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O R I G I N A L R E S E A R C H Open Access
Phase analysis single-photon emission
computed tomography (SPECT) myocardial
perfusion imaging (MPI) detects
dyssynchrony in myocardial scar and
increases specificity of MPI
John P. Bois
1,3*
, Chris Scott
2
, Panithaya Chareonthaitawee
1
, Raymond J. Gibbons
1
and Martin Rodriguez-Porcel
1
Abstract
Background: Myocardial perfusion imaging (MPI) with single-photon emission computed tomography (SPECT) is
commonly used to assess patients with cardiovascular disease. However, in certain scenarios, it may have limited
specificity in the identification of hemodynamically significant coronary artery disease (e.g., false positive), potentially
resulting in additional unnecessary testing and treatment. Phase analysis (PA) is an emerging, highly reproducible
quantitative technology that can differentiate normal myocardial activation (synchrony) from myocardial scar
(dyssynchrony). The objective of this study is to determine if PA can improve the specificity SPECT MPI.
Methods: An initial cohort of 340 patients (derivation cohort), referred for SPECT-MPI, was prospectively enrolled.
Resting MPI studies were assessed for resting perfusion defects (scar). These were utilized as the reference standard
for scar. Subsequently, we collected a second independent validation cohort of 138 patients and tested the potential
of PA to reclassify patients for the diagnosis of “scar”or “no scar.”Patients were assigned to three categories depending
upon their pre-test probability of scar based on multiple clinical and imaging parameters: ≤10% (no scar), 11–74%
(indeterminate), and ≥75% (scar). The ability of PA variables to reclassify patients with scar to a higher group and those
without scar to a lower group was then determined using the net reclassification index (NRI).
Results: Entropy (≥59%) was independently associated with scar in both patient cohorts with an odds ratio greater
than five. Furthermore, when added to multiple clinical/imaging variables, the use of entropy significantly improved
the area under the curve for assessment of scar (0.67 vs. 0.59, p= 0.04). The use of entropy correctly reclassified 24% of
patients without scar, by clinical model, to a lower risk category (as determined by pre-test probability) with an overall
NRI of 18% in this validation cohort.
Discussion: The use of PA entropy can improve the specificity of SPECT MPI and may serve as a useful adjunctive tool
to the interpreting physician. The current study determined the optimal PA parameters to detect scar (derivation
cohort) and applied these parameters to a second, independent, patient group and noted that entropy (≥59%) was
independently associated with scar in both patient cohorts. Therefore, PA, which requires no additional imaging time
or radiation, enhances the diagnostic capabilities of SPECT MPI.
(Continued on next page)
* Correspondence: Bois.john@mayo.edu;bois.john@mayo.edu
1
Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
3
Department of Cardiovascular Diseases, Mayo Clinic College of Medicine,
200 First Street SW, Rochester, MN 55905, USA
Full list of author information is available at the end of the article
© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.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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(Continued from previous page)
Conclusion: The use of PA entropy significantly improved the specificity of SPECT MPI and could influence the labeling
of a patient as having or not having myocardial scar and thereby may influence not only diagnostic reporting but also
potentially prognostic determination and therapeutic decision-making.
Keywords: Coronary artery disease, Nuclear cardiology and PET, Diagnostic testing
Background
Coronary artery disease (CAD) remains the leading
cause of mortality in the USA [1]. To accurately diag-
nose and risk stratify patients with CAD, it is imperative
that clinicians have access to diagnostic imaging tech-
niques that are not only sensitive but also specific.
Nine million myocardial perfusion imaging (MPI) ex-
aminations are performed annually in the USA [2] with
a sensitivity and specificity for the detection of CAD of
approximately 85% and 70%, respectively [3]. Results of
MPI testing have critical diagnostic, prognostic, and
therapeutic ramifications [4–9].
Despite the excellent sensitivity of MPI, its specificity has
been called into question. Motion artifact [10], excessive
subdiaphragmatic activity [11], breast attenuation [12], and
asymmetric ventricular wall thickening [13] can lead to per-
ceived perfusion defects and thereby decrease specificity
[14]. This, in turn, may result in possibly unnecessary inva-
sive procedures and medical treatment with potentially
increased morbidity and mortality. Furthermore, given the
approximate $2000 cost per MPI study [15], some authors
have concluded that the “risk-benefit ratio for stress testing
is not convincing.”[3] These challenges call for further
refinement of MPI, to optimize sensitivity and specificity of
these studies.
Phaseanalysis(PA)isanautomated[16], highly reprodu-
cible [17], and repeatable [18] software application which
can be applied to traditional gated SPECT MPI. It utilizes
the partial volume effect [19] and Fourier first harmonics
to assess the onset of myocardial contraction at over 600
myocardial locations thereby determining myocardial
synchrony [16]. It is independent of the type of imaging
camera [20], reconstruction algorithm [21], or tracer dose
utilized [22]. PA has been shown to detect dyssynchrony in
the heart failure population [23]andmaybeofvaluein
suspected CAD. Specifically, myocardial infarction will
result in regional left ventricular (LV) contractile disparity
and hence myocardial dyssynchrony [24–26], even before
there is visual regional myocardial contractility dysfunction.
The objectives of the current study are the following:
First, to determine the presence of dyssynchrony, by PA, in
patients with myocardial scar on resting SPECT MPI. Sec-
ond, to define optimal PA dyssynchrony parameters, from
a derivation cohort, which will have the greatest specificity
for the detection of scar. Third, to apply these criteria to a
separate cohort (validation cohort) to determine the clinical
utility (degree of patient reclassification, scar vs. no scar)
that results when PA is added to the original SPECT MPI
results as determined by expert analysis.
Methods
Patient selection
ThestudywasapprovedbytheMayo Clinic Institutional
Review Board (IRB). Patients with known or suspected
CAD who were referred for SPECT MPI at a single tertiary
referral center (Mayo Rochester) between August 2014 and
November 2016 were eligible for the study. Those patients
who underwent SPECT MPI between August 2014 and
September 2015 were assigned to the derivation cohort;
patients enrolled between October 2015 and November
2016 were assigned to the validation cohort. Patients were
included if they were (1) ≥18 years of age, (2) were clinic-
ally referred for SPECT MPI, and (3) consented to study
participation. Patients with atrial fibrillation/flutter, left
bundle branch block (LBBB), paced rhythm, chronic resyn-
chronization therapy (CRT), or depressed left ventricular
ejection fraction (LVEF) were intentionally included in this
study to permit broad application of the study’sfindings.
Demographic, clinical, and imaging data was abstracted
from the clinical records on all patients.
SPECT image acquisition protocol and image processing
Resting blood pressure, heart rate, and electrocardiogram
(ECG) were obtained for all patients. For resting images,
8–10 mCi technetium-99m (
99m
Tc) sestamibi was intraven-
ously administered. Forty-five to 60 minutes following
injection, patients underwent upright (seated) and semi-su-
pine gated imaging. All data was acquired utilizing a
D-SPECT camera (Spectrum Dynamics, Haifa, Israel). Im-
ages were obtained as each of the nine pixelated detector
columns rotated along its vertical axis and scanned the
region of myocardium which was designated by the user.
Sixteen frames per cardiac cycle were obtained, and data
from each detector was stored in a 16 × 64 matrix with data
from the nine detectors combined for final image recon-
struction. Emission data was obtained via nine low energy,
tungsten square hole collimators. Images were acquired
using a standard 20% energy window centered on the 140
keV photopeak of
99m
Tc.
Studies were processed using Spectrum Dynamics pro-
prietary reconstruction algorithms (Quantitative Perfusion
SPECT-QPS-, Cedars-Sinai Medical Center, Los Angeles)
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on a dedicated Spectrum Dynamics workstation. Recon-
struction algorithm utilized the maximum-likelihood ex-
pectation-maximization (MELM) method with resolution
recovery, 4–7 iterations and 32 subsets. An additional ker-
nel convolution smoothing filter (Gaussian) was used on
transaxial data. No attenuation or scatter correction was
applied.
Systolic function and perfusion interpretation
Quantitative Gated SPECT (QGS) software package was
utilized to automatically calculate left ventricular (LV)
end-diastolic volume, LV end-systolic volume, and LVEF
from the short-axis images as previously described [27]. Im-
ages were displayed in three planes (short-axis, horizontal
long-axis, and vertical long-axis) and subsequently divided
into 17 segments [28]. QPS analysis was implemented to
create LV myocardial perfusion maps that included auto-
mated calculation of the summed rest score (SRS). Scar
quantification (SQ) was assessed by utilizing a previously
validated method which entails calculating the fraction of a
myocardial profile at five short-axis slices from the apex to
the base of the heart and labeling the fraction of the profiles
falling below a threshold value of 60% as scar [29].
In our laboratory, the determination to report a resting
defect was predicated upon the integration of both clinical
factors and imaging data by the study interpreter. Specific-
ally, each study was reviewed in the three available planes
(in both the supine and semi-supine position) by a con-
sensus of an experienced nuclear cardiologist and nuclear
cardiac radiologist (each with at least 10 years of experi-
ence) utilizing both qualitative and quantitative assess-
ment of perfusion defects. Stress and rest perfusion data
was simultaneously interpreted. QGS data was incorpo-
rated to assess for regional wall motion abnormalities, and
QPS data was reviewed to note the automated SRS and
IQ scores. In cases of disagreement between the auto-
mated perfusion data and the clinical interpretation, prior-
ity was given to the clinical interpretation. Following
integration of this information, a final perfusion score for
each segment was assigned by consensus. Defects that
were felt to be artifact (attenuation or other) such as those
involving the proximal septum or the inferior wall and
those with completely normal wall thickening were re-
ported as normal. A previously described 5-point scoring
system was used to assess each of the 17 cardiac segments
(4 = absent, 3 = severely diminished, 2 = moderately dimin-
ished, 1 = mildly diminished, and 0= normal) [30]. This
final SRS, which takes into account the quantitative SRS
but is not necessarily the same as it can be modified by
the interpreting physician, was calculated by adding the
scores of all 17 segments. Any score > 0 was considered
scar as this is reported as such to the referring physician
at our institution. Furthermore, the objective of the
current study is to assess the presence of dyssynchrony in
any size perfusion defect. Clinical reporting of a resting
perfusion defect was considered the “reference standard”
for detection of scar. Mild scar was defined as a SRS of 1–
4, moderate 5–8, and severe > 8. Both physicians evaluat-
ing myocardial perfusion were blinded to the PA data.
Phase analysis
As described previously, a 3-D count distribution was
obtained from the LV short-axis dataset and subjected to
Fourier analysis resulting in the generation of a phase
distribution completely encompassing the R-R interval
(0–360°) [16]. Utilizing automated software (QGS 3.0;
Cedars-Sinai Medical Center, Los Angeles CA), a phase
histogram and polar map were created portraying the on-
set of myocardial contraction (OMC) for greater than 600
points in LV myocardium. Three indices of LV synchrony
were automatically calculated from the phase histogram:
phase histogram bandwidth (PHB) which portrays the
range of degrees of the cardiac cycle during which myocar-
dium is initiating contraction [31], phase standard devi-
ation (PSD) which represents the standard deviation of the
phase distribution [31], and entropy which is a measure of
the variability in the histogram [32]. One of the study au-
thors (JB) reviewed the PA data and was blinded to corre-
sponding perfusion studies and interpretations.
Pertinent clinical and imaging variables
Clinical risk factors which demonstrated correlation with
obstructive CAD in the NCDR Cath-PCI Registry were
assessed for each patient. These included age, gender,
hyperlipidemia, insulin dependent diabetes mellitus, per-
ipheral vascular disease, smoking history, family history of
premature CAD, and presentation of typical angina symp-
toms [33]. Since the NCDR did not include other variables
that were significant in the Duke databank [34], history of
CAD (any degree), prior history of myocardial infarction
(including ST and non-ST elevation myocardial infarction),
and the presence of Q waves or ST depression on resting
ECG were included as pertinent variables, along with QPS
SRS. Regardless of their association with scar in the current
small study population, all of the above variables were
included in the clinical model [35].
Statistical analysis
Categorical variables were summarized by count and per-
centage and were compared between groups using Pearson
chi-square test or Fisher exact test, where appropriate.
Distributions of continuous variables were examined for
normality. Variables found to be approximately normally
distributed were summarized by mean and standard devi-
ation and compared between groups using two-sample t
test. Continuous variables found to be non-normally
distributed were summarized by median and quartiles and
compared between groups using non-parametric rank-sum
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test. Univariable logistic regression analysis was initially
performed to assess whether an association between dys-
synchrony parameters (PHB, PSD and entropy) and the
presence or absence of scar was present. Results were
summarized as odds ratio (OR) and associated 95% confi-
dence intervals. Receiver-operator characteristic (ROC)
analysis was used to determine optimal thresholds,
weighted for higher specificity in order to minimize
false-positive results as has been described [36], for each
of the dyssynchrony parameters for the detection of scar
within the derivation cohorts. The dyssynchrony parame-
ters at optimal thresholds were then evaluated in addition
to the clinical and imaging parameters using logistic re-
gression in both the derivation and the validation cohorts.
We then assessed the improvement in ROC with dyssyn-
chrony parameters compared to pertinent clinical and im-
aging risk factors alone. Lastly, reclassification of the
patients’diagnosis of scar or no scar by PA variables was
assessed in the validation cohort. Specifically, patients
were assigned to three categories based upon their
pre-test probability of scar as calculated by the designated
clinical and imaging parameters: ≤10% (low risk or no
scar), 11–74% (indeterminate), and ≥75% (high risk or
scar). The ability of PA variables to reclassify patients
with scar to a higher group and those without a scar to
a lower group was then determined by calculating the
net reclassification index (NRI) [37]. Analyses were
completed using SAS version 9.4 (SAS Institute Inc.).
Statistical significance was set a priori at p<.05 and
two-sided pvalues were used.
Results
Total study population—baseline variables
There was a total of 478 patients in the study, 340 in the
initial cohort (derivation), and 138 in the second cohort
(validation) (Fig. 1). Baseline clinical, laboratory, and
imaging variables between the two cohorts were similar
(Table 1). The majority of the patients were male with a
mean age of 67–68 years. Approximately half of the pa-
tient population had a history of coronary artery disease,
two-thirds hypertension and hyperlipidemia, and one
quarter of the patients had diabetes mellitus (7% insulin
dependent). Mean LVEF by SPECT (60%) was the same
between both the derivation and the validation cohorts.
Derivation cohort
One hundred and five patients (31%) in the derivation
cohort had scar as detected by SPECT MPI. All three
PA parameters, entropy, PHB, and PSD, were associated
with scar on univariable analysis (Table 2). Specifically,
all three PA parameters were progressively higher (indi-
cating greater dyssynchrony) as SRS increased, indicat-
ing greater extent and severity of the perfusion defect
(Table 3). ROC analysis was then utilized to determine
the thresholds for each PA variable to optimize specifi-
city for the detection of scar with the resulting variables
ranging in specificity from 86% to 91% (Table 4).
Each of the phase analysis parameters was independ-
ently associated with scar on multivariable analysis
(Table 5). When compared to clinical/imaging variables,
entropy had the greatest association with scar with the
Fig. 1 Study design. The total study population was 478 patients. Patients enrolled between August 2014 and September 2015 were included in
the training group. Patients enrolled from October 2015 to November 2016 were included in the validation group (138 total). Both groups
underwent resting SPECT MPI and were subsequently classified as having a scar or not having a scar based upon their resting SPECT MPI
interpretation. Abbreviations: MPI, myocardial perfusion imaging; SPECT, single-photon emission computed tomography.
Bois et al. EJNMMI Research (2019) 9:11 Page 4 of 11
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Table 1 Baseline clinical, laboratory, and imaging variables between the two cohorts
Variable Derivation (N= 340) Validation (N= 138) pvalue
Scar, n(%) 105 (31%) 40 (29%) 0.68
Demographics
Age 67.3 (12.3) 68.3 (12.0) 0.40
Male gender, n(%) 232 (68%) 98 (71%) 0.55
Caucasian, n(%) 310 (91%) 126 (91%) 0.96
Past medical history
Coronary artery disease, n(%) 161 (47%) 64 (46%) 0.85
STEMI/NSTEMI, n(%) 50 (15%) 18 (13%) 0.64
Prior PCI, n(%) 96 (28%) 32 (23%) 0.26
Prior CABG, n(%) 42 (12%) 19 (14%) 0.67
HFrEF, n(%) 51 (15%) 23 (17%) 0.65
HFpEF, n(%) 7 (2%) 5 (4%) 0.32
ICD, n(%) 10 (3%) 1 (1%) 0.14
CRT, n(%) 1 (0%) 0 (0%) 0.52
Pacemaker, n(%) 12 (4%) 4 (3%) 0.73
Hypertension, n(%) 215 (63%) 86 (62%) 0.85
Hyperlipidemia, n(%) 236 (69%) 86 (62%) 0.13
Smoking, n(%) 167 (48%) 57 (42%) 0.33
OSA, n(%) 65 (19%) 34 (25%) 0.18
COPD, n(%) 21 (6%) 6 (4%) 0.43
Diabetes, insulin dependent, n(%) 27 (8%) 7 (5%) 0.27
Dialysis, n(%) 6 (2%) 1 (1%) 0.39
Peripheral vascular disease 41 (12%) 16 (12%) 0.89
Atrial fibrillation/atrial flutter, n(%) 57 (17%) 33 (24%) 0.07
Ventricular tachycardia, n(%) 6 (2%) 4 (3%) 0.43
Family history of premature CAD, n(%) 46 (14%) 14 (10%) 0.31
Valve disease (moderate or greater), n(%) 28 (14%) 10 (11%) 0.50
Medications
Aspirin, n(%) 211 (62%) 96 (70%) 0.12
Beta-blocker, n(%) 187 (55%) 75 (54%) 0.90
Ticagreloror/plavix/prasugrel, n(%) 53 (16%) 21 (15%) 0.92
Statin, n(%) 222 (65%) 84 (61%) 0.36
CCB, n(%) 60 (18%) 28 (20%) 0.50
Diuretic, n(%) 105 (31%) 46 (33%) 0.60
ECG/imaging
ECG Q wave, n(%) 17 (5%) 4 (3%) 0.31
ECG ST depression, n(%) 19 (6%) 6 (4%) 0.58
ECG paced, n(%) 19 (6%) 9 (7%) 0.69
ECG LBBB, n(%) 18 (5%) 13 (9%) 0.09
ECG atrial fibrillation/atrial flutter, n(%) 31 (9%) 20 (14%) 0.08
Nuclear EF 59.6 (11.9) 60.4 (12.4) 0.53
QPS SRS, median (Q1, Q3) 0.5 (0.0, 3.0) 0.0 (0.0, 2.0) 0.20
Clinical Presentation
Typical symptoms, n(%) 28 (8%) 13 (10%) 0.62
Continuous variables expressed as mean ± standard deviation for symmetric data and median (interquartile range) for asymmetric data. Categorical variables
expressed as count and percentage of patients
Abbreviations:CABG coronary artery bypass grafts, CAD coronary artery disease, COPD chronic obstructive pulmonary disease, CRT cardiac resynchronization
therapy; ECG electrocardiogram, ICD implantable cardioverter defibrillator, HFpEF heart failure with preserved ejection fraction, HFrEF heart failure with reduced
ejection fraction, LBBB left bundle branch block, LVEF left ventricular ejection fraction, NSTEMI non-ST elevation myocardial infarction, OSA obstructive sleep apnea,
PCI percutaneous intervention, QPS Quantitative Perfusion SPECT, SRS summed rest score, STEMI ST elevation myocardial infarction
Bois et al. EJNMMI Research (2019) 9:11 Page 5 of 11
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highest odds ratio (OR = 5.04). Clinical/imaging variable
models were evaluated with and without inclusion of the
phase variables (employing phase analysis cut-off points
determined from the ROC analysis noted in Table 4).
When including entropy to the model, the area under
the curve (AUC) for the detection of scar improved from
0.62 to 0.69 (p= 0.005) (Fig. 2).
Validation cohort
Forty patients (29%) in the validation cohort had scar, and
98 (71%) did not have scar as detected by SPECT MPI. The
number of patients with scar as detected by SPECT MPI
was not different between the two cohorts (31% vs. 29%, p
= 0.68). Importantly, entropy (≥59%) remained independ-
ently associated with scar on multivariable analysis of the
validation cohort with an OR of 5.96. Furthermore, addition
of entropy to the clinical/imaging model improved the
AUC from 0.59 to 0.67 (Fig. 3) with a total study population
NRI of 10% (p= 0.04). For the 98 patients without scar, the
addition of entropy (< 59) to the clinical/imaging model
correctly reclassified 23 (24%) patients to a lower risk
category, incorrectly classified 5 (5%) patients to a higher
risk category, and did not change classification for the
remaining 70 (71%) of patients resulting in a NRI of 18 (p
= 0.04). For the forty patients with scar, 6 (15%) were
correctly reclassified to a higher risk category, 9 (22.5%)
were incorrectly reclassified to a lower risk category, and 25
(62.5%) were not reclassified with a resultant NRI of −8%.
Discussion
This study shows the potential of PA as an adjuvant tool
for the accurate detection of myocardial perfusion defects
in patients with known or suspected CAD. To our know-
ledge, the current investigation is the largest study that
uses PA to assess myocardial scar utilizing resting SPECT
MPI and is the first study to demonstrate its impact on
patient reclassification.
When considering the prognostic and therapeutic impli-
cations of diagnosing a patient as having myocardial scar
[5,8], it is critical to avoid “false-positive”reports which
may actually be attributable to artifacts. Phase analysis
SPECT MPI is an automated, quantitative, repeatable, and
reproducible means by which to assess LV dyssynchrony
during SPECT MPI without the need for additional
imaging time or radiation exposure. Until recently, the
predominant areas of investigation utilizing PA have been
in assessing its ability to predict responders to cardiac
resynchronization therapy [23]. However, emerging litera-
ture has suggested it may be a potentially useful adjunct to
SPECT MPI in the evaluation of patients with known or
suspected CAD. In fact, previous studies have shown that
PA can aid in the detection of ischemia by demonstrating
Table 2 Derivation cohort—association between scar and
phase analysis, univariable analysis
Variable No scar
(N= 235)
Scar
(N= 105)
pvalue
Entropy (%) 45.0 (39.0, 51.0) 51.0 (45.0, 62.0) < .001
Phase histogram
bandwidth (°)
42.0 (36.0, 60.0) 54.0 (42.0, 96.0) < .001
Phase standard
deviation (°)
11.9 (8.5, 19.5) 17.4 (10.6, 25.0) < .001
Continuous variables expressed as median (interquartile range)
Table 3 Derivation cohort—perfusion defect severity and
dyssynchrony
Variable Mild (1–4)
a
(N= 66)
Moderate
(5–8)
b
(N= 18)
Severe (8+)
c
(N= 21)
pvalue
Entropy (%) 48.5 (43.0,
56.0)
55.5 (47.0,
71.0)
62.0 (55.0,
69.0)
< .001
Phase histogram
bandwidth (°)
48.0 (36.0,
72.0)
75.0 (42.0,
138.0)
96.0 (72.0,
138.0)
< .001
Phase standard
deviation (°)
12.7 (9.9,
22.0)
19.7 (11.2,
37.6)
23.5 (17.8,
27.8)
0.002
Continuous variables expressed as median (interquartile range)
a
Mild scar defined as summed rest score of 1–4
b
Moderate scar defined as summed rest score of 5–8
c
Severe scar defined as summed rest score > 8
Table 4 Derivation cohort—phase variables sensitivity and
specificity
Variable Sensitivity (N)
(N= 235)
Specificity
(N= 105)
AUC
Entropy ≥59% 35.2 (37/105) 90.6 (213/235) 0.629
Phase histogram bandwidth ≥78° 38.1 (40/105) 85.6 (202/235) 0.620
Phase standard deviation ≥26.7° 23.8 (25/105) 90.6 (213/235) 0.572
Abbreviations:AUC area under the curve
Table 5 Multivariable analysis of the phase analysis parameters
Variable OR LCL UCL pvalue
Derivation cohort—clinical/imaging
variables
a
alone
Clinical/imaging variables 1.326 1.132 1.552 0.0005
Derivation cohort—clinical/imaging
variables
a
+ entropy
Clinical/imaging variables 1.322 1.121 1.559 < 0.0001
Entropy (≥59%) 5.244 2.856 9.629 0.0009
Derivation cohort—clinical/cmaging
variables
a
+ phase histogram bandwidth
Clinical/imaging variables 1.370 1.162 1.615 < 0.0001
Phase histogram bandwidth (≥78°) 4.186 2.386 7.345 0.0002
Derivation cohort—clinical/imaging
variables
a
+ phase standard deviation
Clinical/imaging variables 1.331 1.133 1.563 0.0006
Phase standard deviation (≥26.7 °) 3.084 1.619 5.875 0.0005
Abbreviations:LCL lower confidence interval, OR odds ratio, UCL upper
confidence interval
a
See methodology section for discussion of chosen clinical/imaging variables
to include in model
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worsening of dyssynchrony after stress testing in patients
with CAD [22,38–42]. Specifically, these investigations
demonstrated that both thallium-201 as well as
99m
Tc str e s s
studies detected dyssynchrony in patients with myocardial
perfusion defects as measured by PHB and PSD [38,40].
Furthermore, the degree of ischemia (multivessel disease vs.
non-multivessel disease) impacted the extent of dyssyn-
chrony [39]. Finally, dyssynchrony parameters could be
utilized to aid in differentiating between ischemic and
non-ischemic cardiomyopathy [41]. The current investiga-
tion is the largest known study that uses PA to assess myo-
cardial scar utilizing SPECT MPI and is the first study to
demonstrate its impact on patient reclassification specific-
ally when compared to expert analysis combined with auto-
matedSRSandIQscores.Ourstudyfoundthatwhen
added to clinical/imaging variables, the use of entropy has a
significant impact on patient reclassification (Table 6).
There is a paucity of data on the potential benefit of PA
for interpretation of rest myocardial perfusion defects.
The findings of the current study of a greater degree of
dyssynchrony in patients with scar are consistent with the
limited data available of PA on resting perfusion imaging
[38,40,42]. To have broader clinical applicability and to
represent a better cross-section of the clinical population,
the current investigation included patients with LBBB,
paced rhythm [40,42], prior coronary revascularization
[42], atrial fibrillation, and cardiomyopathy.
The objective of the study was to assess the potential use
of PA variables to improve SPECT MPI’sspecificity.The
current study highlights the potential role of PA, specifically
entropy, for the correct interpretation of MPI studies in
patients undergoing assessment for CAD. Entropy not only
had a strong independent association with scar but also
improved the AUC when compared to traditional/clinical
imaging variables. As indicated in the statistics section, and
by protocol design, PA variable thresholds were optimized
to improve specificity and limit “false-positive”reporting of
scar and not to improve sensitivity (already high with MPI).
Other PA variables, like PHB and PSD, were also independ-
ently associated with scar in the derivation cohort, but this
significance was not maintained in the validation cohort.
One potential etiology for this finding would be the smaller
patient population assessed in the validation cohort. Poten-
tial future investigations could address this limitation with
the inclusion of a larger validation study population. Lastly,
the current study found that entropy demonstrates a high
Fig. 2 AUC difference estimate, 0.07 (0.02, 0.11), p= 0.005. Abbreviations: AUC area under the curve, ROC receiver operator curve
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rate of correctly reclassifying patients as having a lower
probability of scar.
From the clinical application perspective, we envision
the potential use of dyssynchrony thresholds derived
from this study as a means by which the interpreting nu-
clear physician can assess whether the presumed resting
perfusion defect truly exists or whether it might simply
be artifactual (Fig. 4). Specifically, if there is doubt in
regards to the potential veracity of a resting defect on
MPI, a normal PA entropy (< 59%) could serve as a
means by which to further clarify that scar is not present.
Furthermore, the impact of PA entropy in determining
myocardial scar would likely be even greater in laboratories
with a single reader who does not have access to an
automated IQ program and who is without access to all
relevant clinical data. Ultimately, a combination of the high
specificity of PA with the high sensitivity of MPI could
render the use of SPECT-MPI as an ideal diagnostic tool
for the evaluation of patients with CAD. Finally, specific
subpopulations, particularly patients classified as having
heart failure with reduced ejection fraction, where the
detection of myocardial scar is of critical prognostic and
therapeutic importance would benefit from enhanced
accuracy in the detection of scar.
Limitations
This study was conducted at a tertiary referral center,
resulting in potential referral and selection bias. How-
ever, the baseline demographics of the study population
(69% male, mean age 67years) are consistent with other
Fig. 3 AUC difference estimate, 0.08 (0.002, 0.16), p= 0.04. Abbreviations: AUC area under the curve, NRI net reclassification improvement, ROC
receiver operator curve
Table 6 Patient reclassification when applying entropy
Patient group
(N)
Higher risk
(%)
Lower risk
(%)
No change
(%)
NRI (%)
Scar (40) 6 (15) 9 (22.5) 25 (62.5) −8
No scar (98) 5 (5.1) 23 (23.5) 70 (71.4) 18
Bois et al. EJNMMI Research (2019) 9:11 Page 8 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
study populations undergoing nuclear cardiac stress test-
ing [43]. The current PA assessment was performed utiliz-
ing a specific software (QGS 3.0; Cedars-Sinai Medical
Center, Los Angeles CA) whereas other investigations
have used different software packages (such as the Sync-
Tool™and Emory Cardiac Toolbox). Despite these differ-
ences, these studies [38,40,42] have noted similar results
to the current study. That being said, the defined cut-off
points for dyssynchrony that were determined in the
current study are possibly unique to this software, and
caution should be exercised in the extrapolation of spe-
cifics of this measurement to other packages. Some soft-
ware packages also do not have all PA measurements
reported in the current study, and method of acquisition
of PA parameters may differ between vendors, limiting the
generalizability of these results. Furthermore, we used
image acquisition and processing protocols specific to our
DSPECT MPI laboratory that may be different from other
centers. However, as previously mentioned, prior studies
have demonstrated that PA is repeatable [18] and repro-
ducible [17], with results that are independent of the im-
aging system [20], reconstruction algorithm [21], or tracer
activity utilized [22].
In the current study, resting perfusion defect was la-
beled scar but some of these patients may have actually
had viable myocardium. Nitrate administration may have
helped further discriminate between these two popula-
tions. However, both populations reflect patients with
abnormal myocardium and potential CAD, and thereby
discerning between these patients and those with com-
pletely normal myocardial perfusion still has diagnostic,
prognostic, and therapeutic implications.
In this study, we did not include an independent
measurement of scar, but rather depended on clinical
assessment, as done in routine practice. One potential con-
sideration could be the utilization of gadolinium delayed-
enhancement cardiac magnetic resonance imaging (cMRI)
to assess scar in patients also undergoing PA SPECT MPI.
This would be of importance in determining the potential
role of PA, specifically entropy, when discordance arises be-
tween interpreters who are convinced that scar is present
and the PA data which suggests otherwise.
Finally, comparative assessment with segmental wall
motion and thickening was not performed and would
potentially provide further insights when compared to
phase analysis parameters.
Fig. 4 Comparative cases demonstrating use of phase analysis in instances of questionable resting defect (scar). A 74-year-old male with a history
of hypertension, hyperglycemia and obstructive sleep apnea presented with 4 months of exertional dyspnea and palpitations was referred for
radionuclide stress testing. Short-axis rest and stress images displaced from apex to base (a) demonstrated a possible resting perfusion defect in
the lateral portion of the apex (red arrow). bA 76-year-old obese, hypertensive male presented with complaints of 3 months of exertional
dyspnea underwent radionuclide stress testing which revealed a possible resting defect in the basal inferior segment (blue arrow). cResting polar
map and histogram of patient from case (a) demonstrating significant dyssynchrony, with an entropy 72%. Compare these findings with the
polar map and histogram (d) of the second patient (b) which demonstrates synchrony with entropy 45%
Bois et al. EJNMMI Research (2019) 9:11 Page 9 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Conclusion
The use of PA entropy significantly improved the specifi-
city of SPECT MPI using a solid-state system in a center
with dual physician SPECT MPI interpreters, a quantita-
tive QI program, and ready access to relevant clinical
data. It would likely have even greater impact in a center
with less readily available resources and thereby serve as
a useful adjunctive tool to the nuclear cardiologist.
Abbreviations
99m
Tc: Technetium-99m; AUC: Area under the curve; CAD: Coronary artery
disease; cMRI: Cardiac magnetic resonance imaging; LBBB: Left bundle
branch block; LV: Left ventricular; MELM: Maximum-likelihood expectation-
maximization; MPI: Myocardial perfusion imaging; NRI: Net reclassification
index; OMC: Onset of myocardial contraction; PA: Phase analysis; PHB: Phase
histogram bandwidth; PSD: Phase standard deviation; QGS: Quantitative
gated SPECT; QPS: Quantitative perfusion SPECT; ROC: Receiver-operator
characteristic; SPECT: Single-photon emission computed tomography;
SQ: Scar quantification; SRS: Summed rest score
Acknowledgements
The authors would like to acknowledge Dr. Michael O’Connor for his
assistance with the methodology portion of this article as it relates to image
acquisition and processing protocols and Mr. Robert Glynn CNMT and
Thomas Owens CNMT for their assistance with image acquisition.
Funding
This study was supported by CTSA Grant Number UL1 TR000135 from the
National Center for Advancing Translational Science (NCATS) and the Mayo
Foundation.
Availability of data and materials
The datasets used and/or analyzed during the current study are available
from the corresponding author on reasonable request.
Authors’contributions
JPB contributed to the conception, design, drafting, and final approval of
manuscript. PC, RG, and MRP contributed to the conception, design, and
final approval. CS contributed to the biostatistical analysis. All authors read
and approved the final manuscript.
Ethics approval and consent to participate
The study was approved by the Mayo Clinic Institutional Review Board (IRB).
Consent for publication
Not applicable
Competing interests
Raymond J. Gibbons is a consultant at Astellas Pharm and Peer View
Institute. The other authors declare that they have no competing interests.
Publisher’sNote
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Author details
1
Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
2
Department of Biostatistics, Mayo Clinic, Rochester, MN, USA.
3
Department
of Cardiovascular Diseases, Mayo Clinic College of Medicine, 200 First Street
SW, Rochester, MN 55905, USA.
Received: 2 November 2018 Accepted: 16 January 2019
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