A Preliminary Approach for the Segmentation of Mitral Valve
Regurgitation Jet in Doppler Ecocardiography Images
Eva Costa
1
, Nelson Martins
1,2
, Malik Saad Sultan
2,3
, Diana Veiga
1
, Manuel Jo
˜
ao Ferreira
1,4
,
Sandra Mattos
5
and Miguel Coimbra
2,3
1
Enermeter, Sistemas de Medic¸
˜
ao, Lda, Braga, Portugal
2
Faculdade de Ci
ˆ
encias, Universidade do Porto, Portugal
3
Instituto de Telecomunicac¸
˜
oes, Porto, Portugal
4
Centro Algoritmi, University of Minho, Guimar
˜
aes, Portugal
5
C
´
ırculo do Corac¸
˜
ao de Pernambuco, Recife PE, Brazil
Keywords:
Mitral Regurgitation, Echocardiography, Segmentation, Doppler, Heart, Medical Image Processing.
Abstract:
Rheumatic Fever and Rheumatic Heart Disease remain a major burden among children in developing countries.
Echocardiography with colour flow Doppler is key to early diagnosis. However, the technique requires time
and experienced operators, which are scarce resources in the affected areas. Automatic segmentation of colour
Doppler regurgitation jets could, potentially, reduce the cost of screening, and spread diagnostic accessibility
for a larger number of patients. Ultrasound processing is very challenging due to speckle noise and similarity
of representation of all kinds of tissue. Region-based active contours are suitable tools for the segmentation
in cases of intensity heterogeneities, which makes them interesting algorithms for left atrium segmentation.
HSV colour space describes colour in terms of hues and saturation, which may facilitate the translation of
medical interpretation of the Doppler pseudo-colour into mathematical expression for colour segmentation. A
total of 979 frames from 20 sequences were manually annotated and used to validate the proposed pipeline.
Overall, the results for colour pattern segmentation are promising (sensitivity=0.91 false detection rate=0.10),
but further developments are required for the atrium segmentation (sensitivity=0.80, false detection rate=0.28).
1 INTRODUCTION
Rheumatic Heart Disease (RHD) is the chronic,
longterm sequel of repeated untreated Acute
Rheumatic Fever (ARF) episodes (E. Marijon, 2009).
ARF results from an autoimmune hyper response to
untreated streptococcal infections of the pharynx.
Most of the patients endure active heart inflammation
(carditis) for several months, with the vast majority
having valvulitis on the left side of the heart. The
most affected structure is the mitral valve complex,
which is essential to ensure the unidirectional sys-
temic blood flow from the left atrium to the left
ventricle. Structural changes, such as thickening
or retraction of the valve and its valvar apparatus,
are often found in RHD patients. This may cause
severe strain to the heart leading to abnormal function
with prolapse, stenosis and/or regurgitation. The
major burden of ARF is found among children (5-16
years old) from low to middle income countries and
indigenous populations, and it is directly linked to
hygiene practices and limited access to antibiotics
and medical care (M. Gewitz, 2015).
Most cases of RHD are only diagnosed after
symptoms of heart failure arise and without a clinical
history of ARF. Late diagnosis often results in short
life expectancy, leading to an estimation of 233,000
deaths per year worldwide. Unlike ARF, advanced
cases of RHD are not treatable via secondary prophy-
laxis, since irreversible damage of the heart tissue is
often present (M. Essop, 2014). Surgery is required
for these patients, many of which undergo replace-
ment of their native valves. Early detection increases
life expectancy significantly. Screening campaigns
have shown that most of the early valvar compromise
is silent, thus amenable to go undetected on clinical
examination alone. Borderline or subclinical RHD
cases may not present classical symptoms, but only
mild alterations that can only be identified if subject
to echocardiography (B. Remenyi, 2012).
Even though echocardiography is one of the most
cost effective solutions to screen for heart valve
Costa E., Martins N., Sultan M., Veiga D., Ferreira M., Mattos S. and Coimbra M.
A Preliminary Approach for the Segmentation of Mitral Valve Regurgitation Jet in Doppler Ecocardiography Images.
DOI: 10.5220/0006248900470054
In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017), pages 47-54
ISBN: 978-989-758-215-8
Copyright
c
2017 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
47
anomalies, the average socioeconomic context of the
affected populations is not favourable to this kind
of monitoring RHD patients need. To compensate
for the lack of resources, several screening studies
have been made in different regions to assess the
practicability of some cost reducing measures. In
2015 Lu et al. (J. Lu, 2015) studied the possibility
of simplifying the criteria for RHD detection with
no substantial losses in sensitivity and specificity.
Several studies reported on the feasibility of RHD
screenings being done by non-expert health workers
such as nurses, after being subject to brief training.
Results are favourable and point to the potential use
of the technique in low-resources settings. However,
erroneous regurgitation jet identification and incor-
rect measurement are the predominant causes of false
positives, as the correct and consistent interpretation
of these findings requires experience (M. Ploutz,
2015) (M. Mirabel, 2016) (D. Engelman, 2016).
Moreover, the manual annotation of the regurgitation
jets is time consuming and is highly operator depen-
dent and subjective (Roelandt, 1986) (R. Castello,
1992). The Automatic detection of regurgitation jets
would significantly decrease time and experience de-
pendency of the user. Mitral Regurgitation is visually
assessed via parasternal long axis (PLAX) and apical
4 chamber (A4C) views, with colour flow Doppler
(Figure 1). The pseudo-colour is superimposed on
the brightness mode, representing the movement of
the blood cells in the heart. The Hue represents the
direction of the flow, where the blue and red hues
indicate, respectively, that the flow is moving away
and towards the transducer. The saturation represents
the magnitude of the velocity. Regurgitation jets are
often represented in plain blue hues or high saturation
colour mosaics (blue, yellow and orange hues) due to
turbulence, which may or may not be involved in a
blue or red envelope.
In this paper we propose a semi-automatic
method for the detection of mitral regurgitation jets
candidates, based on HSV space for colour segmen-
tation and localizing active contours for anatomical
segmentation. The paper is organized as follows:
section 2 describes the materials and methods used
for this paper; section 3 presents the proposed method
for regurgitation jet detection; section 4 presents the
results and discussion for the methods exposed in
sections 3; section 5 concludes this paper.
Figure 1: PLAX (left) and A4C (right). Anatomical scheme
(top) and colour flow Doppler mode (bottom).
2 MATERIALS AND METHODS
2.1 Dataset
The dataset used, in this study, consists on 20 se-
quences of paediatric echocardiography, forming a to-
tal of 979 frames, all stored in DICOM format. The
sequences were acquired during the Heart Caravan of
2016, a health care provision initiative which took
place in the State of Para
´
ıba organized by the non-
governmental organization C
´
ırculo do Corac¸
˜
ao. Pa-
tients were all children, with mean age of 10 years
and a standard deviation of 4 years. Half of the pa-
tients were female and the other half were male. All
the patients presented some degree of mitral regurgi-
tation and 5 of them had a previously stablished di-
agnosis of Rheumatic Fever. Eleven of the sequences
were acquired with Vivid I, portable echocardiogra-
phy equipment from GE, with paediatric cardiovas-
cular probe 6S-RS with range of frequency 2.7 to
8 MHz, and image resolution of 422 x 636 pixels.
The frames are stored uncompressed, via Explicit VR
Little-endian transfer syntax. The other 9 were ac-
quired with CX-50, portable echocardiography equip-
ment from Philips, with the paediatric probe S5-1
with frequency range of 1 to 5 MHz, and image reso-
lution of 600x800 pixels. The frames are compressed
in lossless RLE format. Fifteen of the sequences were
acquired via parasternal long axis view, and the other
5 via apical four-chamber view.
2.2 Manual Annotations
The manual annotations used as ground truth for the
results evaluation were drawn by a research associate
experienced in echocardiography analysis. Three cat-
egories of objects were manually segmented: the
BIOIMAGING 2017 - 4th International Conference on Bioimaging
48
left atrium boundaries, the regurgitation jets and the
colour pattern candidates. The colour pattern candi-
dates include detections on the diastole phase, which
cannot be related to regurgitation, and detections on
the systole phase, which may or may not be related to
regurgitation.
2.3 Performance Metrics
In the case of mitral regurgitation jets, it is fundamen-
tal to have a measure of the portion of jets which are
detected. Sensitivity, or true positive rate (1) provide
that information:
T PR = T P/(T P +FN) (1)
On the other hand, it is also important to know
if the detection of negatives is achieved. Specificity
or true negative rate (2) gives the proportion of well
detected negatives for all the real negatives:
T NR = T N/(T N + FP) (2)
Considering the size of the images and the
dimensions of the objects of interest, the majority
of the image will be composed of negative samples,
very easy to detect (note that a significant part of the
image has no information). Therefore, specificity
will not be able to measure small variations of jets
detection. An alternative to the specificity is the false
detection rate (3) which gives the proportion of the
false positives in all the detections:
FDR = FP/(FP + T P) (3)
We will use the same metrics for the segmenta-
tion of the left atrium, evaluating the pixels inside or
outside of the contour, since the purpose of the seg-
mentation is to define a region of interest and not to
get a precise boundary of the chamber.
3 REGURGITATION JET
DETECTION
The detection of regurgitation jets in colour flow
Doppler images requires two phases: colour segmen-
tation and anatomical segmentation. Colour segmen-
tation aims for the detection of the colour patterns
of interest, while anatomical segmentation aims to
define the region of search for those patterns. The
method we propose is represented in Figure 2. Colour
segmentation is divided in two phases: pseudo-colour
isolation and colour pattern segmentation. The result
is a set of candidates of jet regurgitation. The result
of the anatomical segmentation is a set of masks of
the left atrium, which confine the search for colour
patterns.
Both segmentation phases will be explained
throughout this section.
Figure 2: General scheme of the proposed method.
3.1 Colour Segmentation
Based on the clinical assumption that mitral regur-
gitation is always represented by either blue hues
(blood cells move away from the ultrasound trans-
ducer) or high intensity colour mosaics (yellow and
orange hues), we propose a colour-based approach for
the segmentation of regurgitation pattern candidates.
The pseudo-colour mapping of Doppler flow follows
a standard of what is known to be natural and intu-
itive in terms of the human eye perception. The hu-
man perception of colour is often described based on
hues and saturation. We choose to work on the HSV
(hue-saturation-value) colour space, because it offers
the possibility of formulating a mathematical model,
based on physicians interpretations of Doppler colour.
Hue and Saturation together form the chromaticity,
which is the information of colour. Hue describes
a pure colour and saturation describes its purity (a
measure of the dilution of the colour by white light)
(R. Gonzalez, 2008).
3.1.1 Pseudo-colour Isolation
Saturation channel is typically used to isolate regions
of interest in the hue channel. Pseudo-colour has
inherent high degrees of purity, which translate into
high saturation (R. Gonzalez, 2008). Therefore, it is
known that the colour information will be present in
pixels of high saturation. A mask can be generated
by thresholding the saturation channel, to isolate the
pseudo-colour from the grayscale tissue background.
The area of the mask is taken as a measure of its
completeness. A set of thresholds were tested, each
one being a percentage between 0% and 100% of the
maximum intensity of the Saturation channel, with a
step of 10%. The test was made for 15 random sam-
ples from the dataset (3 frames from 5 videos) and
the results are represented in Figure 3. For thresholds
near 0% the area is maximum and for thresholds near
A Preliminary Approach for the Segmentation of Mitral Valve Regurgitation Jet in Doppler Ecocardiography Images
49
100% the area is minimum. In the middle values it
is expected to find a range of thresholds which will
lead to similar area values, since the saturation inten-
sities will not vary significantly for the pseudo-colour
pixels. Tests confirm the existence of a stabilization
plateau, and so, the value in the centre was used as a
threshold, which is 50%. An example of the result of
colour isolation is represented in Figure 4.
Figure 3: Total mask area for each threshold. Each line
represents one frame from 5 randomly selected sequences.
Figure 4: Original PLAX view frame (left) and result from
pseudo-colour isolation (right).
3.1.2 Colour Pattern Segmentation
As previously stated, pseudo-colour formation in
Doppler images relies on the basic convention that
reddish and blueish hues represent blood moving to-
wards and away of the probe. When accessing the
blood flow through the mitral valve, normal unidirec-
tional flow from the left atrium to the left ventricle
should be represented by reddish hues. Other hues
are a product of abnormal motion of blood, such as
regurgitation (Figure 5) or turbulence.
We propose a colour segmentation based, on the
hues, to get candidates of mitral regurgitation. As-
suming that we are interested in all hues but reds and
dark oranges, a simple threshold of the Hue channel
may be sufficient to remove the red component of the
pseudo-colour. Yellow and red hues are represented
by low magnitudes, whereas blue hues are represented
by high magnitudes. The threshold for the red hues
removal is roughly defined, during acquisition time,
by visual inspection of the colour bar provided by the
equipment (Figure 6). The value is set to 10% of the
maximum magnitude of the Hue channel.
Since the pseudo-colour in Doppler only repre-
Figure 5: Regurgitation jets in A4C view. Regurgitation is
represented in a colour mosaic (left) or in blue hues (right).
Figure 6: Colour-bar from Vivid I, GE and Hue channel
magnitude for each colour.
sents one direction of the blood motion, it is expected
to have discontinuities in the representation of jets.
Physicians will often extrapolate the segmentation of
the regurgitation jets to include these regions of pre-
dicted regurgitation. The segmentation is refined us-
ing morphological methods to cover some of the dis-
continuities: morphological closing is applied to con-
nect close blobs and flood fill is applied to fill holes.
3.2 Anatomical Segmentation
World Health Organization guidelines for the
echocardiographic assessment of mitral valve abnor-
malities indicates the use of the A4C and PLAX win-
dows in the diagnosis criteria extraction. Regurgita-
tion jets should be found within the left atrium region
(B. Remenyi, 2012). Both views include other struc-
tures of the heart besides the left atrium and the mitral
valve complex. When selecting a region of interest on
the image for visualization of the colour flow, techni-
cians often include neighbour areas such as part of
the left ventricle, the aorta valve or the right atrium.
These areas, will also have a Doppler response but,
for the assessment of the mitral valve, they are of no
use. Because of that we decided to restrict the search
zone, so that only the left atrium is analysed.
Automatic segmentation of anatomical structures
in ultrasound images is very challenging, due to
the low spatiotemporal resolution, inherent acoustic
interferences and speckle noise, and lack of intensity
and texture differentiation between different tissues
(S. Mazaheri, 2015). Active contour models are very
popular approaches for medical image segmentation.
They usually belong to one of two categories: edge-
based or region-based. Edge-based active contour
models have good performance segmenting regions
BIOIMAGING 2017 - 4th International Conference on Bioimaging
50
of heterogeneous intensity, but are highly sensitive
to noise. Region-based are usually more robust to
noise and initialization, but they assume that the
intensities of each region to be constant, which
makes them not suitable for heterogeneous cases
which we often find in ultrasound images. Localizing
region-based active contours are proposed with the
objective of allying the advantages of both categories
while suppressing their drawbacks. Foreground
and background can be described in terms of small
regions; thus, heterogeneity of the image is not a
problem. A disk kernel (B) moves along each point
of the initial contour, computing exterior and interior
energies, as represented in Figure 7. The total energy
is given by:
E(φ) =
Z
x
δφ(x)
Z
y
B(x,y).F(I(y),φ(y))dydx
+λ
Z
x
δφ(x)
∆φ(x)
dx
(4)
Where I denotes the input image at the domain ,
δφ(x) is the portion of contour centred at x and B is the
neighbourhood kernel around the point (x,y), which is
used to define the localizing area. These elements are
graphically represented by the red circle (B) and the
yellow point (x,y) in Figure 7. F is the internal energy
function. The last term refers to the continuity of the
contour line and is scaled by a factor λ.
An energy optimization algorithm will move the
point by fitting a model to the region.
Figure 7: Graphical representation of the localizing active
contour method. The red circle is the neighbourhood, B, of
the yellow point (x,y) (S. Lankton, 2008).
For this work, the internal energy function F used
was the Uniform Model, defined as:
F = H(φ(y))(I(y) u
x
)
2
+ (1 Hφ(y))(I(y) v
x
)
2
(5)
Where H(φ(y)) represents the exterior of the contour
in the neighbourhood (B) and u
x
represents its mean.
While (1-Hφ(y)) represents the interior and v
x
repre-
sent its mean (S. Lankton, 2008).
For the left atrium segmentation in 2D+t echocar-
diographic sequences, we proposed the application of
localizing region-based active contours. The Doppler
pseudo-colour that is superimposed in the brightness
mode image will interfere with the active contour ad-
justment. For that reason, we use the mask obtained at
the pseudo-colour isolation step, in order to get an es-
timation of the grayscale brightness mode at the back-
ground. The pixels inside the colour mask are passed
to the active contours algorithm as low random val-
ues (below the average of intensity of the image), to
mimic the typical non-tissue speckle affected aspect
of the interior of the atrium. This assumption may
seem as a possible source of errors for the contour
adaptation, specially in cases when the colour mask
is significantly large when compared with the atrium.
However, practical results suggest this is not a signif-
icant drawback for the method. A possible reason for
the ability of adaptation of the localizing active con-
tours in these cases is the internal force term, which
limits the deformability of the contour, maintaining
its shape. The penalizing parameter for the arc-length
of the curvature (λ) must be sufficiently small so it al-
lows deformation of the contour to fit the corners of
the atrium, but high enough to prevent leakage. Four
values between 0.4 and 1.0 were tested and 0.6 was
the one that seemed to keep the best compromise be-
tween the requirements.
Minimal user interaction is required for contour
initialization: two points, one at the centre of the left
atrium and one at the endocardium (inner boundary
of the atrium). The initialization mask is a circle cen-
tred at the first point with radius equal to the distance
between the two points. The stopping condition in
this method is the number of iterations. Since the re-
gional mask will move along each point of the contour
at each iteration, the process may be computationally
heavy. The number of iterations for the first frame
was selected empirically after testing multiple values
of radius of local region and checking at which num-
ber of iterations convergence happened. Frequently
there was no further active contour adaptation or im-
provement around 100 iterations. For the following
frames, the contour initialization was given by the
previous frame contour result. Assuming that the
variation between successive frames is smaller than
the variation between the user input mask and the final
result of the first frame, the number of iterations for
convergence should be smaller. Similar experiment
was made for those frames, starting at 100 iterations
and reducing gradually. The final iterations number
was 50.
Radius of the local region mask is of great rele-
vance for the performance of the algorithm. A study
was made by the author (S. Lankton, 2008) to anal-
yse the effect of the radius on the resulting contour.
The radius should be chosen considering the scale of
A Preliminary Approach for the Segmentation of Mitral Valve Regurgitation Jet in Doppler Ecocardiography Images
51
the object to be segmented and its distance to the sur-
rounding. Very small radius makes the internal en-
ergy function to approximate on of an edge detector
while very large radius make it tend to a global region
statistics approach. Once again, a compromise must
be made to accommodate both requirements of the lo-
cal and global features. Intermediate values of radius
usually result in similar outcomes, but with different
speeds of convergence. Taking the typical dimensions
of the left atrium on the echocardiography windows
into account, a set of eleven radii were tested from 10
to 20, with a step of 1. Results showed small varia-
tion for intermediate values, and a radius of 16 was
chosen.
Before being input to the localizing active con-
tours framework, the images are converted into
grayscale, a histogram equalization is performed and
Gaussian blur is applied for smoothing.
4 RESULTS AND DISCUSSION
4.1 Colour Pattern Segmentation
The dataset was subject to the colour isolation and
pattern segmentation pipeline. The search for patterns
was restricted to the regions of the left atrium given by
the manual annotations. The results from this process
are evaluated against the manual annotations of colour
patterns (regurgitation jets and candidates).
The mean values for the performance metrics are:
sensitivity of 0.913 and false detection rate of 0.103.
Sensitivity shows that more than 91% of the pixels
are correctly identified as jets or candidates by the
pipeline, which is a satisfactory result. An example
can be seen in Figure 9. About 10% of all the detec-
tions are false positives. The distribution of the met-
rics for the 20 cases (Figure 8) show no significate
dispersion between quartiles, with only one outlier.
The results suggest the colours of interest are cor-
rectly identified.
The anatomical segmentation was applied on the
resulting background of the colour isolation of the
images. The evaluation is made against the manual
annotations of the left atrium. The mean values for
the performance metrics are: sensitivity of 0.805 and
false detection rate of 0.284.
More than 80% of the pixels belonging to left atria
were correctly identified as positives, which means
the framework achieved the purpose of defining a
rough region of interest. However, the remaining ex-
cluded pixels may contain valuable colour informa-
tion for the regurgitation jet search. 28% of false pos-
itives indicate that some parts of the boundary were
Figure 8: Whiskers plot of sensitivity and false detection
rate for colour pattern detection.
Figure 9: Result of segmentation by the colour pattern seg-
mentation pipeline (left) and manual annotation of the jet
(right).
extended to neighbour chambers, which may increase
the false detections of regurgitation jets candidates.
Examples of both false positives and false negatives
can be seen in Figure 11.
The fact that the boundaries are often not repre-
sented on the ultrasound image is probably the main
source of error for this framework.
The distribution of the results (Figure 10) shows
some variability between cases, which may indicate
that acquisition conditions affect the performance of
the framework.
4.2 Regurgitation Jet Detection
The final tests consist in applying the left atrium
masks obtained by the localizing active contours
framework as the spatial restriction for the colour
pattern segmentation, in order to understand the im-
pact caused on the pattern segmentation by the semi-
automatic atrium segmentation. The mean values for
the performance metrics are: sensitivity of 0.826 and
false detection rate of 0.228. The distribution of the
results (Figure 12) is similar to the distribution for the
left atrium segmentation. It is possible to infer that
the faults on the anatomical segmentation decrease
the true jet candidates’ detection and increase false
detections. Both cases can be seen on the examples in
Figure 13.
Between the jet candidates, only a small part is
identified with confidence as mitral regurgitation. The
results for the detection of those candidates is: sensi-
BIOIMAGING 2017 - 4th International Conference on Bioimaging
52
Figure 10: Whiskers plot of sensitivity and false detection
rate for left atrium segmentation.
Figure 11: Results of anatomical segmentation (top) and
manual annotations (bottom). First column shows a satis-
factory result. Second column is an example of overesti-
mation of the atrium area. Third column is an example of
underestimation of atrium area.
tivity of 0.863 and false detection rate of 0.513. The
sensitivity increases when compared to the overall de-
tection of patterns. However, the false detection rate
is significate, since the images contain a substantial
amount of non-regurgitation colour patterns.
Another possibility for false detection suppres-
sion, is to confine the search for regurgitation to the
systolic phase of the cardiac cycle. The dataset was
manually identified as systolic or diastolic and the
pipeline was applied only on the systolic group. The
results for detection of real jets are: sensitivity of
0.840 and false detection rate of 0.355. As expected,
the false detection rate decreases if the diastolic phase
is not included, since all the candidates present on that
phase are certainly not regurgitation jets.
5 CONCLUSION
A new method for the segmentation of the mitral re-
gurgitation jet in sequences of 2D+t Doppler echocar-
diography was proposed. While the colour patterns of
interest are correctly identified as regurgitation candi-
dates, the anatomical spatial restriction step is still far
from ideal. Localizing region-based active contours
Figure 12: Whiskers plot of sensitivity and false detection
rate for the combined pipeline.
Figure 13: Results of combined pipeline (top) and manual
annotations (bottom). Occurrence of false negatives (left)
and false positives (right).
adapt to the heterogeneity of the left atrium, but do
not recover well from discontinuities in the bound-
ary of the atrium. This usually occurs on the part of
the boundary formed by the mitral valve, because of
the opening of the leaflets. The results confirm that
the false positives can be decreased by limiting the
search to the systolic phase. The confinement of the
search for that part of the cycle may also improve
the performance of the localizing region-based active
contour, since the leaflets are closed, forming the re-
quired boundary. However, the contour initialization
for each frame is currently the resulting mask from
the previous frame. The division of the sequence into
cycles will create the need for a re-initialization strat-
egy for each systole in a sequence
The addition of manual annotations by other sub-
jects may provide more certainty for the classification
of the colour pattern candidates, and therefore reduce
the false positives. Regarding the colour, it is diffi-
cult to determine further characteristics for the dis-
tinction of candidates between regurgitation jet and
turbulence or other phenomena. Other characteristics
such as shape and relative position or orientation of
the candidates may be useful to decrease the false de-
tection ratio of the jet detection.
Future developments include the detection of the
A Preliminary Approach for the Segmentation of Mitral Valve Regurgitation Jet in Doppler Ecocardiography Images
53
cardiac cycle phase and inclusion of characteristics
other than colour for jet detection.
ACKNOWLEDGEMENTS
This article is a result of the project (NORTE-
01-0247-FEDER-003507-RHDecho), co-funded by
Norte Portugal Regional Operational Programme
(NORTE 2020), under the PORTUGAL 2020 Part-
nership Agreement, through the European Regional
Development Fund (ERDF). This work also had the
collaboration of the Fundac¸
˜
ao para a Ci
ˆ
encia a e Tec-
nologia (FCT) grant no: PD/BD/105761/2014 and has
contributions from the project NanoSTIMA, NORTE-
01-0145-FEDER-000016, supported by Norte Portu-
gal Regional Operational Programme (NORTE 2020),
through Portugal 2020 and the European Regional
Development Fund (ERDF). GE and PHILLIPS for
providing the equipment. Health professionals from
C
´
ırculo do Corac¸
˜
ao for their volunteer work and data
collection. The Health Secretary of Para
´
ıba for their
support to the actualization of the Heart Caravan.
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