ORIGINAL ARTICLE
Quantification of regional myocardial blood flow
estimation with three-dimensional dynamic
rubidium-82 PET and modified spillover
correction model
Chietsugu Katoh, MD, PhD,a Keiichiro Yoshinaga, MD, PhD, FACC,b Ran Klein,
PhD,c,d Katsuhiko Kasai, BSc,a Yuuki Tomiyama, BSc,a Osamu Manabe, MD,
PhD,c Masanao Naya, MD, PhD,e Mamoru Sakakibara, MD, PhD,eHiroyuki Tsutsui, MD, PhD,e Robert A. deKemp, PhD,d and Nagara Tamaki, MD,
PhDc
Purpose. Myocardial blood flow (MBF) estimation with 82Rubidium (82Rb) positronemission tomography (PET) is technically difficult because of the high spillover between regionsof interest, especially due to the long positron range. We sought to develop a new algorithm toreduce the spillover in image-derived blood activity curves, using non-uniform weighted leastsquares fitting.
Methods. Fourteen volunteers underwent imaging with both 3-dimensional (3D) 82Rb and15
O-water PET at rest and during pharmacological stress. Whole left ventricular (LV) 82Rb
MBF was estimated using a one-compartment model, including a myocardium-to-blood spillover correction to estimate the corresponding blood input function Ca(t)whole. Regional K1
values were calculated using this uniform global input function, which simplifies equations andenables robust estimation of MBF. To assess the robustness of the modified algorithm, interoperator repeatability of 3D 82Rb MBF was compared with a previously established method.
Results. Whole LV correlation of 82Rb MBF with 15O-water MBF was better (P < .01) withthe modified spillover correction method (r 5 0.92 vs r 5 0.60). The modified method also
yielded significantly improved inter-operator repeatability of regional MBF quantification(r 5 0.89) versus the established method (r 5 0.82) (P < .01).
Conclusion. A uniform global input function can suppress LV spillover into the imagederived blood input function, resulting in improved precision for MBF quantification with 3D
82
Rb PET. (J Nucl Cardiol 2012)
Key Words: Myocardial blood flow Æ positron emission tomography Æ 82Rb Æ
spillover correction

From the Department of Health Sciences,a Department of Molecular
Imaging,b Department of Nuclear Medicine,c Cardiovascular
Medicine,e Hokkaido University Graduate School of Medicine,
Sapporo, Japan; and Division of Cardiology, National Cardiac PETCentre,d University of Ottawa Heart Institute, Ottawa, ON, Canada.
This study was supported in part by Grants from the Ministry of
Education, Science and Culture (No. 19591395), Northern
Advancement Center for Science & Technology (Sapporo, Japan)(#H19-C-068, H23-S2-17), and Adult Cardiovascular Foundation(Kyoto, Japan). Ran Klein was supported by the Japan Society for
the Promotion of Science (JSPS) and the Natural Sciences andEngineering Research Council of Canada (NSERC) Summer
Program (2008) (Tokyo, Japan and Ottawa, Ontario, Canada).

Received for publication Oct 12, 2011; final revision accepted Mar 29,
2012.
Reprint requests: Keiichiro Yoshinaga, MD, PhD, FACC, Departmentof Molecular Imaging, Hokkaido University Graduate School of
Medicine, Kita15 Nishi7, Kita-Ku, Sapporo, Hokkaido 060-8638,
Japan; kyoshi@med.hokudai.ac.jp.
1071-3581/$34.00
Copyright Ó 2012 American Society of Nuclear Cardiology.
doi:10.1007/s12350-012-9558-1

Katoh et al
Myocardial blood flow by 3D 82Rb PET

Journal of Nuclear Cardiology

INTRODUCTION

pre-test likelihood of CAD) (\5%) based on their risk
factors.23
The study was approved by the Hokkaido University
Graduate School of Medicine Human Research Ethics Board.
Written informed consent was obtained from all subjects.

Recently, our groups and others have shown thatmyocardial blood flow (MBF) can be estimated using
rubidium-82 (82Rb) positron emission tomography(PET)1-4 as well as PET performed using other flow
tracers.5 82Rb is a PET perfusion tracer produced from astrontium-82 (82Sr)/82Rb generator and is widely used
for the diagnosis of coronary artery disease (CAD)6-9 in
PET centers lacking immediate access to a cyclotron.
82
Rb PET has good diagnostic accuracy10-12 and also has
prognostic value in patients with CAD.13,14 The ability
to quantitatively measure regional and global left
ventricular (LV) MBF is one advantage of PET myocardial perfusion imaging.15,16 Quantitative MBF canindicate the functional severity of coronary stenosis andmay provide additional diagnostic information inpatients with CAD.15,17,18 It may also play an importantrole in the prevention of future cardiovascular
events.14,19
Some automated or semi-automated programs, suchas FlowQuant,2,20 MunichHeart,21 and PMOD,22 havebeen developed and applied for MBF estimation with82
Rb. Lortie et al2 validated MBF quantification using a
one-tissue-compartment model of 82Rb kinetics, compared to 13N-ammonia measurements in normal subjects
and CAD patients. Our program is one of several
validated automated programs for MBF quantification inhumans using 3-dimensional (3D) 82Rb PET. However,some variability in stress MBF has been noted inprevious studies. Therefore, there is a need to further
improve the analysis program for MBF quantification
with 82Rb especially in the 3D acquisition mode.
Current PET/computed tomography (CT) scanners
offer only a 3D data-acquisition mode. The highpositron energy and range, together with relatively
low-count statistics from the short tracer half-life, limit
the achievable spatial resolution and may impact theaccuracy of 82Rb MBF measurements due to spillover
effects. The purpose of this study was to develop a new
MBF quantification algorithm for 3D 82Rb dynamic
PET, and to validate it using two-dimensional (2D) 15O
water, which is a freely diffusible tracer with highextraction fraction at increased blood flow.15
MATERIALS AND METHODS
Study Subjects
Fourteen healthy men underwent 3D 82Rb and 2D 15Owater PET studies (mean age 31.6 ± 13.1 years). All participants had a normal resting electrocardiogram (ECG). They did
not have a history of cardiovascular disease and did not takeany cardiac medications. All healthy control subjects had a low

Study Protocol
Each subject underwent rest and pharmacological stressimaging using adenosine triphosphate (ATP), with both
15
O-water and 82Rb PET. The 2D 15O-water and 3D 82Rb
imaging were performed on separate days in randomized order(mean interval 17.2 ± 15.4 days).

PET Acquisition Protocol
Participants were instructed to fast for 6 hours and toabstain from caffeine-containing products for 24 hours prior to
PET studies.17,24,25 Participants were positioned with the heart
centered in the field of view of a whole body PET scanner(ECAT HR?, Siemens/CTI Knoxville, TN), which is capableof both 2D- and 3D-mode data acquisition.
15

O-Water Imaging

After a 6-minute transmission scan for attenuation correction, participants inhaled 2,000 megabecquerels (MBq) of
15
O-carbon-monoxide (CO) (0.14% CO mixed with room air)for 1 minute and were then imaged over 5 minutes to obtain ablood volume image.17,26 After decay of 15O radioactivity,
1,500 MBq of 15O-water was administered intravenously at aslow infusion rate (2 minutes) at rest, and a 20-frame dynamic
PET acquisition was initiated with varying frame durations(6 9 5 seconds, 6 9 15 seconds, 8930 seconds) over 6 minutes.16,25 All 15O-water imaging was performed in 2D modewith septa extended.
Pharmacological stress was induced by ATP (160 lg/kg/
minute 9 9 minutes).1 Three minutes after the start of theATP, a second dynamic scan was started using the same
dynamic sequence at rest. ATP was continued for a total of
9 minutes. During the entire exam, symptoms, heart rate, bloodpressure, and ECG were monitored continuously.

82

Rb Imaging

The 82Rb protocol was similar to that of 15O-water exceptthat immediately following the transmission scan, 555 MBq of82
Rb (BRACCO Diagnosis, Princeton, NJ or DRAXimage,
Kirkland, QC) was administered intravenously. An 8-minute23-frame dynamic scan was initiated with increasing frame
duration in 3D mode with septa retracted (18 9 10 seconds,
5 9 60 seconds).1,14,27
Pharmacological stress was induced by ATP (160 lg/
kg/minute 9 9 minutes) 10 minutes after the rest image
acquisition.1

Journal of Nuclear Cardiology

Quantification of MBF
Images were reconstructed using the vendor-supplied
filtered back-projection software (ECAT v7.2). We used a
10-mm Hann window of the ramp filter for 2D 15O-water
images, and a 12-mm Hann window of the ramp filter for
3D 82Rb images, respectively.2 Each frame consisted of 63
transaxial slices each having 128 9 128 voxels with dimensions 3.4 9 3.4 9 2.4 mm. MBF quantification was
performed using a software program developed in-house.28
Regions of interest (ROIs) for the LV cavity blood pool andthe LV myocardium were defined semi-automatically. The
LV wall was divided into 16 segments.16,27 The ROIs were
sampled in all timeframes to derive LV blood and myocardium time-activity curves. The program then performed
MBF quantification using a one-tissue-compartment tracer
kinetic model.27,28 The MBF quantification processes differed for 15O-water and 82Rb as described below.

15

O-Water ModelAll frames of the water image were summed, and theblood volume image of 15O-CO was then subtracted togenerate a myocardial uptake image. The uptake image wasused to reorient the original image to semi-automatically create
short-axis slices and to automatically create the myocardial
ROIs. The blood volume image was similarly reoriented andthen automatically segmented to define a blood pool ROI withintegral intensity equal to 85% of maximum pixel intensity.
The ROIs were applied to the entire dynamic image to generate
LV blood and regional myocardium time-activity curves(TACs), LV(t), and R(t), respectively. The MBF was estimatedas described in the ‘‘Appendix.’’

Katoh et al
Myocardial blood flow by 3D 82Rb PET
82

Rb ModelsEstablished method. With 82Rb, a myocardial

uptake image was generated by summing the last 5-8 minutesof the dynamic image sequence. A blood volume image wascreated by summing the first 2 minutes of the image
sequence.2 The images were reoriented and segmented semiautomatically to generate LV blood and myocardial TACs
using the dynamic data from 0 to 8 minutes.28
The MBF estimation is described in the ‘‘Appendix.’’
Wavelet-based noise reduction method. A
wavelet-based noise-reduction protocol was performed toeliminate noise in the measured TAC in the myocardial ROI
R(t) and in the left ventricular ROI LV(t).4 Two-compartment
model analysis was also applied and MBF was estimated basedon Lin’s approach.4
Modified or dual-spillover method. For
robust estimation of regional K1 and MBF, our method
calculates the K1 value using a global uniform input function
Cawhole(t), which is estimated from a whole myocardial ROI,
W(t) (Figure 1). Fitting Eqs. 1, 3, and 4 yielded K1, k2,
perfusable tissue fraction (PTF), and VA for the wholemyocardial ROI data, as well as the corresponding input
function Cawhole(t). The MBF was estimated using the global
uniform input function Cawhole(t) as described in the‘‘Appendix.’’
Thus, the established and the modified methods differedin two aspects. (1) The modified method first estimated a purespillover-corrected blood time-activity curve and then used it
for calculating flow in all LV regions, while the establishedmethod spillover-corrected the blood input function for eachmyocardial region independently. (2) The established method
used uniform timeframe weighting while the modified method
weighed each frame by the pure spillover-corrected blood

Figure 1. Scheme of the algorithm and sample data curves of the established method (A) and ourmodified method (B). A R(t) and LV(t) correspond to measured time-activity curves in a regional
myocardial ROI and in the left ventricle, respectively. An input function Ca(t), tissue time-activity
curve Ct(t), and regional MBF are derived from R(t) and LV(t). Different input function Ca(t) is
calculated for estimation of each regional K1 value. B W(t) corresponds to a measured time-activity
curve in the whole myocardial ROI. A uniform input function Cawhole(t) is derived from W(t) andLV(t). Then each regional MBF is estimated using this input function Cawhole(t) and R(t). The same
input function Cawhole(t) is applied for estimation of each regional K1 value.

Katoh et al
Myocardial blood flow by 3D 82Rb PET

Journal of Nuclear Cardiology

activity. The same LV blood and myocardial ROIs, andtherefore TACs, were used for processing with both theestablished and the modified methods.
The estimated MBF values from the established uniform
and the modified weighted methods were compared against
MBF values estimated from the corresponding 15O-water
images.

Segmentation Classification
Using quantitative coronary angiography (QCA) data, LV
segments were classified into 2 groups according to whether or
not they contained stenotic regions. Significant coronary
stenosis was defined as C70% luminal narrowing.

Statistical Analysis
Inter-operator Repeatability of 82Rb MBF
Each 3D 82Rb scan was processed by two operators using
both established and modified methods. The regional MBF
values obtained by the different operators were compared toassess inter-operator repeatability. The repeated studies were
performed independently by two nuclear physicians, one
novice and one expert. The novice user had never used theprogram of MBF quantification with 3D 82Rb data. The expert
user was the developer of this program.

Simulation Study
We performed a simulation study to evaluate the sensitivity of MBF to measurement errors in the parameter PTF, thetissue fraction in the LV myocardium ROI, using 15O-waterand both the established and the modified 82Rb methods.
The mean myocardial and LV TACs were generated using
data for the 20 subjects from 3D 82Rb PET studies. The
percentage error in MBF was calculated as a function of thepercentage error of the parameter PTF. The percentage error of
PTF was modified from -10% to ?10% and the % error in
MBF versus % error in PTF was plotted for each MBF
quantification method.

To assess the concordance between the 3D 82Rb MBF andthe 2D 15O-water MBF, Pearson’s correlation coefficients were
calculated. A Bland-Altman analysis was applied to evaluate
the agreement among the whole LV MBF values derived fromeach of these methods. To evaluate the inter-operator repeatability of the regional MBF with 3D 82Rb, Pearson’s
correlation coefficients were calculated and the Bland-Altman
analysis was applied for regional MBF. These two results wereapplied for the established method and the modified method.
The reproducibility coefficient (RPC%) was used to evaluate
agreement in results between the two operators. The differences between results using the established method and results
using the modified method were compared using Fisher ztransformation. All data were presented as mean and standard
deviations. A P value of\.05 was considered to be statisticallysignificant in the assessment of differences in correlation
coefficients and differences in variability.

RESULTS
The ATP stress was well tolerated by all participants. None of the participants had ischemic ECGchanges or chest pain during ATP stress.
Hemodynamics

Subjects with Known or Suspected CAD
To demonstrate the clinical utility of the global input
function model, 12 patients with known or suspected CAD
were recruited. At the time of these additional studies, 2D dataacquisition was still standard5 for PET MBF quantification
using 82Rb with an injected dose of 1480 MBq.29 Methods forimage processing and MBF estimation were the same as thosedescribed above for the healthy volunteers.

Heart rate and systolic blood pressure at rest weresimilar in 15O-water and 82Rb PET studies (Table 1). In
both studies, rate pressure product (RPP) significantly
increased during ATP stress (P \ .01). Table 1 shows
the variables of systolic blood pressure, heart rate, and
RPP in the 15O-water and 82Rb scans in resting andhyperemic states. No significant hemodynamic change
was observed during any of the dynamic acquisitions.

Table 1. Hemodynamics during 15O-water and 82Rb PET studies
82

Rb 3D Rest

HR
sBP
RPP

57.6 ± 5.3
111.1 ± 15.7
6409 ± 1143

82

Rb 3D Stress15

81.4 ± 11.7
108.2 ± 14.5
8812 ± 1780** P \ .01 versus rest.
HR, Heart rate; sBP, systolic blood pressure; RPP, rate pressure product.

O-Water Rest
57.1 ± 4.9
105.6 ± 14.9
6001 ± 793

15

O-Water Stress80.4 ± 9.4
103.3 ± 13.5
8296 ± 1362*

Journal of Nuclear Cardiology

Katoh et al
Myocardial blood flow by 3D 82Rb PET

Figure 2. A Correlation and Bland-Altman plots of MBF in the whole LV wall with 15O-water and82
Rb (established method). B Correlation and Bland-Altman plots of MBF in the whole LV wallwith 15O-water and 82Rb (modified method).
82

Rb Versus 15O-Water MBF

The global MBF values obtained from 14 subjects
at rest and during stress using the modified 82Rb method
correlated better with those obtained using 15O-water(r = 0.92) than with those obtained using theestablished method (r = 0.60), (P \ .01) (Figure 2).
Furthermore, the linear-regression slope was closer tounity with the modified method than with the establishedmethod (0.88 vs 0.44) indicating less bias (P \ .05).
Inter-operator Repeatability of 3D 82Rb
MBF
Significantly improved inter-operator repeatabilityof regional MBF quantification (448 segments fromfourteen subjects at rest and stress) was obtained using

the modified method (r = 0.89, RPC%=4.7%) than withthe established method (r = 0.82, RPC% = 5.6%)(P \ .01) (Figure 3).
Regional Heterogeneity of 3D 82Rb MBF
The previously established one-compartment model
showed significant regional heterogeneity, with higher
average 82Rb MBF at rest in the septal region(1.62 ± 0.52 mL/minute/g) than in the lateral region(1.28 ± 0.47) (P \ .01). The two-compartment modelwith wavelet-based noise reduction method also showedsome regional heterogeneity, with higher average 82Rb
MBF at rest in the septal region (1.47 ± 0.61
mL/minute/g) than in the lateral region (1.21 ± 0.37)(P \ .01). Our modified spillover correction method

Katoh et al
Myocardial blood flow by 3D 82Rb PET

Journal of Nuclear Cardiology

Figure 3. A Correlation and Bland-Altman plots of regional MBF test-retest repeatability with 3D82
Rb (established method). B Correlation and Bland-Altman plots of regional MBF test-retest
repeatability with 3D 82Rb (modified method).

showed improved regional homogeneity, with no significant differences in MBF in these same regions(1.18 ± 0.41, 1.09 ± 0.35) (P = ns) (Figure 4).

23% in MBF, while with the modified method theunderestimation was suppressed to 15%.
Subjects with Known or Suspected CAD

Simulation Studies of Sensitivity to Error in
PTF
Figure 5 shows the results of the simulation studies,
which demonstrate the effect of error in the parameter PTF
on MBF values for both the established and modified dualspillover methods. Relatively less error was induced in
MBF with the modified method. With the establishedmethod, an overestimation of 5% in PTF caused an
overestimation of approximately 25% in MBF, while withthe modified method the overestimation was suppressedto 10%. With the established method, an underestimationof 5% in PTF caused an underestimation of approximately

The CAD patient demographics have been presented previously.29 Briefly, the mean age of 12
consecutive patients was 70.8 ± 10.2 years (6 men).
Nine patients had known CAD and 3 patients had
suspected CAD. There was no significant difference inrest MBF determined using the established model versus
the global input function model (Table 2). The hyperemic MBF was lower in stenotic segments (P B .05)
than in non-stenotic segments as determined using the
global input function model as well as the establishedmodel. Coronary flow reserve (CFR) was also reduced in
the stenotic segments as determined using both models.

Journal of Nuclear Cardiology

Katoh et al
Myocardial blood flow by 3D 82Rb PET

Figure 4. An example subject whose regional MBF in the septal region yielded higher estimateswith the established method due to high spillover both from the right ventricle and from the left
ventricle. The modified method with a uniform input function Cawhole(t) and with weighted fitting,estimated more homogeneous regional MBF due to appropriate correction of spillover from theblood into the myocardium.

DISCUSSION
82

Rb and 3D dynamic PET may allow for MBF
quantification without a cyclotron.5 However, it has

been difficult to estimate MBF using 3D data acquisition
with high repeatability.29 In this study, we developed amodified method for MBF quantification with 3D 82Rb

Katoh et al
Myocardial blood flow by 3D 82Rb PET

Figure 5. Simulations demonstrate the effect of error in theparameter PTF (tissue fraction in the myocardial wall ROI)
ranging from -10% to ?10%. Lower error in estimated MBF
is observed with the modified method than with the conventional method.

using a dual-spillover model and blood-input-weighted
fitting. Using this modified algorithm, 3D 82Rb MBF
correlated well and agreed with 2D 15O-water MBF
values. This method also demonstrated improved interoperator repeatability over our previous establishedmethod,2 partly due to improved stability in estimating
regional MBF using 82Rb.
3D 82Rb MBF Measurements
Most of the current PET/CT scanners have only a
3D data-acquisition mode available. 3D mode acquisitions can reduce patient radiation exposure and can also
prolong the generator’s usable lifetime.30 On the other
hand, without septa in the 3D PET scanner, promptgammas, scatter, and the associated noise are increased,
which may cause deterioration in image quality and may

Journal of Nuclear Cardiology

increase variability. Therefore, 2D data acquisition has
been the standard approach for MBF quantification with82
Rb.1,5 Our group has performed 3D data acquisitionpreviously for MBF quantification using 82Rb. Hyperemic MBF is more variable than rest MBF,2 which may
be partially explained by reduced 82Rb extraction at
higher MBF.21 The relatively long positron range andspillover from the LV and right ventricular (RV) cavities
may also contribute to increased MBF variability with82
Rb. In this study, we designed a software algorithm fordefining a 3D ROI of the whole LV myocardium. A
myocardium-to-blood pool spillover correction algorithm was implemented to reduce bias in the LV blood
signal. Compared to the previous approach, rest andhyperemic MBF showed better correlation with thatusing 2D 15O-water as the standard for comparison. Our
modified method improved the repeatability coefficient;however, there was still relatively high variability using
our modified approach. Inter-operator variability using
other established PET myocardial perfusion tracers has
been reported. Previous reports showed lower variability
using O-15-labeled water (0.17 at rest and 0.43
mL/minute/g at stress).31 N-13 ammonia PET MPI also
showed less variability (0.20 at rest and 0.3 during coldpressor test).32 The variability in this study was nearly
1 mL/minute/g with combined rest and stress MBF,
which is higher than in previous reports. This larger
variability may depend on the physical characteristics of82
Rb, with a very short half-life, high-energy positronand 777 keV prompt gamma-ray.33 These result in highprompt-gamma and Compton scatter components, especially using 3D PET acquisition compared to previous
2D PET studies using O-15 water and N-13 ammonia.
Further technical improvement may be needed toovercome these physical characteristics of 82Rb and to
reduce the variability using 82Rb and 3D PET.
A wavelet-based noise-reduction protocol can
improve the signal-to-noise ratio of time-activity curves

Table 2. Regional MBF data in patients with CAD

Rest
Global input function
Established model2
StressGlobal input function
Established model2
CFR
Global input function
Established model2

Stenotic region (n 5 12)

Non-stenotic region (n 5 24)

P value

0.93 ± 0.15
0.91 ± 0.27

0.95 ± 0.17
0.94 ± 0.36

n.s.
n.s.

1.03 ± 0.28
1.13 ± 0.43

2.80 ± 0.61
2.67 ± 1.06

\.05
\.05

1.24 ± 0.39
1.28 ± 0.44

2.95 ± 0.55
2.87 ± 0.71

\.05
\.05

Journal of Nuclear Cardiology

derived from dynamic PET images with 82Rb.4 The twocompartment model with wavelet-based noise reduction
approach is an established method. This method has
been validated only for 2D data acquisition, however, it
could be expected to apply for 3D data acquisition as
well.
Effect of the Uniform Input Function
For robust estimation of regional K1 and MBF, ourmodified method calculated regional K1 values using auniform global input function Cawhole(t), which wasestimated from a 3D ROI of the whole myocardium(Figure 1). The previously established method estimatedthe input function independently for each regional
myocardial TAC, resulting in less stable MBF estimation. Increased stability with the modified method islikely due to less noise in the uniform pure blood curve.
In addition, having fewer free parameters in the optimization function likely reduces the regional variability ofparameters by improving numerical stability. We previously applied this global input function approach in 15Owater PET and were able to detect reduced blood flow
response in patients with CAD and Kawasaki disease.17,26 In this study, our global input function model
was able to detect reduced MBF in stenotic regions
during pharmacological stress as well as the establishedmodel using 2D 82Rb. Thus, we expect this global input
function method using 3D 82Rb should apply in patientswith CAD. However, this approach requires further
evaluation..
We designed an algorithm to set a 3D ROI of thewhole myocardium and divide it into 16 segments
automatically, thus removing some sources of variability
in the process, which is also very time-consuming if
performed manually. Currently, our ROI-setting software requires a few manual interactions, but it enables
near operator-independent ROI definition and stableregional MBF quantification.
Sensitivity of MBF to Error in PTF
A simulation study (Figure 5) showed lower sensitivity of MBF quantification to the tissue fraction (PTF)
in the LV wall ROI with the modified method than withthe established method. MBF operator variability was
also reduced with the modified method. Applying aweighted fitting and using a uniform input function
Cawhole(t) might contribute to the reduction of error. The
value (1 - b) is the contribution ratio of the whole
myocardial radioactivity and is an anatomical parameterfor the whole LV myocardium. Thus, in the modified
method, the parameter b was applied to the whole
myocardial data.28

Katoh et al
Myocardial blood flow by 3D 82Rb PET

Effect of the Weighted Fitting
To improve the stability of measurements ofregional K1 and MBF, the pure blood input curve
Cawhole(t) was adopted as the weighting function in the
nonlinear fitting analysis to suppress the spillover fromthe blood. Figure 4 showed that the established method
overestimated the septal MBF due to spillover fromthe blood activity in both right and left ventricles. The
modified method showed less bias from spillover in the
septal region. The error weighted by the blood activity
Cawhole(t) enforced strict tolerance in the curve-fitting
during the timeframes of high radioactivity in the blood.
The modified method enabled the suppression of spillover from the blood into the myocardium. Therefore,
our modified method might have a better correlation in
the repeated measurement values of regional MBF than
did the established method. This result implies that ourmodified method may also be able to detect regional
MBF reduction in patients with CAD.

Clinical Indications
PET MBF measurements represent a physiologicalapproach using a bioactive tracer that is non-invasive
and relatively simple to apply. In the past, these PET
measurements have been performed using either 13Nammonia or 15O-water with 2D PET imaging. However,13
N-ammonia and 15O-water require an on-site cyclotron. Thus, these approaches have not been widely
available in the clinical routine. 3D PET scanners arebeing installed on an increasing basis, and there are
more than 1000 PET or PET/CT scanners in North
America and Europe to date36 mainly for oncology
imaging. In these centers, it would also be possible toperform MBF measurements using 82Rb. 82Rb has a very
short physical half-life (76 seconds) and the generator
can provide radiotracer doses at 6-minute intervals,
allowing for repeat MBF measurements to be taken
within a very short time span.7 In the current protocol,
rest and pharmacological stress measurements werecompleted in approximately 30 minutes. Thus, 82Rb
PET coronary vascular function tests can be applied tomany patients in a clinical setting.34
Study Limitations
The principal difficulty in validating the new
method was the lack of an ex vivo gold standard todetermine the success of our algorithm. Therefore, we
relied primarily on the MBF data from 2D 15O-water
PET using our program to improve the accuracy andreliability of 82Rb MBF values with 3D data acquisitionin comparison to the standard values.28

Katoh et al
Myocardial blood flow by 3D 82Rb PET

Our ROI-definition software requires few manualhandling steps; it enables almost operator-independent
ROI definition thereby contributing to reproducible
regional MBF quantification. Consequently, there wasno significant difference in MBF results obtained by theexpert user and the novice user. A novice operator was
employed to verify the robustness of our ROI-setting
program, which yielded operator-independent estimates.
Intra-operator variability is expected to be less thaninter-operator variability with our nearly operator-independent ROI-definition algorithm.28
In this study, we performed 82Rb cardiac PET scans
without correction of misalignment due to respiratory
and cardiac motion between transmission and emission
images. This misalignment can cause errors in quantification of MBF. Further validations with respiratory andelectro cardiac gated imaging are needed.35,36
In this study, we acquired 82Rb data using an HR?
PET scanner. Compared to images from current PET/CT
scanners, the image quality may be slightly lower.
However, the HR? PET scanner can perform 2D and
3D data acquisitions. Head-to-head comparison is particularly important for this evaluation. Our new
approach can be applied in current PET/CT scanners
and might significantly enhance the ability to performroutine MBF quantification. This possibility requires
further investigation.
CONCLUSION
We have developed a modified method for regional
MBF estimation with 3D 82Rb, which was well correlated with that using 2D 15O-water, and demonstrated
good inter-operator repeatability. The algorithm allows
for simpler and more stable estimation of regional MBF
using 3D 82Rb PET, in comparison with previously
established methods.
Acknowledgments
The authors thank Sayaka Takamori, RT; Keiichi Magota,
RT; Hiroshi Arai, RT; Hidehiko Omote, RT; Kyotaro Suzuma,
MS; and Ken-ichi Nishijima, PhD, for their technicalexpertise, and Eriko Suzuki for her administrative support ofthis study. This study was supported in part by grants from the
Ministry of Education, Science and Culture (No. 19591395),
Northern Advancement Center for Science & Technology(Sapporo, Japan) (Grant #H19-C-068, H23-S2-17), and Adult
Cardiovascular Research Foundation (Kyoto Japan). Ran
Klein was supported by the Japan Society for the Promotion of
Science (JSPS) and Natural Sciences and Engineering
Research Council of Canada (NSERC) Summer Program(2008) (Tokyo, Japan and Ottawa, Ontario, Canada).

Journal of Nuclear Cardiology

APPENDIX
15

O-Water Model

The myocardium was modeled as a partial-volume
mixture of arterial blood Ca(t) and tissue Ct(t) activity
concentrations as in Eq. 1 (see below) where PTF
denoted perfusable tissue fraction, VA is the fractional
arterial blood volume, and q is the density of tissue(1.04 g/ml).
RðtÞ ¼ PTF  q  CtðtÞ þ VA  CaðtÞ

ð1Þ

The change in tissue activity concentration wasmodeled using the 1-tissue compartment model in Eq. 2(see below) where F denotes blood flow in mL/minute/g.
The parameter q is the partition coefficient of water inthe myocardium and is equal to 0.91.
dCtðtÞ=dt ¼ F  CaðtÞ  ðF=qÞ  CtðtÞ
ð2Þ
In the LV blood cavity, activity concentration wasmodeled as a partial-volume mixture of b = 85%
arterial blood and (1 - b = 15%) myocardial tissue asshown in Eq. 3.
LVðtÞ ¼ b  CaðtÞ þ ð1  bÞ  q  CtðtÞ

ð3Þ

Equations 1, 2, and 3 were solved with a nonlinear
least-squares analysis to estimate PTF, VA, and F,
which was used as the estimate of MBF.
82

Rb ModelsEstablished method. The measured tissue TAC
in each myocardial ROI, R(t), during the entire scan
length was estimated using Eq. 1. The change in tissue
activity concentration was modeled using the one-tissuecompartment modeldCtðtÞ=dt ¼ K1  CaðtÞ  k2  CtðtÞ
ð4Þ
where K1 (mL/minute/g) is the uptake rate from blood
into the tissue and k2 (/minute) is the washout rate frommyocardial tissue into the blood Ca(t) (Bq/mL). Radioactivity in the LV blood pool was calculated using Eq. 3
with b = 85%.
The parameters PTF, VA, K1, and k2 were derived
by nonlinear least-squares minimization using Eqs. 1, 3,
and 4, where PTF represents the tissue fraction in the LV
myocardium ROI.
Thus, the spillover-corrected pure blood Ca(t) for
each segment was estimated. Conversion from K1 to
MBF was estimated with the modified Renkin-Crone
model1,27 as shown in Eq. 5.
K1 ¼ MBF½1  0:86 exp ð0:543=MBFÞ

ð5Þ

Journal of Nuclear Cardiology

Katoh et al
Myocardial blood flow by 3D 82Rb PET

Modified or dual-spillover method. Regional
myocardial ROI data, R(t), were then analyzed using
Eqs. 6 and 7.
RðtÞ ¼ PTF  q  CtðtÞ þ VA  Cawhole ðtÞdCtðtÞ= dt ¼ K1  Cawhole ðtÞ  k2  CtðtÞ

ð6Þ
ð7Þ

The regional myocardial ROI curve and left ventricular ROI blood curve (R(t) and LV(t), respectively)
were sampled from the dynamic image as in theestablished conventional method and were assumed to
be a linear combination of the uniform blood Cawhole(t)and myocardium Ct(t) as in Eqs. 1 and 3. In addition, therelationship between Cawhole(t) and Ct(t) was defined by
the one-tissue-compartment model as in Eq. 7.
The parameters PTF, VA, K1, and k2 were simultaneously estimated by minimizing the weighted error(e) as shown in Eq. 8 between the measured curve R(t)and the model in Eq. 6. The error was weighted by theblood activity concentration Cawhole(t) to enforce strict
tolerance in the curve fitting during the timeframes
corresponding to high radioactivity in the blood.
e ¼ R½Cawhole ðtÞ  ðPTF  q  CtðtÞ þ VA
 Cawhole ðtÞ  RðtÞÞ2

ð8Þ

K1 values were converted into MBF using the
Renkin-Crone extraction function as in Eq. 5.3
The estimated MBF values from the establisheduniform and modified weighted methods were comparedagainst MBF values estimated from the corresponding
15
O-water images.
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