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Research
Cite this article: Teo S-K, Vos FJA, Tan R-S,
Zhong L, Su Y. 2015 Regional ejection fraction
and regional area strain for left ventricular
function assessment in male patients after
first-time myocardial infarction. J. R. Soc.
Interface 12: 20150006.
http://dx.doi.org/10.1098/rsif.2015.0006
Received: 2 January 2015
Accepted: 29 January 2015
Subject Areas:
biomedical engineering
Keywords:
regional ejection fraction, regional area strain,
left ventricle, myocardial infarction, cardiac
magnetic resonance imaging
Author for correspondence:
Soo-Kng Teo
e-mail: [email protected]
Electronic supplementary material is available
at http://dx.doi.org/10.1098/rsif.2015.0006 or
via http://rsif.royalsocietypublishing.org.
Regional ejection fraction and regional
area strain for left ventricular function
assessment in male patients after
first-time myocardial infarction
Soo-Kng Teo1, F. J. A. Vos1,4, Ru-San Tan2,3, Liang Zhong2,3 and Yi Su1
1
Department of Computing Science, Institute of High Performance Computing, Agency for Science, Technology
and Research (A*STAR), Singapore, Republic of Singapore
2
National Heart Centre Singapore, Republic of Singapore
3
Duke-NUS Graduate Medical School Singapore, Republic of Singapore
4
Technical Medicine, University of Twente, Maarn, The Netherlands
In this work, we present a method to assess left ventricle (LV) regional function from cardiac magnetic resonance (CMR) imaging based on the regional
ejection fraction (REF) and regional area strain (RAS). CMR scans were performed for 30 patients after first-time myocardial infarction (MI) and nine
age- and sex-matched healthy volunteers. The CMR images were processed
to reconstruct three-dimensional LV geometry, and the REF and RAS in a
16-segment model were computed using our proposed methodology. The
method of computing the REF was tested and shown to be robust against
variation in user input. Furthermore, analysis of data was feasible in all
patients and healthy volunteers without any exclusions. The REF correlated
well with the RAS in a nonlinear manner (quadratic fit—R 2 ¼ 0.88). In
patients after first-time MI, the REF and RAS were significantly reduced
across all 16 segments (REF: p , 0.05; RAS: p , 0.01). Moreover, the REF
and RAS significantly decreased with the extent of transmural scar obtained
from late gadolinium-enhanced CMR images. In addition, we show that the
REF and RAS can be used to identify regions with compromised function in
the patients with preserved global ejection fraction with reasonable accuracy
(more than 78%). These preliminary results confirmed the validity of our
approach for accurate analysis of LV regional function. Our approach potentially offers physicians new insights into the local characteristics of the
myocardial mechanics after a MI.
1. Introduction
Myocardial infarction (MI), or more commonly known as ‘heart attack’, is a
major cause of death and disability worldwide. It usually results in injuries
to the myocardial tissues and alters the mechanical properties of the heart ventricles—a process known as remodelling. Each year, an estimated 635 000
Americans have a first hospitalized MI [1]. Accurate and reproducible determination of the left ventricular (LV) function in the heart is essential for the
diagnosis, disease stratification and estimation of prognosis for the majority
of cardiac diseases. LV remodelling after a MI is currently assessed clinically
using changes in global LV volume and ejection fraction (EF). This method
aggregates the regional contribution of both the infarcted (injured) and noninfarcted myocardial tissues and does not provide any specific information
on the functionality or non-functionality of the infarcted regions. In addition,
it has been shown that patients after MI can potentially exhibit LV EF in the
‘normal’ range, illustrating these indexes are insufficient for diagnosis, disease
stratification and estimation of prognosis [2,3]. For accurate regional assessment
of the myocardial tissue properties and functions, the current clinical ‘gold standard’ is cardiac magnetic resonance (CMR) imaging using delayed contrast
enhancement [4–6]. This method has the advantage that it allows physicians
& 2015 The Author(s) Published by the Royal Society. All rights reserved.
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J. R. Soc. Interface 12: 20150006
an alternative geometrical approach for assessing regional
LV functions that incorporates information from the longaxis plane of the LV. We proposed to compute the REF and
regional area strain (RAS) based on the reconstructed threedimensional LV geometry from CMR images and used
these indices to assess the regional LV functions. This assessment will be validated against the LGE scarring score which
is regarded as the clinical benchmark. The reconstruction
of our three-dimensional LV geometry relies on standard
CMR imaging that is routine in existing clinic and hospital
workflow for cardiac investigation and thus our approach
can be seamlessly integrated in this workflow without
causing any disruptions. In addition, our approach does
not require the use of contrast agent and our indices are computed non-invasively in a computationally efficient way;
analysis time for a typical case is less than 5 min.
The RAS is a quantitative strain measurement that combines the endocardial wall strains in the circumferential,
longitudinal and radial directions. We had first proposed the
use of this index for assessing the regional functions in the
right ventricle for patients with repaired tetralogy of Fallot
[17]. This index is different from the myocardium wall strain
measurements from three-dimensional echocardiography, CT
imaging and CMR imaging as these wall strains are directional. Furthermore, the RAS can be computed in a frameless
manner and is not dependent on the AHA nomenclature. In
this paper, we computed the RAS using the AHA nomenclature in order to validate against the REF and LGE scarring
score. We postulate that the RAS can provide additional information to physicians about the regional mechanical properties
of the LV myocardial tissues. The RAS generally reflects the
extent of deformation in the LV endocardial surface (the
inner surface of the ventricle) during contraction and relaxation. During normal contraction, the endocardial surface
deforms due to the shortening of the myofibres embedded
inside the tissue. We hypothesize that the RAS should decrease
at infarcted regions after MI as a result of myocardial cell death
due to prolonged ischaemia (restriction to blood supply)
[18,19]. Similarly, the REF should also decrease at infarcted
regions after MI as the contribution of that region to the pumping efficacy of the LV is decreased due to reduced deformation
of the endocardial surface. Taken together, the REF and RAS
can be used for improving the diagnosis, disease stratification
and estimation of prognosis after MI.
The novelty of our approach is twofold: (i) the use of the
RAS and REF to assess regional LV function after MI with
validation against LGE scarring score and (ii) applying the
RAS and REF to discriminate MI patients with preserved
global EF (more than or equal to 50%) specifically in a subgroup
of patients. We demonstrate that the RAS and REF can be used
to identify regions in the LV associated with compromised function (as indicated by the LGE scarring score) for this particular
subgroup of patients. This identification is important as this
subgroup of patients exhibit ‘normal’ global EF and could be
diagnosed as healthy based on the use of global indices.
The primary objectives of this paper are therefore to:
(i) validate the reproducibility of the REF using our geometrical approach, (ii) assess the correlation between REF and RAS
for both the patient and control (healthy) groups, (iii) compare the differences in REF and RAS between patients and
control groups, and (iv) assess the feasibility of using the
REF and RAS to identify regions with compromised function
in the patient subgroup with preserved global EF.
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to visually inspect the extent of the infarction to the myocardium tissue through the use of a contrast agent. The infarcted
regions typically show higher intensity as compared with the
normal myocardial tissue in the late gadolinium-enhanced
(LGE) CMR images and the extent of the infarction can be
semi-quantified through the use of a scar score. However,
the disadvantage of this method is that the LGE scarring
score is only semi-quantitative and subject to both
intra- and inter-observer variability.
To overcome the above limitation of the LV EF and LGE
CMR imaging, different regional indices were proposed in
the last decade to enable clinicians to estimate the performance
of the LV at specific regions of interest. These regional indices
can quantitatively measure the localized functions of the LV
and complement existing clinical standard such as LGE
CMR imaging. The two most widely accepted regional indices
for such localized functional assessment are (i) regional ejection fraction (REF) and (ii) regional myocardium wall strains.
The REF is usually computed using either CMR images or
multi-detector computed tomography (CT) images [7–10].
The partitioning of the LV from the images is based on the
17-segment model that was introduced by Cerqueira et al. in
2002 [11]. This nomenclature published by the American
Heart Association (AHA) was designed due to a lack of standardization in medical imaging and has been widely adopted
clinically. However, the methodology of calculating the REF
has not been standardized and different groups adopt slightly
different methods in computing this index. For example, Zeb
et al. [9] used a floating point method for partitioning the LV
in order to compute the REF, whereas Masci et al. [10] used
a fixed point method. The differences in both methods pertain
to the definition of the basal–apical axis from the images. For
the floating point method, the basal–apical axis is defined
independently from the end-diastolic (ED) and end-systolic
(ES) frames, respectively, whereas the fixed point method
defined this axis from only the ED frame.
Similarly, the regional myocardium wall strain can be
computed using echocardiography, CMR images or CT
images. For example, Pourmorteza et al. used CT imaging
to extract a measure that reflects local myocardial contraction.
However, the potential accuracy of the method in computing
the myocardium wall strains has yet to be confirmed by a first
patient study [12]. Three-dimensional speckle tracking
echocardiography is also widely adopted for measuring the
circumferential, radial and longitudinal strains in the myocardium [13,14]. Kleijn et al. [15] determined the regional
function of the myocardium based on automatic wall
motion analysis using three-dimensional speckle tracking
echocardiography. Generally, echocardiography showed a
high accuracy for assessment of regional LV function but
only when performed by an expert reader that makes data
reproducibility very difficult. Bogaert et al. were among the
first groups to propose using a combined positron emission
tomography –magnetic resonance imaging approach to compute both the REF and myocardium wall strains and compare
it against myocardial blood flow and glucose metabolism for
MI patients [7,16]. However, their results were not validated
against LGE scarring score as their method for partitioning
the LV is non-standard.
In summary, most of the methodologies described above
for computing these regional indices are defined based on
either CMR images or multi-detector CT images acquired in
the short-axis plane of the LV. In this study, we proposed
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Table 1. Population characteristics, for healthy subjects (n ¼ 9) and patients (n ¼ 30). n.s., not significant.
patients
healthy
p
53 + 10
n.s.
gender (M/F)
weight (kg)
30/0
75 + 17
9/0
72 + 11
n.s.
n.s.
body surface area (m2)
systolic blood pressure (mmHg)
1.85 + 0.22
127 + 20
1.90 + 0.19
131 + 15
n.s.
n.s.
diastolic blood pressure (mmHg)
LV ED volume index (ml)
76 + 9
184 + 46
77 + 6
133 + 36
n.s.
,0.0001
LV ES volume (ml)
103 + 46
45 + 20
,0.0001
46 + 11
67 + 6
,0.0001
LV EF (%)
2. Material and methods
2.1. Population
This study consisted of 30 MI patients and nine age- and sexmatched healthy volunteers (table 1). All 30 patients had a first
MI, with a successfully reperfused ST-elevation, weeks after the
acute event. For the healthy subjects, none of them had: (i) significant valvular or congenital cardiac disease, (ii) history of
MI, (iii) coronary artery lesions, or (iv) abnormal LV pressure,
ED volume or EF.
All subjects underwent CMR scanning using steady-state free
precession cine gradient echo sequences imaged on a 1.5 T Siemens
scanner (Avanto, Siemens Medical Solutions, Erlangen). Scans for
the patients were acquired one to three months after MI and three
sets of CMR images were taken. The first set of images (shortaxis) was taken along the plane that passes through the mitral
and aortic valves of the heart. The second set of images (longaxis) was taken on planes orthogonal to the short-axis images,
and oblique to each other, giving an angular cross-sectional view
of the LV. The last set of images was taken on the vertical cross
section (long-axis) orthogonal to and connecting with the shortaxis plane images. The CMR images have a spatial resolution of
1.5 mm in-plane and 8 mm out-of-plane, acquired in a single
breath hold, with 22 temporal phases per heart cycle. Of these
images, those corresponding to the cardiac cycle at end-diastole
(ED) and end-systole (ES) are manually segmented and used for
analysis. The ED and ES frames are defined using the valve-closure
and valve-opening images, respectively; the frame of the valveclosure (valve-opening) image is designated as the ED (ES) frame.
To assess degree of MI, LGE CMR scans were also acquired for
the MI patients. Scar scores were defined as the regional increase
in CMR signal intensities 20 min after injection of 0.2 mmol kg21
gadolinium-diethylenetriamine pentaacetic acid. All subjects were
recruited without consideration of gender or ethnicity and gave
informed consent. The study protocol was approved by the
SingHealth Centralised Institutional Review Board.
2.2. Three-dimensional geometrical reconstruction and
mesh partitioning
To perform segmentation of the endocardium, images from
the CMR acquisition were processed using the LVtools plugin
included in the CMRTOOLS software package (Cardiovascular
Solution, UK). Segmentation of the LV endocardial contours is
manually performed on both the short- and long-axis images.
Papillary and trabeculae were excluded from the myocardium
during segmentation. The contours on these two sets of
images are then fused to reduce registration errors during the
reconstruction process. Control points are fixed on the surface
of the reconstructed endocardium and these points are defined
by the intersection of the short- and long-axis views. To create
a more realistic reconstruction, we also use the long-axis views
oriented at regular angular intervals about the LV axis. A
series of B-spline curves are then generated to represent the
contours of the endocardial surface.
The endocardial contours are subsequently exported from
CMRTOOLS and an in-house meshing toolkit is used to reconstruct
the three-dimensional geometry of the LV endocardial surface in
terms of a two-manifold structured triangle mesh (denoted as V).
Using discrete geometrical representation of the LV endocardial surface affords ease in the extraction of various clinically meaningful
indices [20–23]. To facilitate analyses, the endocardial mesh is partitioned into 16 segments based on the standard published by the
AHA [11]. As the standardized nomenclature was established for
image-based data, we proposed a modified approach to generate
the 16-segment model for three-dimensional geometrical models
[20]. A typical three-dimensional LV geometry reconstructed from
CMR images is as shown in figure 1. Abidance to this recommended
nomenclature allows us to achieve adequate sampling of the LV
without exceeding the relevant limits for clinical and research applications. It is worth noting that Segment 17 in the standardized
nomenclature has been omitted in our approach because it is difficult to acquire the true apex position due to the inter-slice spacing
of our CMR images (8 mm). Details of the mesh partitioning
approach can be found in our previous work [20].
2.3. Calculation of regional area strain
The RAS is a dimensionless quantity that measures the magnitude of change in the surface area of the myocardial tissue as it
contracts from ED to ES. This index can be regarded as a parameter that integrates the effects of deformation of the
myocardium tissues in the longitudinal, circumferential and
radial directions. In our previous work, we had employed the
RAS in the study of patients with repaired tetralogy of Fallot
[17]. To calculate the RAS of each segment, we use
SAi,ES
RASi ¼ ln
,
(2:1)
SAi,ED
where SAi,ED is the endocardial surface area of Segment i at ED
and SAi,ES is the endocardial surface area of Segment i at ES.
2.4. Calculation of regional ejection fraction
The representation of the LV as surface meshes presents us with
the convenience of calculating its volume/sub-volumes by
making use of only its vertex information. First, the divergence
theorem is used to reduce the volume integral to a sum of surface
integrals over the individual faces of the LV mesh. Next, these
surface integrals are then further reduced to a sum of line
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53 + 10
age (years)
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parameter
3
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(a)
(b)
4
Segment 4
Segment 5
1
Segment 2
Segment 6
Segment 1
7
2
8
Segment 9
Segment 10
Segment 11
14
3
Segment 8
Segment 12
6
16
15
11
5
10
4
Segment 15
Segment 14
Segment 16
Segment 13
two-dimensional segmentation
nomenclature
as viewed from the apex
Figure 1. (a) Regional partitioning of the LV superimposed onto the CMR images for one sample patient. Basal region: segments 1 –6; mid-cavity region: segments
7 – 12; apical region: segments 13 – 16. (b) Partitioning of the reconstructed three-dimensional LV geometry with the corresponding two-dimensional bulls-eye plot.
(Online version in colour.)
integrals using Green’s theorem [24]. Finally, the volume Vi of
each LV region is given by
X
Vi ¼
(y1 y0 )(z2 z0 ) (y2 y0 )(z1 z0 )(x0 þ x1 þ x2 ), (2:2)
w[Vi
where (x0, y0, z0), (x1, y1, z1) and (x2, y2, z2) are the coordinates of
the vertices of a face f of the LV sub-mesh Vi representing the
Segment i (1 i 16). The convention of the face vertex ordering
is taken in the counter-clockwise direction with the face normal
pointing away from the LV chamber. The REFi of segment i is
then defined as
REFi ¼
Vi,ED Vi,ES
,
Vi,ED
(2:3)
where Vi,ED is the volume of Segment i at ED and Vi,ES is the
volume of Segment i at ES. This definition of the REF in our geometrical approach can be seen as an extension of the LV EF as
defined by the AHA [25].
2.5. Myocardium scar scoring
Myocardium tissue scarring for patients was graded on a fourpoint scale by an experienced cardiologist for each segment
based on the CMR intensity of the infarcted tissues and the
size of the infarction. This scoring is performed using the LGE
CMR images. Scoring criteria: 0 ¼ no infarction, 1 ¼ 1 – 25%
infarction of myocardium, 2 ¼ 26– 50% infarction of myocardium, 3 ¼ 51 –75% infarction of myocardium and 4 ¼ 76 – 100%
infarction of myocardium, according to the AHA [26,27].
2.6. Validation
Our approach is fully automated for a given set of segmented
endocardial contours and there is no variability in the computation. The only variability in our geometrical-based approach
for computing the REF is the user-specified location of the anatomical landmark (pref ) on the basal contour as shown in figure 1. This
landmark corresponds to the anterior attachment junction of the
right ventricle to the LV and is used for orientating the endocardial
surface mesh. Therefore, to validate the robustness and reproducibility of our approach, we studied the effect due to noise in
the pref location to mimic intra- and inter-observer variabilities.
Specifically, we randomly varied the location of pref by 1.5 mm
to 4.5 mm (the equivalent of 1 –3 image pixels) in three independent runs and recomputed the REF for each subject. For each
run, the randomization of pref for each subject was performed
three times, i.e. there are three sets of recomputed REF at each of
the pref variations at 1.5, 3.0 and 4.5 mm. These results were compared to the original REF using the Bland–Altman analysis to
test the robustness and reproducibility of our approach.
We note that it is also possible to orientate the LV endocardial surface using either (i) the posterior attachment junction or
(ii) both posterior and anterior attachment junctions. Both choices
are valid and the anatomical landmark(s) is used solely for reference to orientate our mesh to the physical LV anatomy. In this
paper, we had chosen to use the anterior attachment junction
and have focused on the effect of its variability on our proposed
geometric-based approach to compute REF. Our rationale for
using one reference point as opposed to two is to minimize
variability arising from additional user input.
2.7. Statistical analysis
All quantitative data are expressed as the mean + s.d. Comparison
between pairs of quantitative variables was performed by using an
independent sample parametric test (Student’s t-test). The association between the RAS and the REF was studied using the
coefficient of determination (R 2). The Bland–Altman analysis
was used for the validation of our geometry-based methodology
for computing the REF with a 95% CI. Comparison of means
across two or more samples is performed using one-way
ANOVA with the Tukey test to determine significant differences
between pairs of means. All statistical tests are performed using
the statistical programming language R 3.1.1 [28].
3. Results
3.1. The geometrical-based approach of computing
regional ejection fraction is robust and
reproducible
To test the robustness of our approach, the REF was recomputed by varying the location of the anatomical landmark (see
subsection Validation) in the control group. The results of our
robustness analysis are plotted in the form of linear regression
and Bland–Altman plots, as shown in figure 2, using one representative set of the recomputed REF at each run. The mean
difference in the REF between our original data and the recomputed data (y-axis values in the Bland–Altman plots) is
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9
12
13
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Segment 3
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(a)
(i)
(ii)
(iii)
5
95
R2 = 0.99
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R2 = 0.99
R2 = 0.99
recomputed REF (%)
85
75
65
55
55
65
75
85
95 45
55
reference REF (%)
(b)
3
65
75
85
95 45
55
reference REF (%)
(i)
65
75
85
95
reference REF (%)
(ii)
(iii)
difference in REF (%)
2
1
0
–1
–2
–3
50
60
70
80
90
50
60
mean REF (%)
70
80
mean REF (%)
90
50
60
70
80
90
mean REF (%)
Figure 2. Validation of our approach for computing REF: linear regression (a) and Bland – Altman (b) plots for illustrating the agreement between original REF and
the recomputed REF by varying the location of the anatomical landmark by 1.5 mm (a(i),b(i)), 3.0 mm (a(ii),b(ii)) and 4.5 mm (a(iii),b(iii)).
approximately zero in all three runs when the landmark location
was varied by 1.5 mm, 3.0 mm and 4.5 mm, respectively. This
suggests that there is no systematic error in our REF computation approach associated with the landmark location.
Similarly, the 95% confidence limit (representing the mean
difference +1.96 s.d., denoted by the dotted-line in
the Bland–Altman plots) contains 94%, 96% and 95% of the
REF variations, respectively, in the three runs. This suggests
that the REF computation approach is not sensitive to the
exact location of the anatomical landmark. Furthermore, the correlations between the original REF and the recomputed REF
were excellent (R 2 ¼ 0.99) in all three runs, thereby validating
our approach for computing the REF to be both reproducible
and robust against variation in user input.
In addition, we have also recomputed the REF and RAS by
varying the input contours for a subset of subjects in our study
(see the electronic supplementary material). Specifically, there
was also no statistical difference between the original REF/
RAS results and the recomputed REF/RAS results (REF: p ¼
0.92 . 0.05; RAS: p ¼ 0.61 . 0.05 using a paired t-test),
suggesting that our approach is indeed robust against variation
in the user-segmented input contours.
3.2. There is excellent correlation between regional
ejection fraction and regional area strain for both
control and patient group
For the control group, the REF and RAS were calculated for all
segments (9 controls 16 segments ¼ 144 segments) using our
proposed approach. The mean global EF and area strain were
69.7% and 73.5%, respectively (table 2). Both the REF and
RAS demonstrated an increase from basal level towards the
apical part of the ventricle. Functional non-uniformity was
also noted between the inferior and anterior regions of
the LV. The contribution to the REF of the inferior region (segments 4, 10 and 15) was significantly higher compared with the
anterior region (segments 1, 7 and 13) throughout the entire
ventricle ( p , 0.01). No such differences were found between
the lateral and septal side.
For the patient group, the REF and RAS were also calculated
for all segments (30 patients 16 segments ¼ 480 segments).
No exclusion of segments was necessary (table 3). To assess
the correlation between the REF and RAS, a scatter plot was
generated combining data from both the control and patient
group (figure 3). It can be seen from the plot that the RAS has
an excellent correlation with the REF. Using a quadratic fit,
the R 2 is 0.88 for the combined data (n ¼ 624). We observe
that the slope of the quadratic fit at higher REF (more than
40%) is larger compared with that at lower REF. This observation suggests that the RAS could potentially be more
sensitive at functional assessment in segments with higher
REF. In summary, the RAS reflects the deformation in the endocardial surface and can be used to measure the regional LV
contraction performance, whereas the REF reflects the regional
contribution to the overall pumping efficacy of the LV.
3.3. There is a significant drop in the regional ejection
fraction and regional area strain after myocardial
infarction
A comparison was made between the control and patient
group using both the REF and RAS. An overview is shown
in figure 4. We observed a significant decrease for both the
REF and RAS across all 16 segments in the patient group
after MI (REF: p , 0.05; RAS: p , 0.01). A decrease of the
REF to below 44% for patients is visible across the basal,
mid-cavity and apical regions (table 4). Similarly, the RAS in
these regions also decreased to less than 38%. Due to necrosis
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Table 2. Table of normal reference values for REF and RAS for healthy
subjects (n ¼ 9).
REF (%)
RAS (%)
(1) basal anterior
(2) basal anterior septal
56.5 + 8.7
55.7 + 6.1
56.4 + 10.5
51.2 + 8.1
LGE0
(3) basal inferior septal
(4) basal inferior
63.2 + 6.4
67.0 + 7.0
54.4 + 8.5
58.2 + 9.9
remote
(5) basal inferior lateral
64.0 + 6.4
57.3 + 11.6
basal
54
84
(6) basal anterior lateral
(7) mid anterior
59.9 + 8.4
69.2 + 7.0
59.1 + 14.5
75.1 + 15.1
mid-cavity
apex
21
16
(8) mid anterior septal
(9) mid inferior septal
70.1 + 8.5
74.6 + 9.0
72.4 + 15.0
75.5 + 15.4
total
91
(10) mid inferior
(11) mid inferior lateral
78.4 + 7.0
77.4 + 6.6
84.1 + 19.0
86.8 + 22.0
(12) mid anterior lateral
72.4 + 8.9
80.4 + 21.3
(13) apical anterior
(14) apical septal
73.2 + 8.5
74.1 + 10.3
84.4 + 12.8
85.7 + 12.0
(15) apical inferior
(16) apical lateral
80.4 + 5.9
79.5 + 7.8
97.7 + 14.2
97.2 + 15.9
aggregrating over the basal, mid-cavity and apical regions
regions
REF
RAS
(i) basal
61.0 + 8.0
56.1 + 10.6
(ii) mid-cavity
(iii) apical
73.7 + 8.3
76.8 + 8.5
79.0 + 18.1
91.3 + 14.6
EF
AS
69.7 + 10.7
73.5 + 20.5
aggregating over the entire LV
global
in the infarcted regions, cells will not be able to deform and
play a part in the contraction of the LV. As a result, less
blood will be properly transported through the heart, resulting
in lower REF and correspondingly lower global EF.
3.4. There is a significant drop in the regional ejection
fraction and regional area strain for infarcted
segments as compared with non-infarct segments
The REF and RAS were correlated to the LGE CMR scarring
scores in the patient group as LGE CMR is the current ‘gold
standard’ for LV function assessment. For this analysis, segments are grouped together based on their LGE CMR
scarring score into four categories: (i) High LGE scarring score
of 3 or 4 (denoted as LGE3-4), (ii) Low LGE scarring score of
1 or 2 (denoted as LGE1-2), (iii) LGE scarring score of 0 but adjacent to another infarcted segment (denoted as Border), and (iv)
LGE scarring score of 0 with all adjacent segments also having
LGE scarring score of 0 (denoted as Remote). The rationale for
this grouping is twofold: (i) to study the effect of scarring extent
on regional functions and (ii) to study if regional functions are
compromised in segments that are adjacent to infarct segments.
The results of our analysis are shown in figure 5.
border
LGE1-2
LGE3-4
total
28
14
180
91
23
37
46
31
35
180
120
198
111
80
480
Figure 5a(i),b(i) shows the correlation for all 16 segments
(global analysis). Both REF and RAS for segments in the
Remote group are significantly different from those segments
in the Border group ( p , 0.05), LGE1-2 group ( p , 0.01) and
LGE3-4 group ( p , 0.01). Similarly, both REF and RAS for segments in the Border group are significantly different from those
segments in the LGE1-2 group ( p , 0.01) and LGE3-4 group
( p , 0.01). The subsequent panels in figure 5 show the correlation for segments aggregated over the basal, mid-cavity and
apical regions, respectively. A similar trend can also be seen:
Basal region. Both REF and RAS for segments in the Remote
group are significantly different from those segments in
the Border group ( p , 0.01), LGE1-2 group ( p , 0.01)
and LGE3-4 group ( p , 0.01).
Mid-cavity region. Both REF and RAS for segments in the Remote
group are significantly different from those segments in the
LGE1-2 group ( p , 0.01) and LGE3-4 group ( p , 0.01).
Apical region. Both REF and RAS for segments in the Remote
group are significantly different from those segments in
the LGE1-2 group (REF: p , 0.01; RAS: p , 0.05) and
LGE3-4 group ( p , 0.01).
Summarizing our observation, the REF and RAS in the
patient group were significantly different between segments
in the Remote group and infarct segments (LGE1-2 and
LGE3-4 group), suggesting that both indices can potentially
be used for the identification of the infarct areas. This difference was observed across the basal, mid-cavity and apical
regions, showing that our method of discrimination is
robust and independent of the location of the infarct regions.
Furthermore, we also observe that both REF and RAS for segments in the Remote group are higher as compared with the
Border group. We hypothesize that segments in the Border
group exhibit some degree of compromised function arising
from their proximity to the adjacent infarcted segments.
3.5. It is feasible to identify segments with
compromised function for patients with ‘normal’
global ejection fraction using the regional ejection
fraction and regional area strain
From the patient group, we identified a subgroup of patients
with preserved global EF (more than or equal to 50%) and
show that the REF and RAS can be used to identify segments
J. R. Soc. Interface 12: 20150006
segments
6
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Table 3. Patient database overview (n ¼ 30) of all segments for different
ventricular levels, based on the LGE scar score. Segments are grouped
together based on their LGE CMR scarring score into four categories:
(i) LGE3-4, (ii) LGE1-2, (iii) border (LGE0), and (iv) remote (LGE0). Refer to
main text for details of grouping.
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7
140
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120
control group
patient group
quadratic fit
R2 = 0.88
80
J. R. Soc. Interface 12: 20150006
RAS (%)
100
60
40
20
0
20
40
60
80
100
REF (%)
0
20
40
60
80
Figure 3. Correlation between the REF and RAS for the combined data from normal control and patient groups. The RAS has an excellent correlation with the REF.
Fitting to the data is performed using a quadratic functional. (Online version in colour.)
(a)
(b)
100
90
120
control group
patient group
110
100
80
90
80
60
RAS (%)
REF (%)
70
50
40
70
60
50
40
30
30
20
20
10
0
control group
patient group
10
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
segments
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
segments
Figure 4. Comparison of REF (a) and RAS (b) between control and patient groups. There is a significant decrease in both the REF and RAS in the patient group
across all 16 segments as compared with the control group (REF: p , 0.05; RAS: p , 0.01).
Table 4. Comparison of the REF and RAS for control and patient groups at the base, mid-cavity and apex level.
REF (%)
RAS (%)
level
control
patient
p
control
patient
p
basal
61.0 + 8.0
43.2 + 15.1
,0.01
56.1 + 10.6
34.7 + 17.3
,0.01
mid-cavity
apex
73.7 + 8.3
76.8 + 8.5
43.9 + 19.8
39.6 + 25.6
,0.01
,0.01
79.0 + 18.1
91.3 + 14.6
37.4 + 21.5
36.9 + 23.7
,0.01
,0.01
with compromised function in this subgroup. Here, we
define segments with compromised function as segments in
the Border, LGE1-2 or LGE3-4 groups (see above section).
The identification was performed by considering the basal,
mid-cavity and apical regions independently for each patient
with the threshold values for REF and RAS obtained using
(mean 2 1.0 s.d.) from the normal control group. The
results of our analysis are shown in table 5.
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(a)
(b)
global (i)
130
110
**
100
110
90
80
RAS (%)
REF (%)
70
60
50
70
60
50
40
40
30
30
20
10
10
0
remote
border
LGE1–2
0
LGE3–4
LGE3–4
LGE1–2
LGE3–4
120
**
110
90
100
*
80
**
90
70
80
RAS (%)
REF (%)
LGE1–2
130
100
60
50
70
60
50
40
40
30
30
20
20
10
10
remote
border
LGE1–2
0
LGE3–4
mid
(iii)
remote
border
(iii)
130
110
**
100
**
120
**
90
**
110
100
80
90
70
80
RAS (%)
REF (%)
border
basal (ii)
(ii)
110
60
50
70
60
50
40
40
30
30
20
20
10
10
remote
border
LGE1–2
0
LGE3–4
remote
border
130
**
100
LGE1–2
LGE3–4
apical (iv)
(iv)
110
**
120
*
110
**
90
**
*
100
80
90
70
80
RAS (%)
REF (%)
remote
60
50
70
60
50
40
40
30
30
20
20
10
10
0
remote
border
LGE1–2
LGE3–4
remote
border
LGE1–2
LGE3–4
Figure 5. Box-whisker plot correlating the LGE scar score with REF (a) and RAS (b) for the entire LV (a(i),b(i)), basal region (a(ii),b(ii)), mid-cavity region (a(iii),b(iii)) and
apical region (a(iv),b(iv)). On each box, the central mark (in red) is the median, the edges of the box are the 25th and 75th percentiles and the whiskers extend to
approximately +2.7 s.d. coverage if the data are normally distributed. Segments are grouped together based on their LGE CMR scarring score into four categories:
LGE3-4, LGE1-2, border (LGE0) and remote (LGE0). Refer to main text for details of grouping (*p , 0.05, **p , 0.01). (Online version in colour.)
For this subgroup of patients, our approach has an accuracy of approximately 78% (REF—78.4%; RAS—78.2%)
for identifying segments with compromised function. Generally, the accuracy of our approach decreases with increasing
global EF in this patient subgroup. For patients with global
EF , 60%, our approach has an average accuracy of 84.2%
(REF) and 85.1% (RAS). Conversely, for patient 14 with
global EF of 67.6% (grey highlight in table 5), our approach
J. R. Soc. Interface 12: 20150006
20
0
**
*
100
80
0
**
120
**
*
90
0
rsif.royalsocietypublishing.org
(i)
8
50.0
50.0
50.0
51.7
52.3
54.5
55.4
56.1
56.9
57.2
58.1
62.3
63.9
67.6
1
2
3
4
5
6
7
8
9
10
11
12
13
14
gross average
global EF
12
10
10
10
10
14
14
13
11
14
11
11
15
13
no. segments
73.6
0.0
70.0
60.0
100.0
70.0
78.6
50.0
76.9
90.9
85.7
100.0
90.9
73.3
84.6
Se
82.7
0.0
87.5
100.0
90.9
77.8
100.0
100.0
100.0
90.9
92.3
68.8
71.4
100.0
78.6
1P
70.2
100.0
83.3
100.0
83.3
66.7
100.0
100.0
100.0
80.0
50.0
0.0
20.0
100.0
0.0
Sp
46.2
25.0
62.5
60.0
100.0
57.1
40.0
22.2
50.0
80.0
33.3
n.a.
50.0
20.0
0.0
2P
78.2
0.0
78.8
80.0
95.5
73.9
89.3
75.0
88.5
90.9
89.0
84.4
81.2
86.7
81.6
Acc
71.9
8.3
20.0
70.0
90.0
70.0
85.7
57.1
69.2
81.8
78.6
100.0
81.8
93.3
100.0
Se
85.0
100.0
66.7
77.8
81.8
77.8
100.0
100.0
100.0
75.0
91.7
73.3
64.3
100.0
81.3
1P
J. R. Soc. Interface 12: 20150006
ID
RAS
n.a.
50.0
100.0
0.0
50.0
25.0
42.9
50.0
25.0
80.0
57.1
38.5
57.1
26.7
46.3
100.0
20.0
0.0
40.0
50.0
100.0
100.0
100.0
66.7
66.7
83.3
66.7
100.0
63.8
2P
0.0
Sp
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REF
78.4
54.2
43.3
73.9
85.9
73.9
92.9
78.6
84.6
78.4
85.1
86.7
73.1
96.7
90.6
Acc
Table 5. Identification of segments with compromised functions for patients with ‘normal’ EF using the REF and RAS. True positive (TP) are segments in the Border, LGE1-2 or LGE3-4 groups correctly identified as segments with compromised
function; true negative (TN) are segments in the Remote group correctly identified as segments with no compromised function; false negative (FN) are segments in the Border, LGE1-2 or LGE3-4 groups that are not identified as segments
with compromised function; false positive (FP) are segments in Remote group mis-identified as segments with compromised function. Sensitivity (Se) is defined as 100 (TP)/(TP þ FN); specificity (Sp) is defined as 100 (TN)/(TN þ FP);
positive predictive value (þP) is defined as 100 (TP)/(TP þ FP); and negative predictive value (2P) is defined as 100 (TN)/(TN þ FN). Accuracy (Acc) is the average of Se and þP. No. segments refer to the total number of segments
with compromised function, i.e. segments in the Border, LGE1-2 or LGE3-4 groups.
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9
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identification of segments with compromised function (patient 8)
100
10
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90
basal
mid-cavity
apical
80
70
50
40
30
20
10
LGE score
0
B
36
B
R
R
B
4
1
2
3
4
5
6
7
4
B
B
B
B
4
4
4
2
8
9
segments
10
11
12
13
14
15
16
Figure 6. Bar chart of the RAS for all 16 segments for a typical patient with preserved global EF. The detection thresholds for identifying segments with compromised function are denoted by the red horizontal lines. Different thresholds were used for the basal, mid-cavity and apical regions. Segments with RAS below the
threshold are identified as segments with compromised function. B and R denote Border and Remote segments, respectively (both groups have LGE scarring score
of 0). (Online version in colour.)
either failed (based on REF) or was able to identify 1 segment
out of the 12 segments with compromised function (based on
RAS). Nonetheless, we had shown that it is feasible to use the
REF and RAS to identify regions with compromised function
in patients with preserved global EF. The RAS across all 16
segments for patient 8 are shown in figure 6 to illustrate
our approach graphically. For this particular patient, there
are two false negative (FN) segments (segments 3 and 6),
i.e. segments in the Border, LGE1-2 or LGE3-4 groups that
are not identified as segments with compromised function.
4. Discussion
This paper presents a validated computer-based approach
to determine the REF and RAS for patients after a first-time
MI. After MI, while only a selected part of the LV is injured,
the global LV performance is affected. In daily clinical practice, global indicators are still used for the assessment
of the LV function despite their limitation in providing regional information [2,3]. To overcome this limitation, regional
indices such as REF and regional myocardium wall strains
were introduced to estimate localized functions at specific
regions of interest. However, there are still no standardized
approaches for computing the REF from imaging and
the measurement of myocardium wall strains from echocardiography is still heavily dependent on the skills of the
operator. In this paper, we proposed an alternative geometrical approach for assessing regional LV functions that
incorporates information from both the short- and long-axis
CMR images of the LV. We computed the REF and RAS
based on the reconstructed three-dimensional LV geometry
and showed that both indices correlate well with the LGE
CMR scarring score (see figure 5). We also showed that
both indices can be used to identify segments with compromised function with reasonable accuracy in patients with
preserved global EF (see table 5).
Our approach for computing the REF depends on the segmented contours from the CMR images and a user-specified
location of an anatomical landmark (anterior attachment
junction of the right ventricle to the LV). We had shown
that the REF computation is indeed robust to variations in
the location of the anatomical landmark. The reproducibility
of the REF to variations in the segmented contours is not considered in this study as it has been shown that LV parameters
are highly reproducible [29,30]. As such, there is no reason to
doubt that our endocardial contour segmentation protocols
will induce any significant variations to the computed REF
or RAS values. We acknowledge that the values of the computed REF and RAS are dependent on both the segmented
contours and location of the user-defined anatomical landmark. However, it is highly unlikely to change the trend
and interpretation of our reported results.
We used the valve-closure (valve-opening) time to define
the ED and ES frames in our approach for computing the REF
and RAS. It is possible that the time to reach peak REF for
certain segments might not coincide with the ES frame as
defined above. A more accurate approach to track the time
to reach peak REF is then to compute the volume against
time and use the frame of minimum volume to compute
the REF. This is the approach proposed by Suinesiaputra
et al. [31] for assessing intraventricular dyssynchrony for the
LV. However, the main objective of our study is to study
the differences in REF and RAS between patients and control
groups and not to measure any potential LV intraventricular
dyssynchrony. As such, using the valve-opening time to
J. R. Soc. Interface 12: 20150006
RAS (%)
60
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CMR imaging is essential to assess the degree of viability of the
LV myocardial tissues. If available, LGE CMR imaging and
single-photon emission computed tomography are the preferred options for such regional assessment. However, both
techniques require a contrast agent or radionuclide tracer to
improve the image quality for infarct localization. All REF
and RAS data presented in this article are obtained from
CMR images without the use of any contrast agent. Furthermore, the RAS showed an excellent correlation with the REF,
making the RAS another potential candidate to estimate the
extent of MI on a regional level. In daily practice, our proposed
indices can provide information to physicians regarding (i) the
pumping efficiency of all 16 segments of the LV (through the
6. Limitations
Our approach relies on the availability of segmented LV endocardium CMR images that are performed manually, which
is considered time-consuming, as well as the availability of the
magnetic resonance imaging acquisition sequence. Automatic
image segmentation techniques [32,33] could be explored to
speed up the manual segmentation process. Other segmentation
techniques such as shape-based interpolation [34] and superresolution [35,36] can also be potentially used to automate and
increase the accuracy of the cardiac segmentation. The deployment of such an automated segmentation algorithm will allow
our computation of the REF and RAS to be further speeded up
as compared with our current approach. Furthermore, as clinical
CMR images are highly anisotropic with inter-slice spacing in
the range of 5–10 mm, geometrical interpolation between
image slices needs to be performed and this may affect the accuracy of the geometry reconstruction. A 16-segment model was
used to partition different regions of the LV. The adoption of
such a nomenclature allows us to achieve adequate sampling
of the LV without exceeding the relevant limits for clinical and
research purposes. In contrast to standardized nomenclature, a
minor adjustment has been made. Due to the generally intricate
shape of the apical region of the LV, and the fact that the apex is
below the bottom-most apical short-axis slice, it is difficult to
locate the exact location of the apex. Therefore, region 17 is completely excluded from consideration. For partitioning, different
planes were specified to split the mesh into 16 segments based
on two centroid points at the basal and apical contours of the
LV. This two-point method does not allow the partitioning to
take into account the unique curvature of the LV. Currently,
more work is being done to improve this method.
Finally, we acknowledge that the number of patients in
our study is relatively limited (30 patients). As such, the statistical power of our results has to be further substantiated
with additional clinical trials involving more patients. The
two main objectives of this paper are to prove the feasibility
of (i) using the RAS and REF to assess regional LV function
after MI with validation against LGE scarring score and
(ii) applying the RAS and REF to discriminate MI patients
with preserved global EF (more than or equal to 50%). As
a proof-of-concept, we have shown that our approach is
able to meet both objectives successfully in the group of
patients recruited for this study. As the average age of the
patient group is 53 years, we were also limited in our recruitment of healthy normal controls that are age-matched to the
patient group because it is difficult to recruit older subjects
without any history of cardiac diseases. Using normal controls from a younger age group in this study will not be
feasible as that will skew the REF and RAS of the normal
control group to a higher value. This is because it will be
reasonable to assume that myocardium contractility and
function are higher in younger subjects as compared to
older subjects.
11
J. R. Soc. Interface 12: 20150006
5. Clinical applicability
REF) to enable localization of significant coronary artery stenosis and (ii) the contractility of the myocardium (through RAS)
that can elucidate the alterations in the mechanical properties
of the myocardial tissues (figure 6). This approach potentially
offers the physician with new insights into the local characteristics of the myocardial tissues after an infarction, which are
essential for diagnosis and disease stratification.
rsif.royalsocietypublishing.org
define ES is still valid and justifiable as this provides a
consistent basis for comparing across subjects in both the
patient and control groups.
For the normal control group, we observed that the REF
and RAS demonstrated an increase from basal level towards
the apical part of the ventricle. We postulate that this
observation is a result of the increased LV contraction and
deformation towards the apex as compared with the basal
region. This can be explained by the increase in twisting
motion at the apex regions as the myofibres from the endocardial surface run in an almost opposite direction to the
myofibres on the epicardial surface. For the patient group,
we did not observe any such trend in the REF and RAS
(see table 4). This suggests that after MI, the LV contraction
and deformation deviates from the trend seen in normal
controls as certain regions lose contractile function.
Also, the correlation between the REF and RAS (see
figure 3) appears to be nonlinear. We used a quadratic functional to fit the scatter plot of the REF against RAS and
obtained a coefficient of determination (R 2) of 0.88. The
slope of the quadratic fit at higher REF (more than 40%) is
larger compared with that at lower REF, suggesting that the
RAS (REF) could potentially be more sensitive at function
assessment in segments with higher (lower) REF. Combining
both indices for regional function assessment and discrimination of patients will be advantageous and could
potentially increase the accuracy of the assessment.
For identifying segments with compromised function in
patients with preserved EF, we used a detection threshold
of (mean 2 1.0 s.d.) derived from the normal control
group. Our results show that the accuracy of our detection
generally decreases with increasing global EF. For one particular patient in this study ( patient 14 in table 5), the
infarcted segments exhibit REF and RAS that are comparable
to normal controls. This observation is interesting and could
warrant further investigation to ascertain if infarcted segments after MI are able to retain any contractile functions.
It could also be possible that for this particular patient, the
size of the infarct is small compared with the size of the
segments and hence the effect of the infarction on the functionality of the segment is minimal. We can also further
increase the accuracy of our approach by either (i) varying
our detection threshold or (ii) increasing the number of
normal controls. We acknowledge that the number of patient
and normal controls in this study is limited and reiterate that
this is a proof-of-concept study.
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7. Conclusion
Funding statement. This work was supported in part by research grants
from the Agency for Science, Technology and Research (A*STAR),
SERC Biomedical Engineering Programme grant, 092 148 0071 and
132 148 0012.
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Correction
Cite this article: Teo S-K, Vos FJA, Tan R-S,
Zhong L, Su Y. 2015 Regional ejection fraction
and regional area strain for left ventricular
function assessment in male patients after
first-time myocardial infarction. J. R. Soc.
Interface 12: 20150221.
http://dx.doi.org/10.1098/rsif.2015.0221
Soo-Kng Teo, F. J. A. Vos, Ru-San Tan, Liang Zhong and Yi Su
J. R. Soc. Interface 12, 20150006 (2015; Published online 18 February 2015)
(doi:10.1098/rsif.2015.0006)
The x-axis tick labels (for REF) in the original figure 3 are wrong. In figure 3,
there were a total of 11 ticks on the x-axis, and their respective labels are
[0, 20, 40, 60, 80, 100, 0, 20, 40, 60, 80]. This is a mistake that we overlooked
during the proof-read of the manuscript. The range for REF on the x-axis is
from 0% to 100%.
The correct x-axis tick labels are reflected in the revised figure 3 submitted in
this correction. In the revised figure, the correct range for REF is labelled with
six x-axis tick labels from 0% to 100%. The six x-axis tick labels are [0, 20, 40, 60,
80, 100]. There are no other changes to figure 3 except for this re-labelling of the
x-axis tick labels. Also, this error in the labelling does not affect any of the
results presented in the main text.
Finally, we apologize for any inconvenience caused as a result of our
oversight.
140
120
control group
patient group
quadratic fit
R2 = 0.88
100
RAS (%)
rsif.royalsocietypublishing.org
Regional ejection fraction and regional
area strain for left ventricular function
assessment in male patients after
first-time myocardial infarction
80
60
40
20
0
20
40
60
REF (%)
80
100
Figure 3. Correlation between the REF and RAS for the combined data from normal control and
patient groups. The RAS has an excellent correlation with the REF. Fitting to the data is performed
using a quadratic functional. (Online version in colour.)
& 2015 The Author(s) Published by the Royal Society. All rights reserved.