Effect of Bayesian-penalized likelihood reconstruction on [13N]-NH3 rest perfusion quantification
Effect of Bayesian-penalized likelihood reconstruction on [13N]-NH3 rest perfusion quantification
Jim O' Doherty 0
Daniel R. McGowan 0
Carla Abreu 0
Sally Barrington 0
0 Reprint requests: Jim O' Doherty, PET Imaging Centre, Division of Imaging Sciences and Biomedical Engineering, King's College London, King's Health Partners, St. Thomas' Hospital , 1st Floor, Lambeth Wing, London SE1 7EH , United Kingdom
1 Department of Oncology, University of Oxford , Oxford , United Kingdom
2 Radiation Physics and Protection, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust , Oxford , United Kingdom
3 PET Imaging Centre, Division of Imaging Sciences and Biomedical Engineering, King's College London, King's Health Partners, St. Thomas' Hospital , London , United Kingdom
Objectives. Myocardial blood flow (MBF) imaging is used in patients with suspected cardiac sarcoidosis, and also in stress/rest studies. The accuracy of MBF is dependent on imaging parameters such as new reconstruction methodologies. In this work, we aim to assess the impact of a novel PET reconstruction algorithm (Bayesian-penalized likelihood-BPL) on the values determined from the calculation of [13N]-NH3 MBF values. Methods. Data from 21 patients undergoing rest MBF evaluation [13N]-NH3 as part of sarcoidosis imaging were retrospectively analyzed. Each scan was reconstructed with a range of BPL coefficients (1-500), and standard clinical FBP and OSEM reconstructions. MBF values were calculated via an automated software routine for all datasets. Results. Reconstruction of [13N]-NH3 dynamic data using the BPL, OSEM, or FBP reconstruction showed no quantitative differences for the calculation of territorial or global MBF (P 5 .97). Image noise was lower using OSEM or BPL reconstructions than FBP and noise from BPL reached levels seen in OSEM images between B 5 300 and B 5 400. Intrasubject differences between all reconstructions over all patients in respect of all cardiac territories showed a maximum coefficient of variation of 9.74%. Conclusion. Quantitation of MBF via kinetic modeling of cardiac rest MBF by [13N]-NH3 is minimally affected by the use of a BPL reconstruction technique, with BPL images presenting with less noise. (J Nucl Cardiol 2017;24:282-90.)
OSEM FBP BPL
Ordered subsets expectation maximization Filtered back projection Bayesian-penalized likelihood
See related editorial, pp. 291–293
Myocardial blood flow
Image-derived input function
Coronary flow reserve
Copyright 2016 The Author(s). This article is an open access
Quantitative myocardial blood flow (MBF) imaging
with PET is utilized in many centers around the world in
a clinical setting for investigation of the human coronary
circulation.1-3 The technique allows for the quantitative
assessment of the distribution of flow for delineation of
the extent and severity of coronary artery diseases,
microvascular function, as well as other conditions such
as cardiac sarcoidosis. Radiological imaging detects
cardiac involvement in almost 40% of patients with
sarcoidosis and is more sensitive than ECG, Holter
monitoring, and echocardiography,4 with a high
sensitivity and specificity reported for [18F]-FDG PET of 89%
and 78%, respectively.5 Quantification of MBF has been
routinely used as an aid in suspected cardiac sarcoidosis
in order to rule out coronary artery disease or to identify
resting perfusion defects suggestive of
inflammationinduced tissue damage.6 Studies have also shown that
there is a characteristic uptake pattern in the myocardium
of patients with active cardiac sarcoidosis.7
‘‘Eyes-tothighs’’ FDG PET-CT scanning is further used to detect
any noncardiac granulomatous involvement (such as
pulmonary), with atlases recently published showing the
imaging features and patterns,8 and comprehensive
imaging protocols have also recently been published.6
New commercial PET reconstruction algorithms are
continuously being developed and investigatied, with
their operation potentially affecting the final voxel
values presented in PET images. Many new commercial
methods include data corrections such as point-spread
function modeling of the entire PET field of view aimed
at improving spatial resolution during image
reconstruction.9,10 One such example of this, and of interest to this
work, is a new Bayesian-penalized likelihood (BPL)
reconstruction algorithm developed by GE Healthcare
(commercially named Q.Clear ). The technique
involves point-spread-function modeling with noise
modeling controlled through the use of a penalty term
that penalizes image intensity differences between
neighboring pixels. The penalty function is controlled
by a unitless ‘beta value’ (henceforth called ‘‘B’’ in this
work) which adjusts the strength of the regularizing term
in the objective function of the reconstruction,11 and is
the only input to the reconstruction algorithm rather than
traditional iterations, subsets, and post-reconstruction
smoothing filters of iterative OSEM algorithms. The
algorithm is allowed to run to effective convergence,
allowing for an improved quantitative accuracy of
imaging rather than suspending the algorithm after a
certain number of iterations to control the image noise.
Previous work has shown how BPL reconstruction
algorithms provide improved signal-to-noise (SNRs)
and signal-to-background ratios (SBRs) in the imaging
of colorectal liver metastases,12 lung nodules,13 and
mediastinal lymph nodes.14 Results of these studies
showed significant increases in the average maximum
standardized uptake value (SUVmax) after BPL
processing. Earlier work has focused on oncology [18F]-FDG
PET and found a B value of 400 to be optimum.15 Image
reconstruction has been shown to have an effect on
resulting activity concentration in cardiac studies,16 and
previous work has shown differences in [13N]-NH3
MBF of up to 11% between FBP and OSEM
reconstructions.17 MBF has also been investigated in relation
to technological changes such as 2D and 3D PET,18 and
on different software packages for [13N]-NH3 MBF
calculations,19 although to the best of our knowledge, no
studies have yet investigated the use of new PSF
reconstruction methods in dynamic [13N]-NH3 cardiac
studies, or investigated the effects of different B values
for these studies. The aim of this study was therefore to
evaluate the quantitative effect on calculated MBF
values of employing the BPL algorithm with different
penalization factors on [13N]-NH3 images with a range
of B values, compared to our imaging standard of FBP
Patients and Scanning
Clinical scans from 21 patients (8 female, 13 male, mean
age 50.4 ± 12.5 years) imaged for suspected cardiac
sarcoidosis comprising dynamic [13N]-NH3 scans were retrospectively
analyzed. No selection criteria were applied to the patients.
Only members of the clinical team, in compliance with the UK
Data Protection Act, reviewed patient data, and consequently,
specific Research Ethics Approval was not required. Patients
were asked to fast for 12 hours as MBF imaging was combined
with an [18F]-FDG viability imaging study, which required
minimization of myocardial glucose uptake. All data were
acquired on a GE Discovery 710 PET-CT scanner (GE
Healthcare, Waukesha, USA) at Site 1 (St Thomas’ Hospital,
London, UK). BPL reconstruction via Q.Clear was not
available at Site 1, and thus, RAW PET sinograms and PET
calibration files were sent to Site 2 (Oxford University
Hospitals, Oxford, UK) for reconstruction as outlined below.
A cine-CT was acquired (100 kVp, 10 mA, 0.5-second
rotation, 5.5-seconds cine duration, and 40-mm collimation) for
attenuation correction, and was reconstructed to 2.5-mm
contiguous slices (704 images). ECG gating was not used for PET
or CT imaging. Following the cine CT, patients were injected
with an average activity concentration of 527 ± 24 MBq of
[13N]-NH3, and 3D PET was acquired in listmode.
Attenuation correction of [13N]-NH3 PET images was
performed using an average of the acquired cine CT, a
wellpublished method to correct for potential respiratory
registration artifacts in [13N]-NH3 cardiac imaging by matching CT
and PET temporal resolutions.20 Due to enzymatic conversion
of ammonia to glutamate, the final 20 minutes’ duration of the
26 minutes acquisition was used to reconstruct a single static
frame of 47 slices (not part of this analysis), which was then
used to determine the registration vector between the PET and
the average cine CT using ACQC (Attenuation Correction
Quality Control) software present on the PET-CT scanner. CT
and PET images were manually aligned to provide the best
visual registration and the resulting shift vector was then
applied to the reconstruction of the dynamic data. An
experienced clinician reviewed the images of each registration as
part of a standard clinical protocol.
List-mode PET data were re-binned to 12 9 10 seconds,
6 9 20 seconds, 2 9 1 minutes and 1 9 20 minutes. Only
the first 6 minutes’ duration of data was used for MBF
calculation. For clinical reporting of MBF, images were
reconstructed at Site 1 using a filtered back-projection (FBP)
algorithm with a 6.4-mm post-smoothing filter (enhanced
Hanning). We also reconstructed a time-of-flight OSEM
dataset (2 iterations, 24 subsets, 6.4-mm Gaussian filter) for
comparison. RAW PET sinograms and average cine CT
datasets were transferred to Site 2, who then reconstructed
the sinograms with the same FBP and OSEM reconstruction
parameters for quality assurance (to ensure that MBF values
were equivalent from both sites), as well as performing BPL
reconstructions with values of B = 1, 50, 100, 200, 300, 400,
and 500 (guided by earlier work on [18F]-FDG imaging). Site
2 employed the normalization, and dose calibrator files from
Site 1 that were required for image reconstruction.
Reconstructed images were then sent to Site 1 for final MBF
Image and Data Analysis
Myocardial blood flow (MBF) values were calculated
from the reconstructed dynamic [13N]-NH3 data using
SiemensMBF software (Siemens Medical Solutions, Erlangen,
Germany) on a Syngo workstation. The software employs a
2tissue compartment [13N]-NH3 kinetic model with four
parameters (1 vascular volume and 3 transport coefficients)
describing extraction and retention of [13N]-ammonia in
myocardial tissue.21 After loading data into the software, the
program automatically performed segmentations of myocardial
walls and also placed a VOI in the left ventricle for
determination of an image-derived input function (IDIF). No
manual adjustments were made to the automatically segmented
volumes. Values of MBF (mL minute-1 g-1) were assigned to
the standardized American Heart Association (AHA)
17segment model, and vascular territories were defined based on
the standard division of the polar map.22
Figure 1 shows the same single short axis, vertical
long axis and horizontal long axis slice from the
summed 20-minute images from FBP, and also from
the increasing B value from BPL reconstructions.
Increased levels of smoothing can be seen with the
increasing B value. Figure 2 demonstrates example
results of the automated segmentation procedure on
each reconstruction, showing successful
segmentation over the entire range of B values independent
of the levels of smoothing resulting from the
Examples of the image IDIFs required to drive the
kinetic model used in quantification software are shown
in Figure 3, where the effects of the reconstruction
algorithms on image noise (assessed by ±1 standard
deviation of the mean of the region of interest) can
clearly be observed. Noise was seen to decrease with the
increasing B value, as shown by example in Figure 4,
where the standard deviation of a single IDIF has been
averaged (using the 12 9 10 seconds frames only) for a
single patient. When repeated for all patients, OSEM
levels of noise in IDIF and myocardial uptake curves
were observed between B = 300 and B = 400. We used
FBP reconstruction as a gold standard, as is common in
dynamic cardiac PET imaging utilizing kinetic
modeling, due to low system bias, reliable quantitative
capabilities and ability to cater for rapid time varying
changes in activity concentration.16 Using this
methodology, calculation of the area under the curve
(AUC—a critical parameter for performing PET
compartmental kinetic modeling23) of the input function for
all reconstructions (FBP, OSEM, B = 1, 50, 100, 200,
300, 400, 500) showed a maximum difference over all
patients of 12.2% lower with B = 50. The cumulative
AUC is shown in Figure 5. A minimum difference over
all patients of 6.1% lower than FBP with B = 400 in the
case shown was determined. Although FBP showed a
consistently higher AUC, maximum and minimum
differences in AUC between FBP and BPL algorithms
did not correspond with any specific B value.
On performing FBP and OSEM reconstructions at
Site 1 and independently for the same reconstructions at
Site 2, automated analysis resulted in identical MBF
values. This was an important step showing that RAW
PET data was transferrable, and reconstruction at Site 2
was equivalent in terms of scanner normalization files
and corrections, and therefore, the only changes
affecting the reconstruction was the implementation of the
MBF values for each vascular cardiac territory, left
anterior descending (LAD), left circumflex (LCX), and
right coronary artery (RCX) as well as a global MBF
values were automatically computed by the kinetic
modeling software. Overall, MBF from each patient
using all reconstructions were closely correlated, with a
range of coefficient of variation (CoV) from 1.95%
(min) - 9.74% (max) over all vascular territories and
also globally. A box-plot of all MBF values is shown in
Figure 6. One-way ANOVA results comparing a single
parameter (image reconstruction) in each vascular
territory (LAD, LCX, and RCX) showed P [ .95 for MBF
values in all vascular territories, as well as in the global
MBF, indicating that MBF values from all
reconstructions for each patient were statistically equivalent. There
were also no clearly defined bias trends resulting from
any of the reconstructions.
We reconstructed and analyzed all images at a 128
matrix as part of the standard clinical protocol, which
enabled a high speed of reconstructing 21 dynamic and
many static datasets for each patient. However, we
reconstructed all data for five of the dynamic
[13N]NH3 studies (selected at random) using 256 matrix size
in order to investigate any effect on MBF, which
produced a maximum difference in global MBF of
3.9% over all 5 patients (2.4% in LAD, 2.8% in LCX,
and 3.1% in RCX over all five datasets, respectively).
Thus, the matrix size of 128 has only a minor effect of
the resulting MBF values. None of the five patient
datasets showed any large change in MBF values on
the higher matrix size, either for vascular territory or
We have provided to the best of the authors’
knowledge, the first comparisons of a new fully
converged BPL reconstruction algorithm on [13N]-NH3
cardiac MBF values, showing that MBF quantification
of the PET data is not significantly affected over the
range of penalization factors of B = 1 to B = 500 in
BPL reconstruction. Images such as those shown in
Figure 1 show increased smoothing at higher B values.
As noted in previous work, due to the higher
regularization penalty factor, images will appear smoother at
the expense of causing blurring of the reconstructed
images.15 However, as shown in Figure 2, the automatic
segmentation routine was still able to reproducibly
segment the myocardium from the images throughout
the range of examined B values. The effects of the
increased image noise of the IDIF as assessed by ±1
standard deviation of the values within the VOI shown
in Figure 3 shows that although the mean IDIF is
consistent over the range of B values, the standard
deviation on each point decreases with the increasing B
value. Similar noise relationships with the increasing B
values were obtained for the myocardial uptake curves
In our experience of using a commercially
produced MBF quantitation program, the software reports
an error value, although it is the error related to the
fitting of MBF parameters resulting from the kinetic
modeling process (K1), and is not inclusive of the noise
in the IDIF or myocardial uptake curve. As shown in
Figures 3 and 4, the effects of reconstruction can cause
a large amount of noise in the resulting VOIs, and
therefore should be accounted for by any analysis
software. However, as shown by Figures 5 and 6,
accounting for only the average activity concentration
and no associated noise in the VOI, both the area under
the IDIF and the resulting global and territorial MBF
values do not experience significant changes with the
increasing B value. For the patient with a maximum
AUC difference of 12.2% compared to standard FBP
reconstruction, this difference led to a difference of
6.3% in estimation of MBF in the LCX territory (0.63
and 0.59 mL minute-1 g-1 for FBP and B = 400,
Our cohort of potential sarcoidosis patients did not
represent an optimal cohort to assess the BPL
reconstructions in [18F]-FDG viability imaging due to the
intentional suppression of physiological [18F]-FDG
uptake in the heart in order to minimize false-positive
results. In this state, free fatty acids account for up to
90% of oxygen consumption of normal myocytes,7 and
this technique in patients without cardiac sarcoid
involvement produces images primarily of the blood
pool, with poor myocardial uptake of FDG, except in
cases where sarcoidosis was present, or in patients with
suboptimal preparation. However, as cardiac [18F]-FDG
imaging is only performed normally for sarcoidosis
purposes at our center, patients undergoing viability
imaging with [18F]-FDG for other purposes (such as
coronary artery disease) could be used to investigate if
and how the BPL reconstruction affects the uptake
pattern of the [18F]-FDG.
As is customary in clinical cardiac studies, caution
should be taken that PET data is attenuation corrected
and respiratory corrected as appropriate, as inaccurate
attenuation correction has been known to affect
quantitation of cardiac PET studies.20,24 This study focused
on clinically acquired data attenuation corrected with an
average of a free-breathing cine CT acquisition as
recommended in ASNC guidelines,25 however, there are
many other strategies for providing AC and also
respiratory gating26 with comparisons of methods being
carried out.20,27 In our work, all data originated from the
same raw sinograms, and thus, any motion affecting
imaging would affect all reconstructions in the same
manner, and hence it is the relative difference in MBF
that was of interest. However, the use of a standardized
dynamic test methodology to examine differences in
reconstruction while remove the confounding effects of
motion artifacts, and allowing a focus purely on the
kinetic analysis would be a welcome addition to
accurately clarify a comparison of reconstructions.
Investigations with the use of a simplistic nonmoving
cardiac perfusion phantom have recently been
performed in MR imaging, and would provide a useful
standard by which to compare reconstructions and
benchmark kinetic modeling software analysis routines
free from the considerations of motion artifacts.28 A
further potential source of error is the scanner being
affected by the high count rate due to all of the
[13N]NH3 activity being in the field of view at the same time.
This would potentially increase the dead time of some of
the PET detectors during the initial frames of imaging,
affecting the input function used to drive the kinetic
model.29 Although this was not explicitly accounted for
in this work, dead time would have affected all of the
reconstructions in the same manner, and therefore was
disregarded for this analysis.
Our study represents preliminary data investigating
this novel reconstruction algorithm, and should be
further explored with a larger dataset. However,
multiinstitutional comparison of PET MBF studies remain
limited by differences in tracers, kinetic models,
technical methodology, image analysis software, and
pharmacological vasodilating agents.1,3 For example,
the reported PET stress MBFs in normal individuals
vary from 1.86 ± 0.27 to 5.05 ± 0.90 mL g-1 minute-1,
with a 27% weighted average coefficient of variation for
single measurements.30 Some standardization of MBF
quantification has recently been performed by way of
comparing image analysis software with [82Rb]-Cl31 or
[13N]-NH3 data19 acquired at a single site. These
studies show good correlation between the MBF values
resulting from different analysis packages. Options for
improving methodological standardization such as
image reconstruction deserve careful study and may
prove an important factor of the overall ultimate clinical
utility of absolute MBF measurements. Also of potential
interest in the future may be the use of 4D parametric
image reconstruction, allowing for the reconstruction of
parametric images from cardiac studies directly from
sinograms,32,33 which potentially allow motion
compensation along with a better estimation of the kinetic
parameters than the traditional indirect approach of
using frame-by-frame reconstruction and curve fitting to
regional time-activity curves.
Our study has some limitations, with the foremost
being that we analyzed the results of only 21 patients
without stress MBF (and thus CFR) in this small study.
However, as the image data originate from the same raw
sinogram data, the effect of intra-subject biological
variation was removed. Over these 21 patients, we
showed a minor influence of BPL reconstruction in the
automated quantification of MBF. Furthermore, rest
perfusion imaging for sarcoid diagnosis is a niche
application of MBF quantification, and efforts should be
extended to investigate the effects of the BPL
reconstruction with stress-rest studies, and any potential effect
on the variation of the myocardial flow reserve (MFR)
ratio. Although we expect the low coefficient of
variation between reconstructions to be independent of flow
rate, we currently lack the data to validate this claim.
Furthermore, resulting image quality was not assessed in
this work, and such an assessment of the blurring of high
B value images using the BPL algorithm may be useful
for identification of perfusion defects. A subjective
imaging score by a trained physician could be used to
visually compare the static [13N]-NH3 reconstructions
in terms of their image quality rather than solely
quantification of MBF. We have shown that for MBF
calculations in our dataset, OSEM and FBP datasets are
equivalent and produce comparable values, as has been
shown by previous cardiac studies using low count
density data in [15O]-H2O and [18F]-FDG studies,34,35
and at-rest MBF values (\3.4 mL g-1 minute-1) from
[13N]-NH3 studies.17 However, for our datasets, we
now extend this equivalency to include PSF modeling
via the BPL reconstruction algorithms investigated in
this work as these algorithms begin to gain more clinical
availability and prevalence.
New PET reconstruction algorithms such as the
BPL technique which allow for effective convergence of
image accuracy while also suppressing noise through the
use of a penalization factor are becoming more
commonplace in clinical PET imaging. However, the use of
these reconstruction techniques have not yet been
assessed in the quantification of MBF. There is a
requirement to demonstrate that quantification of MBF
is at least as good using BPL reconstruction as the
current gold standard, which due to linearity reasons at
highly changing activity concentrations in our case is an
NEW KNOWLEDGE GAINED
Our study of MBF in 21 patients undergoing
dynamic rest [13N]-NH3 imaging showed that using a
PET reconstruction algorithm that runs to effective
convergence with noise suppression, such as the BPL
algorithm employed in this work, does not lead to
significant differences in the quantification of rest MBF.
We have also identified that the BPL algorithm with a B
value of 300 gives the same level of noise in the image
as a standard clinically used OSEM algorithm.
Our work from this study shows that the effects of
employing a BPL reconstruction in [13N]-NH3 cardiac
PET data do not have a significant affect on the
quantification of the rest MBF over all cardiac
territories. The coefficient of variation over the entire
reconstructions was found to be a maximum of 9.74%,
and the use of the BPL algorithm with the increasing B
value produced images with less image noise. Noise
equivalence to standard OSEM reconstruction was
achieved with a B value of 300.
The authors are grateful to the radiography staff at St.
Thomas’ Hospital PET Centre for the collection of cardiac
JOD acknowledges financial support from the
Department of Health through the NIHR Biomedical Research Centre
award to Guy’s & St. Thomas’ NHS Foundation Trust in
partnership with King’s College London and King’s College
Hospital NHS Foundation Trust and The Centre of Excellence
in Medical Engineering funded by the Wellcome Trust and
EPSRC under Grant Number WT 088641/Z/09/Z. DM is
funded by a National Institute for Health Research (NIHR)/
Health Education England (HEE) Healthcare Scientist Chief
Scientific Officer Doctoral Award, and is supported by the
CRUK Oxford Centre C5255/A18085. This paper presents
independent research funded by the NIHR. The views
expressed are those of the authors and not necessarily those
of the NHS, the NIHR, HEE, or the Department of Health.
This article is distributed under the terms of the Creative
Commons Attribution 4.0 International License (http://creative
commons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided you give
appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons license, and indicate if
changes were made.
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