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Nuclear Medicine and Biology 36 (2009) 877 – 882
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Validation of the calculation of the clearance rate constant (kmono) of[11 C]acetate using parametric kmono image for myocardial
oxidative metabolism☆
Raihan Hussain, Takashi Kudo⁎, Testuya Tsujikawa, Masato Kobayashi,
Yasuhisa Fujibayashi, Hidehiko Okazawa
Biomedical Imaging Research Center, University of Fukui, Fukui, Japan
Received 14 April 2009; received in revised form 9 June 2009; accepted 2 July 2009

Abstract
Introduction: The purpose of this study was to validate the calculation of myocardial oxidative metabolism rate using a parametric clearance
rate constant (kmono) image.
Methods: Fifteen subjects (seven volunteers, eight patients) were studied. Dynamic PET was acquired after intravenous injection of 700
MBq of [11C]acetate. The clearance rate constant of [11C]acetate (kmono) was calculated pixel by pixel to generate the parametric kmono image.
The kmono values from this image and those calculated from the dynamic image were compared in the same regions of interest (ROIs).
Results: Two different methods showed an excellent correlation except in the very low range. Regression equations were y=0.99x+0.0034(r2=0.86, Pb.001) and y=1.16x−0.0077 (r2=0.87, Pb.001) in normal volunteer and patient groups, respectively, and y=1.07x−0.0019(r2=0.87, Pb.001) when combined.
Conclusions: Both methods exhibited similar values of kmono. Parametric kmono image may result in better visual understanding of regional
myocardial oxidative metabolism.
© 2009 Elsevier Inc. All rights reserved.
Keywords: [11C]Acetate; Parametric image; Oxidative metabolism; Cardiology; PET

1. Introduction
The noninvasive quantification of myocardial oxidative
metabolism, particularly on a regional basis, is of criticalimportance in both cardiovascular investigation and clinicalcardiology [1]. [11C]Acetate avidly accumulates in the
myocardium and then rapidly enters the tricarboxylic acid
(TCA) cycle. The 11C label is released as 11CO2 at a rate
proportional to the oxidation rate of the TCA cycle [2,3].
Since mitochondrial oxidation is tightly coupled to oxidative
phosphorylation, and since other pathways for metabolism of☆

The authors attest that there is no financial support to be disclosed.
There are no commercial associations that might pose a conflict of interest.
⁎ Corresponding author. Tel.: +81 776 61 8491; fax: +81 776 61 8170.
E-mail addresses: raihan_h@yahoo.com (R. Hussain),tkudo@u-fukui.ac.jp (T. Kudo).
0969-8051/$ – see front matter © 2009 Elsevier Inc. All rights reserved.
doi:10.1016/j.nucmedbio.2009.07.002

acetate by the heart are modest, the oxidation of 11C-acetate
to 11CO2 and subsequent clearance from the heart provide anindex of myocardial oxidative metabolism [4]. This
clearance of [11C]acetate can be noninvasively determinedusing dynamic positron emission tomography (PET).
A clearance rate constant of [11 C]acetate, usually
represented as kmono, is usually measured at regions ofinterests (ROIs) on the dynamic count-based image andrepresented only as numerical values. But images instead ofmere numerical values would be more useful in a clinicalsetting, especially in viability assessment. Thus, we developed a method for pixel-by-pixel generation of parametrickmono image using logarithmic conversion of original
dynamic counts. The purpose of this study was to validatea method to calculate kmono using parametric kmono images
and to determine whether it is reliable or not in comparison
with common ROI measurement methods on the dynamic
count-based image.

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2. Materials and methods
2.1. Subjects
Fifteen subjects, seven young male healthy volunteers(age range: 21 to 36 years; mean: 28.9±6.7 years) and eightpatients (male=7, female=1; age range: 60 to 87 years;
mean: 73.0±10.3 years) with coronary artery disease(CAD) with known myocardial infarction, were studied.
The study was approved by the Ethics Committee of Fukui
University Hospital. Written informed consent was
obtained from each subject.
2.2. PET Procedures
PET scans were performed with a conventional wholebody PET scanner (ADVANCE, General Electric Medical
System, Milwaukee, WI, USA), which permitted simultaneous acquisition of 35 image slices in a two-dimensional
acquisition mode with interslice spacing of 4.25 mm [5].
Performance tests showed the intrinsic resolution of thescanner to be 4.6 to 5.7 mm in the transaxial direction and 4.0
to 5.3 mm in the axial direction. At first, a 10-mintransmission scan was performed using 68Ge/68 Ga forattenuation correction before the tracer administration.
Then, about 700 MBq of [11C]acetate was slowly administered intravenously over 30 s, and dynamic acquisition was
done (18×5-, 7×30-, 5×60- and 5×120-s frames). Transaxial
slices were collected in a matrix size of 128×128 pixels. The
data were reconstructed with the filtered back-projection
method using a Hanning filter with 6.0 mm full-width
resolution at half maximum in the transaxial direction [6].
2.3. Calculation of kmono values
To achieve the kmono values, we applied two different
methods. The first method used the original dynamic images.
Data acquired between 3 and 8 min (when the activity was
maximum) after administration of the tracer were summed
together to form a composite image, which was only used toplace ROIs in the proper position. A total of 24 ROIs (8×3slices) were placed over the anterior, anteroseptal, septal,
inferior, posterior and lateral walls of this image to obtain thetime–activity curve (TAC). It is a well-known fact that kmono
obtained from the equation q=Ae−kt (where q=count/pixel
per minute corrected for physical decay, k=kmono and t=time)[7] is an index of myocardial oxidative metabolism. A
monoexponential function was fit to the TAC of these ROIs,
and the values of kmono were determined. The monoexponential fit begins at the time when the blood pool activity
becomes low and TAC shows clear washout. In several
reports, curve fitting begins at 2 to 4 min after injection.
However, we found that excluding the data acquired before
400 s after injection usually resulted in better fitting.
Considering this, we used data acquired 8–20 min after
injection for the calculation of kmono. Typical TAC andmonoexponential fitting are presented in Fig. 1.

Fig. 1. Typical myocardial TAC of [11C]acetate PET. Dots indicate actualtime–activity curve. The solid line indicates exponential curve fitting using
data 3–20 min after injection. The dashed line indicates fitting using data 8–
20 min after injection. The inset is the same graph displayed with log scale.

The second method utilized the kmono parametric image,which was constructed by calculating the pixel-by-pixel
kmono values using the same time frames used in the first
method. Usually, exponential fitting is necessary to calculate
kmono. To simplify the pixel-by-pixel mathematical calculation, logarithmic conversion of image count was used. With
this conversion, the equation previously described isconverted to the following linear function: log q=log A(−kt). Thus, we can easily calculate the kmono values pixel by
pixel by linear fitting. The background noise and blood-pool
region were masked to avoid unnecessary calculation of
extramyocardial pixels. The generated parametric kmono
image was then smoothed using a 6-mm full width at half
maximum Gaussian filter to reduce image noise. The average
kmono value of 24 ROIs in each subject was also calculated toperform subject-by-subject analysis.
2.4. Statistical analysis
The kmono values measured with the two methods werecorrelated by linear least-square regression analysis. Differences between the kmono values obtained with the two
methods were also assessed by paired t test and the Bland–
Altman plot method. All values were expressed as means±S.
D. Pb.05 was considered statistically significant. Statisticalanalysis was performed using the Statistica 06 package(Statsoft, Tulsa, OK, USA).

3. Results
3.1. Comparison of kmono values
Fig. 2 shows the serial images from count-based
reconstruction at the mid-ventricular level after administration of [11C]acetate in normal (upper row) and infarcted
myocardium (bottom row). Fig. 3 shows the corresponding
kmono images of the same subject.

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879

Fig. 2. Serial dynamic images from count-based reconstruction at the mid-ventricular level in normal myocardium (top row) and infarction in the apical anteriorwall (bottom row). Approximately 700 MBq of [11C]acetate was injected and the heart was imaged starting at the time of injection for 20 min.

Averages of the kmono values in each subject obtained by
the two different methods showed an excellent correlation.
The kmono values measured with parametric images were0.061±0.014, and those measured with dynamic images were0.063±0.014. The coefficient of determination (r2) was 0.95
(Pb.001), and the slope of the regression equation was very
close to the line of identity (slope=1.008) (Fig. 4).
The kmono values in each ROI were also compared(Fig. 5). Those obtained by the two different methods
showed a slight but significant difference when each ROI
was analyzed (0.061±0.018 vs. 0.063±0.020, Pb.01 for theparametric kmono image and dynamic image, respectively).
An excellent correlation was found between the two
methods except in the very low range (r 2 =0.87,
slope=1.07, Pb.001). When normal volunteers and CAD
patients were separately analyzed, the kmono values were

Fig. 3. The parametric kmono images of the same subjects presented in Fig. 2(left column) and reoriented into vertical long axis view (right column). Notethat the low kmono value is visible on the image of a CAD patient. To create
these parametric images, approximately 700 MBq of [11C]acetate wasinjected and the heart was imaged starting at the time of injection for 20 min.

0.065±0.019 vs. 0.068±0.021 (Pb.01, normal group) and
0.057±0.015 vs. 0.058±0.019 (Pb.01, CAD group),
obtained from the parametric kmono image and dynamic
image, respectively. Correlation was still excellent evenwhen normal volunteer and CAD groups were analyzed
separately (r2=0.86, slope=0.99, Pb.001 for the normal
volunteer group; r2=0.87, slope=1.16, Pb.001 for the
CAD group).
3.2. Bland-Altman analysis
The Bland–Altman plot between the kmono values
obtained from the two methods is presented in Fig. 6.
When all the ROIs were analyzed separately, the plot still

Fig. 4. The averaged kmono values in each subject are plotted in a scatter
chart with kmono from a parametric image (ROIs) along the x-axis and from a
dynamic image along the y-axis. The r2 values, P values and equations areshown in the graph.

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showed good agreement between the two measurements
although it showed slightly strained distribution in the very
low range, suggesting that the parametric kmono image
tended to show higher values than the ROI method in thevery low range of kmono.
4. Discussion
The current study shows that kmono calculated on a pixelby-pixel basis using logarithmic conversion of original
dynamic counts and that obtained with the help ofmonoexponential fitting of the dynamic TACs of raw countdata over the same ROIs have very good correlation. With
this technique, parametric images of kmono can be createdreliably and easily. This kmono parametric image can provide
an easy visual clue for assessing myocardial metabolism,
which is required in daily practice such as viability
assessment. The visual assessment of viability using flow/
metabolism mismatch criteria becomes possible with a single[11C]acetate PET acquisition. The only exception was in thevery low range, where parametric kmono values tend to behigher (overestimated) than dynamic kmono values. In normal
subjects, homogenous clearance of [11C]acetate throughout
the myocardium was seen on the parametric kmono image,whereas in patients with myocardial infarction, it wasdiminished in the infracted zones which is indicative ofdiminished myocardial oxidative metabolism as reported
previously [7].
[11C]Acetate PET permits the evaluation of myocardial
oxidative metabolism which provides important information
about cardiac disease [8]. One of the most important issues
in clinical nuclear cardiology is the assessment of myocardial viability. For the myocardial viability assessment, flow
metabolism mismatch detected with FDG-PET underglucose loading (and perfusion imaging) is considered thegold standard [9–12]. However, FDG imaging has several
difficulties; for example, it requires feeding status control
and has poor image quality in diabetic patients. [11C]Acetate
has been proposed as an alternative marker with minimal
substrate dependency [11]. 11C-Acetate is superior to FDG
because it does not require feeding status control and iseasily applicable to diabetic patients. It has been reported
that viable segments after myocardial infarction on FDGPET have preserved oxidative metabolism [13,14]. Additionally, a study reported that the segments without
contractile reserve but with preserved glucose metabolism
show a reduction in the oxidative metabolic response tostress [15], which suggests superior specificity of [11C]
acetate in viability assessment.

Fig. 5. The kmono values are plotted in a scatter chart with kmono from aparametric image along the x-axis and from a dynamic image along the yaxis. The r2 values, P values and equations are shown in (A) normal
myocardium, (B) myocardial infarction and (C) normal myocardium andmyocardial infarction combined together.

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Fig. 6. Bland–Altman plot comparing the two methods. The averaged kmono in each subject shows excellent agreement between the two methods without
systematic deviation from 0 (Panel A). When each ROI is analyzed separately (Panel B), the Bland–Altman plot shows a trend in which kmono values measured
from parametric kmono images tend to be higher than those measured from dynamic images in very low kmono range. There is a minor but significant bias from 0.

To assess the viability, visual inspection is usually
performed, especially when flow/metabolism mismatch
criteria are used. So representing kmono in numerical values
only is not sufficient: representing in image format ispreferred. Clinicians rely extensively on visual judgment,
and parametric kmono image could have an added advantage
in this respect. The imaging guidelines from the American
Society of Nuclear Cardiology recommend combined
evaluation of perfusion and FDG images for viability
assessment. This comparison is usually performed withqualitative image comparison [16]. Thus, parametric imagesof biological processes have been shown to facilitate
quantitative analysis in the clinical environment [17,18].

[11 C]Acetate also allows accurate quantification of
myocardial perfusion at rest as well as under stress conditions[19]. We previously reported that quantitative parametricimage of myocardial perfusion is easily generated using theearly-phase dynamic image of [11C]acetate PET [20]. So it isattractive as a single-tracer technique that yields quantitative
data and images on perfusion and metabolism and thus mayhelp curtail the use of double-tracer studies [11].
Our method of creating kmono parametric images showed
a good correlation with kmono measured on original dynamic
image using the ROI and TAC method, except in the very
low range, as mentioned before. These areas corresponded
to known areas of infarction, where the oxidative metabo-

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lism was negligible and possessed very low counts. This
low-count statistic might be a cause of error on calculation.
However, both methods gave values far below the normal
range; so, clinically, this difference seems to be of noimportance. We noted from the paired t test that there was aslight systematic underestimation of kmono obtained fromthe parametric kmono image compared to that obtained fromthe ROI method. Because pixel-by-pixel calculation is very
susceptible to noise, we performed image smoothing for
kmono parametric image generation. This smoothing might
have resulted in slight underestimation. However, theamount of underestimation was only about 3.7%. Thus,
we believe that it should not cause any problem as theunderestimation is not random, but is systematic.
5. Conclusion
Myocardial acetate clearance rate constant obtained fromparametric image and that obtained from the ROI method on
dynamic image in healthy volunteers as well as in patientgroups exhibited similar values. Moreover, the parametrickmono image may result in better understanding of regional
myocardial oxidative metabolism than using just the kmono
numerical values. With the use of parametric kmono images,
visual inspection of metabolism is easily performed inclinical practices such as viability assessment.
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