 2I, 4I,
6I, or 8I (16S2I, 16S4I, 16S6I, or 16S8I) of 28S and 2I, 4I,
6I, or 8I (28S2I, 28S4I, 28S6I, or 28S8I) using the
optimum size of the GF, which was applied to the patient
study. The values of MBF and kmono, which were
calculated using each parameter of the OSEM algorithm(MBFOSEM and kmonoOSEM, respectively), were comparedwith those calculated using the FBP algorithm (MBFFBP
and kmonoFBP, respectively). These PET images werecorrected using radioactive decay to the start time, adeconvolution scatter correction method [24], and therecovery coefficient of the PET camera.
Quantitative analysis

The MBF (ml/min/g) values were calculated by a Patlak
plot using dynamic image data reconstructed with FBPand OSEM algorithms as reported previously [12,25].
A summed image of data obtained 30–150 s after the startof PET acquisition as well as MRI T1 images were used
to place regions of interest (ROIs) in a suitable position.
Fifteen ROIs (5  3 slices) of 6.0 mm diameter weredrawn in the left ventricular (LV) cavity to obtain a time–
activity curve (TAC) of arterial input function [26] and inanterior (ANT), septal (SEP), inferior (INF), posterior(POS), and lateral (LAT) walls to obtain TACs of each(a) 40(b) 40

COV (%)COV (%)

Fig. 1

30

20

0

5

10
15
Filter size (mm)

20

30

20

0

5

10
15
Filter size (mm)20(a) Effect of smoothing filter size on filtered back-projection and ordered-subset expectation maximization algorithms at the global myocardium inhealthy volunteers. The value of the coefficient of variation (COV) reached a maximum when a 10.0 mm full-width resolution at half-maximum hanningfilter was applied in the filtered back-projection algorithm. (b) A filter size of 10.0 mm full-width resolution at half-maximum was best for a Gaussian
post-filter in the ordered-subset expectation maximization algorithm using parameters of 28S and 2I ( ), 4I ( ), 6I ( ) or 8I ( ).

Copyright © Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.

HV
Global
ANT
SEP
INF
POS
LAT
CAD
Ischemia

Values are mean values ± SD.
ANT, anterior; CAD, coronary artery disease; FBP, filtered back-projection; HV, healthy volunteer; INF, inferior; LAT, lateral; MBF, myocardial blood flow; OSEM, ordered-subset expectation maximization; POS, posterior; SEP,
septal.
*P < 0.01 and **P < 0.05 compared with MBFFBP using analysis of variance and a post-hoc test.

0.21 ± 0.09
0.21 ± 0.09
0.20 ± 0.09
0.17 ± 0.11
0.15 ± 0.12
0.12 ± 0.13
0.21 ± 0.09

0.14 ± 0.14

0.17 ± 0.11

0.20 ± 0.09

0.21 ± 0.09

0.19 ± 0.10

0.21 ± 0.09

0.62 ± 0.14**
0.60 ± 0.13
0.65 ± 0.11
0.57 ± 0.18
0.62 ± 0.14
0.61 ± 0.13
0.61 ± 0.14
0.59 ± 0.13
0.64 ± 0.10
0.57 ± 0.18
0.61 ± 0.13
0.60 ± 0.12
0.58 ± 0.11
0.56 ± 0.09
0.61 ± 0.10
0.55 ± 0.16
0.57 ± 0.10
0.57 ± 0.09
0.49 ± 0.10*
0.47 ± 0.06*
0.49 ± 0.15
0.51 ± 0.14
0.43 ± 0.06**
0.48 ± 0.08**
0.46 ± 0.10*
0.44 ± 0.06*
0.46 ± 0.16
0.48 ± 0.13**
0.37 ± 0.06*
0.45 ± 0.08*
0.28 ± 0.08*
0.26 ± 0.04*
0.29 ± 0.17*
0.29 ± 0.07*
0.18 ± 0.08*
0.29 ± 0.06*
0.60 ± 0.14
0.59 ± 0.12
0.64 ± 0.08
0.56 ± 0.18
0.60 ± 0.14
0.60 ± 0.13

0.39 ± 0.12*
0.38 ± 0.05*
0.41 ± 0.18
0.43 ± 0.11*
0.23 ± 0.08*
0.36 ± 0.13*

0.49 ± 0.09*
0.47 ± 0.06*
0.49 ± 0.15
0.51 ± 0.14
0.43 ± 0.05**
0.48 ± 0.08**

0.56 ± 0.10*
0.53 ± 0.08**
0.57 ± 0.11
0.54 ± 0.15
0.54 ± 0.08
0.55 ± 0.08

0.60 ± 0.12
0.58 ± 0.10
0.63 ± 0.10
0.56 ± 0.17
0.59 ± 0.11
0.58 ± 0.10

0.55 ± 0.10*
0.53 ± 0.09
0.53 ± 0.09
0.53 ± 0.13
0.54 ± 0.11
0.54 ± 0.08

0.59 ± 0.12
0.57 ± 0.10
0.62 ± 0.10
0.56 ± 0.16
0.59 ± 0.12
0.59 ± 0.10

8
6
2
8
6
Iteration
FBP

Subset2

4

4
RegionTable 1

MBF values at the global myocardium and myocardial region in HV and CAD patients

OSEM

16
4

8

2

4

28

6

132 Nuclear Medicine Communications 2012, Vol 33 No 2

myocardial region in HV and in the LV cavity and theischemia region in the CAD patients, using dynamic
image data obtained 30–150 s from the start of PET
acquisition. The average MBF values of global myocardium were calculated using the values of ANT, SEP, INF,
POS, and LAT.
The kmono values were calculated by a graphical analysis
method using dynamic image data obtained between 8 and
20 min of 11C-acetate PET [22]. A summed image of dataobtained over 8–20 min as well as MRI T1 images wereused to place ROIs in a suitable position. Fifteen ROIs
(5  3 slices) were placed over the ANT, SEP, INF, POS,
and LATregions of dynamic images of HV, and the ischemia
region of the dynamic images of the CAD patients, toobtain the TAC of each myocardial region. The average
kmono values of global myocardium were calculated using
the values of ANT, SEP, INF, POS, and LAT. The kmono
values were obtained using the equation q = Ae – kt [whereq = count/pixel/ min corrected for physical decay, k = kmono(per min), and t = time (min)] [25,27,28]. A monoexponential function was fitted to the TAC of each myocardial
region and the values of kmono were determined.
Statistical analysis

Image noise was defined as the coefficient of variation[COV, SD/mean  100 (%)] of the pixel values within the
global myocardium using the HF and the GF filters inthe HV study. The values for MBFOSEM and kmonoOSEM
were compared with those of MBFFBP and kmonoFBP using
analysis of variance and a post-hoc test. The correlation
between MBFFBP and MBFOSEM and that between
kmonoFBP and kmonoOSEM were evaluated using linear
regression analysis. Agreements between MBFFBP and
MBFOSEM and between kmonoFBP and kmonoOSEM wereanalyzed by Bland–Altman plots that show the differences (FBP – OSEM) versus the entire range of averagevalues ± 2SD [29,30].

Results
Myocardial blood flow calculation

The smoothing effect of the different sizes of each filter
tested using the FBP and OSEM algorithms was determined (Fig. 1). The COV reached a maximum of 32% when
10.0HF was applied to the projection data of the FBP
algorithm. In the OSEM algorithm, 10.0GF yielded the
maximum COV, although the COV did not change
significantly when the size of the GF was changed.
Conditions of 10.0HF and 10.0GF were then applied to
the FBP and OSEM algorithms, respectively, to evaluate
combinations of different S and I values in the OSEM
algorithm. The MBF values of the global myocardium andof myocardial regions in HV, and of the ischemia region in
CAD patients, were determined (Table 1). The MBFOSEM
that was calculated for the global myocardium of HV using
16S6I, 16S8I, 28S4I, or 28S6I was not significantly different
from the MBFFBP calculated for the same region. In the

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Quantification of OSEM algorithm Kobayashi et al. 133

Table 2

Correlation of MBFFBP and MBFOSEM on the global myocardium in HV and CAD patients

SubsetIteration
HV
Slopear2
CAD
Slopea
r2

4

16

28

2

4

6

8

2

4

6

8

2

4

6

8

0.15
0.07

0.45
0.27

0.46
0.42

0.53
0.57

0.52
0.58

0.69
0.86

0.79
0.93

0.88
0.94

0.66
0.87

0.85
0.93

0.97
0.96

1.02
0.96

0.42
0.08

0.43
0.08

0.53
0.14

0.62
0.22

0.62
0.23

0.83
0.59

0.97
0.83

0.98
0.90

0.78
0.48

0.93
0.85

0.99
0.93

1.02
0.95

CAD, coronary artery disease; FBP, filtered back-projection; HV, healthy volunteer; MBF, myocardial blood flow; OSEM, ordered-subset expectation maximization.
Slope of the regression line between MBFFBP and MBFOSEM.

aFig. 2

0.40

0.40

0.30

0.30
Difference(b) 0.50

Difference(a) 0.50

0.20
0.10

0.20
0.10

0.00

0.00

−0.10

−0.10

−0.20
0.30 0.40

0.50 0.60
Average0.70 0.80

−0.20
0.00

0.10

0.20
0.30
Average

0.40(a) Bland–Altman analyses of myocardial blood flow showing the differences between the average filtered back-projection and each parameter ofordered-subset expectation maximization in healthy volunteers and (b) in coronary artery disease patients. The magnitude of the bias was small( – 0.01 ml/min/g in the HV and 0.00 ml/min/g in the coronary artery disease patients), with limits of agreement ranging from values of 0.05 ml/min/g.
Means (solid lines) ± 2SD (broken line) are shown.

myocardial regions, differences between MBFFBP and
MBFOSEM, calculated using increasing S and I numbers in
the OSEM algorithm, were not significant. In the CAD
patient group, there were no significant differences
between the value of MBFFBP and the value of MBFOSEM
calculated using any combination of S and I. The
correlation between MBFFBP and MBFOSEM at the global
myocardium in HV and in CAD patients was calculated(Table 2). In the correlation between MBFFBP and
MBFOSEM, the slope of the regression line was improvedby increasing the S and I numbers of the OSEM algorithm
in HV and in CAD patients. The best correlations were
between MBFFBP and MBFOSEM with 28S6I in HV andwith 28S8I at the ischemia region of the CAD patients.
The mean differences between MBFFBP and MBFOSEM
with 28S6I were – 0.01 ± 0.05 ml/min/g in the HV patientsand 0.00 ± 0.05 ml/min/g in the CAD patients, as assessedusing Bland–Altman analysis (Fig. 2).
kmono calculation

The values of kmonoOSEM that were calculated using 4S2I
or 4S4I, and 10.0GF were significantly different from thevalue of kmonoFBP in HV (Table 3). The slope of theregression line and the square of the correlation

coefficient between kmonoFBP and kmonoOSEM were large,
with the exception of kmonoOSEM with 4S2I and 4S4I
(Table 4). The mean differences between the value of
kmonoFBP and that of kmonoOSEM with 28S6I, which are theoptimal parameters for measurement of MBFOSEM, were0.000 ± 0.005 in HV and 0.000 ± 0.006 in the CAD
patients, as assessed using Bland–Altman analysis (Fig. 3).

Discussion
The iterative reconstruction algorithm including OSEM
has been improved to provide clear PET images
compared with the FBP algorithm. Although some kindsof iterative reconstruction algorithms are installed inclinical PET scanners [31,32], it is difficult for technicians or operators to determine appropriate parameters of
the algorithm in an effort to yield accurate quantitative
PET values. This study evaluated the effects of thesmoothing filter function, and the number of S and I in
the OSEM algorithm, on the measurement of MBF andkmono values using 11C-acetate PET. The OSEM algorithm is useful for clinical PET studies because thisalgorithm improves image quality and reduces streak
artifacts compared with the FBP algorithm [2]. Application of a smoothing filter further decreases image noise in

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0.027 ± 0.016
0.028 ± 0.015
0.026 ± 0.016
0.026 ± 0.016
0.027 ± 0.015
0.028 ± 0.013
0.029 ± 0.013
0.030 ± 0.013
0.031 ± 0.012
0.030 ± 0.011
0.027 ± 0.014

HV
Global
ANT
SEP
INF
POS
LAT
CAD
Ischemia

Values are mean values ± SD.
ANT, anterior; CAD, coronary artery disease; FBP, filtered back-projection; HV, healthy volunteer; INF, inferior; LAT, lateral; OSEM, ordered-subset expectation maximization; POS, posterior; SEP, septal.
*P < 0.01 and **P < 0.05 compared with MBFFBP using analysis of variance and a post-hoc test.

0.027 ± 0.016
0.027 ± 0.016

0.062 ± 0.018
0.063 ± 0.014
0.070 ± 0.021
0.060 ± 0.016
0.057 ± 0.019
0.057 ± 0.019
0.062 ± 0.018
0.063 ± 0.014
0.069 ± 0.022
0.060 ± 0.014
0.060 ± 0.017
0.057 ± 0.019
0.062 ± 0.018
0.063 ± 0.014
0.070 ± 0.022
0.060 ± 0.014
0.061 ± 0.019
0.059 ± 0.019
0.062 ± 0.018
0.064 ± 0.013
0.069 ± 0.022
0.061 ± 0.016
0.059 ± 0.017
0.059 ± 0.018
0.062 ± 0.018
0.063 ± 0.014
0.070 ± 0.022
0.061 ± 0.016
0.060 ± 0.020
0.059 ± 0.019
0.062 ± 0.018
0.063 ± 0.014
0.070 ± 0.022
0.060 ± 0.014
0.061 ± 0.020
0.060 ± 0.017
0.062 ± 0.017
0.063 ± 0.014
0.069 ± 0.019
0.060 ± 0.014
0.060 ± 0.019
0.059 ± 0.018
0.062 ± 0.017
0.063 ± 0.014
0.069 ± 0.019
0.060 ± 0.014
0.059 ± 0.019
0.059 ± 0.018
0.061 ± 0.016
0.063 ± 0.014
0.066 ± 0.016
0.060 ± 0.014
0.059 ± 0.019
0.059 ± 0.018
0.059 ± 0.016*
0.061 ± 0.015*
0.063 ± 0.017
0.054 ± 0.012**
0.056 ± 0.016
0.060 ± 0.019
0.052 ± 0.013*
0.059 ± 0.010**
0.054 ± 0.013**
0.050 ± 0.008**
0.050 ± 0.015**
0.053 ± 0.016*
0.063 ± 0.018
0.064 ± 0.013
0.067 ± 0.019
0.063 ± 0.017
0.060 ± 0.020
0.060 ± 0.017

4286Iteration
FBP

Subset2

4

4
RegionTable 3kmono values of the global myocardium and myocardial region in HV and CAD patients

OSEM

16

6

8

2

4

28

6

0.062 ± 0.018
0.063 ± 0.014
0.070 ± 0.021
0.064 ± 0.016
0.057 ± 0.019
0.059 ± 0.018

Nuclear Medicine Communications 2012, Vol 33 No 2

8134

PET images. In this study, the effect of the smoothingfilter on MBF calculation was evaluated using early timeframes with a short time duration, because the images in
the frame have considerable image noise. The GF in the
OSEM algorithm was optimized at 10.0 mm FWHM, atwhich point the maximum COV of 35–40% was attained.
This maximum was higher than that obtained with the FBP
algorithm after optimization of the HF in the FBP
algorithm (Fig. 1). Therefore, the image quality using
OSEM is greater than that using FBP, which producesconsiderable image noise. The MBFFBP using 10.0HF was
0.60 ± 0.14 ml/min/g at the global myocardium in HV. In aprevious study, MBFFBP calculated using a Shepp filter of
Nyquist frequency (0.3 cycles per pixel) was 0.70 ±0.11 ml/min/g in HV using 11C-acetate [20]. Although thisShepp filter was not optimized for the measurement of
MBFFBP with 11C-acetate, no significant differences wereobserved between the MBFFBP that was calculated using
the Shepp filter and that calculated using the HF. Kudo
et al. [12] showed that the MBFFBP values calculated for
11
C-acetate showed an excellent linear correlation withthose calculated for 13N-ammonia using a Patlak plot. In
11
C-acetate PET, the values of MBFOSEM were close to
those of MBFFBP at the global myocardium and at eachmyocardial region when the number of S and I wereincreased, especially in HV (Table 1). The best correlations
were between the value of MBFFBP with 10.0HF and thatof MBFOSEM with 28S6I and 10.0GF at the global
myocardium of HV or that of MBFOSEM with 28S8I and
10.0GF at the ischemia region of the CAD patients(Table 2). However, the MBFOSEM with 28S8I was
significantly different from the MBFFBP at the global
myocardium in HV because image noise was elevated withincreasing iteration in the OSEM algorithm. As a result, theCOV of 28S8I was slightly smaller than that of 28S6I(Fig. 1). Thus, high iteration numbers should not be usedin the OSEM algorithm for quantitative PET studies.
There was excellent agreement between the value ofthe average MBF of each participant, calculated using
MBFOSEM with 28S6I and 10.0GF, and MBFFBP with
10.0HF in HV and CAD patients (Fig. 2). The short-axis
MBFOSEM images obtained using 28S6I and 10.0GF wereclear compared with MBFFBP images obtained with 10.0HF(Figs 4 and 5). Therefore, when using 11C-acetate PET,
parameters of 28S6I and 10.0GF should be used for themeasurement of MBFOSEM. The excellent quality of the
MBFOSEM images will improve diagnostic performance in
PET studies. For example, misregistration between transmission attenuation and emission images causes artifactual
abnormalities on cardiac PET images that result in falsepositive defects [33,34]. The severe artifacts are produced
by diaphragmatic displacement, BMI, and heart sizes.
These artifacts can be decreased using the appropriate
parameter of the OSEM algorithm.
The MBFOSEM values were compared with the MBFFBP
values of previous cardiac 15N-ammonia PETstudies [4–6].

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Quantification of OSEM algorithm Kobayashi et al. 135

Table 4

Correlation of kmonoFBP and kmonoOSEM on the global myocardium in HV and CAD patients

SubsetIteration
HV
Slopear2
CAD
Slopea
r2

4

16

28

2

4

6

8

2

4

6

8

2

4

6

8

0.69
0.87

0.84
0.90

0.90
0.96

0.94
0.97

0.94
0.97

1.00
0.98

1.01
0.98

0.99
0.98

1.00
0.97

1.01
0.97

1.01
0.96

1.01
0.95

0.56
0.50

0.66
0.62

0.75
0.75

0.82
0.84

0.84
0.82

1.00
0.96

1.07
0.98

1.10
0.98

0.99
0.95

1.10
0.98

1.11
0.97

1.08
0.95

CAD, coronary artery disease; FBP, filtered back-projection; HV, healthy volunteer; MBF, myocardial blood flow; OSEM, ordered-subset expectation maximization.
Slope of the regression line between MBFFBP and MBFOSEM.

aFig. 3

0.010

0.010

0.005

0.005

Difference(b) 0.015

Difference(a) 0.015

0.000−0.005−0.010−0.005−0.010−0.015−0.015−0.020
0.02

0.000

0.04

0.06
0.08
Average

0.10−0.020
0.00

0.02

0.04
0.06
Average

0.08

Bland–Altman analyses of kmono showing the differences between the average filtered back-projection and each parameter of ordered-subset
expectation maximization in healthy volunteers (a) and coronary artery disease patients (b). There was no difference between kmonoFBP and
kmonoOSEM. Means (solid lines) ± 2SD (broken line) are shown.

Søndergaard et al. [4] showed the usefulness of OSEM
with 16S6I and 8.0GF compared with FBP and a ramp
filter. However, as they did not perform partial volume
correction in that study, the MBF values calculated using
these parameters of the OSEM algorithm will not be
accurate. Chen et al. [5] assessed the effects of changing
the number of I and the smoothing filter of the OSEM
algorithm on MBF. In their study, the value of MBFOSEM
with 28S8I and 10.0GF correlated best with MBFFBP with
10.0HF. However, the parameters of the OSEM algorithm
were not optimized in that study, because they did notassess the effect of combinations of S and I numbers.
Hove et al. [6] estimated MBFOSEM values using 28S2I
and 6.0HF, which is the default setting of the equipment.
They reported that this value was underestimatedcompared with the value obtained using MBFFBP and
7.0 mm FWHM HF or with the value obtained by Chenet al. When appropriate parameters (16S4I or 16S6I) of
the OSEM algorithm were applied, the OSEM algorithm
was also shown to be a useful tool in previous cardiac
18
F-FDG PET studies for improving image quality [2–4].
Although these reported parameters for the OSEM
algorithm in 15N-ammonia and 18F-FDG studies are notsignificantly different from the 28S6I with 10.0GF
parameters, which were appropriate for MBF evaluation
using 11C-acetate PET in our study, none of theseprevious studies ever evaluated in detail the effect of
combinations of S and I numbers and different filter sizes
on MBF measurement.
Using 11C-acetate PET, it is possible to easily measure
not only the MBF value [7–12] but also the kmono
values [13–22]. The present study is the first toinvestigate the effect of parameters of OSEM algorithms
on the measurement of kmono using a graphical analysis
method of monoexponential curve fitting and dataobtained 8–20 min after 11C-acetate injection [22]. Filter
sizes of 10.0HF and 10.0GF, which are appropriate for themeasurement of MBF, were applied to kmono measurement because smoothing filters had a greater effecton MBF measurement than on kmono measurement. The
kmonoOSEM values obtained were not significantly different from the kmonoFBP values, with the exception of values
obtained using 4S2I and 4S4I in HV (Table 3). The effectof increasing S and I on the measurement of kmono would
be small. Bland–Altman analysis indicated no difference
between the value of kmonoOSEM obtained using 28S6I and
10.0GF and kmonoFBP with 10.0HF (Fig. 3). The OSEM
algorithm with 28S6I and 10.0GF produced clear
kmonoOSEM images compared with kmonoFBP with 10.0HF(Figs 4 and 5). Therefore, parameters of 28S6I and
10.0GF in the OSEM algorithm are appropriate for

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Nuclear Medicine Communications 2012, Vol 33 No 2

Fig. 4

(a)

(b)

(c)

(d)

Representative short-axis images for the calculation of (a) MBFFBP , (b) MBFOSEM, (c) kmonoFBP , and (d) kmonoOSEM in HV. The MBF values were
calculated based on Patlak-plot analyses using early-phase dynamic images. These images were summed using early-phase dynamic data for the
calculation of MBF and later-phase data obtained 8–20 min after 11C-acetate injection for the calculation of kmono. FBP, filtered back-projection;
MBF, myocardial blood flow; OSEM, ordered-subset expectation maximization.

calculation of the MBFOSEM and kmonoOSEM values in
11
C-acetate PET. The reconstruction time using adequate
parameters of the OSEM algorithm was little different
from that with the FBP algorithm due to recent progress incomputer performance. It will be possible to apply these
28S6I with 10.0GF parameters that were appropriate in
11
C-acetate PET for the measurement of MBFOSEM
using 15N-ammonia PET because both 11C-acetate and15
N-ammonia can measure the MBF of myocardium.
The appropriate parameters of the OSEM algorithm
defined in this study will be applicable to other 2D-PET
scanners if a radiotracer with a similar level of radioactivity is injected, because the total counts are generally
determined by injection radioactivity in 2D-PET scanners. The intrinsic spatial resolution of the PET scanner
will not be related to measurement of MBF and kmono
because the sizes of the ROIs that are drawn over the LV
cavity and the myocardial region are larger than thespatial resolution, and PET counts in the ROI are

sufficient for calculation of the MBF and kmono. However,the parameters defined in this study could not be applied
to three-dimensional (3D)-PET because, even with thesame injected radioactivity for a 2D-PET and a 3D-PET
scan, the total counts of 3D-PET, including several
scatter counts, will increase compared with those of
2D-PET with a septa system. In addition, the appropriate
parameters cannot be used in the OSEM algorithm when
the detectable level of radioactive counts increases
significantly on a PET scanner with a new detector andacquisition technology compared with conventional PET
scanners. We have not attempted to determine the effectof a loop filter in the OSEM algorithm in the General
Electric Medical System scanner because smoothing
GF strength is an essential factor for the reduction ofimage noise in PET images in other scanners. If analysis
of other detailed parameters of the OSEM algorithm are
required, such a study would necessitate a much larger
population of patients as well as evaluation of diagnostic
performance.

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Quantification of OSEM algorithm Kobayashi et al. 137

Fig. 5

(a)

(b)

(c)

(d)

Representative short-axis images for the calculation of (a) MBFFBP , (b) MBFOSEM, (c) kmonoFBP , and (d) kmonoOSEM in coronary artery disease
patients with ischemia of ANT and SEP walls. These images were summed using early-phase dynamic data for the calculation of MBF and laterphase data obtained 8–20 min after 11C-acetate injection for the calculation of kmono. FBP, filtered back-projection; MBF, myocardial blood flow;
OSEM, ordered-subset expectation maximization.

Conclusion
Application of the OSEM algorithm to a PET study yields
noiseless, artifact-reduction images and quantitative
PET values when the smoothing filter size and thenumbers of S and I are optimized. Parameters of OSEM
that are adequate for the measurement of MBFOSEM
and kmonoOSEM using 11C-acetate PET are 28S6I with a
10.0GF.

Fukui Brain Project, Research and Education Program forLife Science, University of Fukui.
Conflicts of interest

There are no conflicts of interest.

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Acknowledgements
The authors thank Satonao Nakakoji, Akira Ito, Hiroshi
Oikawa, and other staff of the Biological Imaging
Research Center, the University of Fukui, for theirtechnical support.
This study was partly funded by Grants-in-Aid for
Scientific Research from Japan Society for the Promotionof Science (19790862, 20249055, 21390342, 21591551,
21659286, 22659219, 22791180, and 23300361) and the

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