# Model-Based Analysis of Flow-Mediated Dilation and Intima-Media Thickness

Authors: G. Bartoli; G. Menegaz; M. Lisi; G. Di Stolfo; S. Dragoni; T. Gori
Journal: International Journal of Biomedical Imaging
 (2008)

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## Abstract

We present an end-to-end system for the automatic measurement of flow-mediated dilation (FMD) and intima-media thickness (IMT) for the assessment of the arterial function.
The video sequences are acquired from a B-mode echographic
scanner. A spline model (deformable template) is fitted to the
data to detect the artery boundaries and track them all along
the video sequence. The a priori knowledge about the image
features and its content is exploited. Preprocessing is performed
to improve both the visual quality of video frames for visual
inspection and the performance of the segmentation algorithm
without affecting the accuracy of the measurements. The system
allows real-time processing as well as a high level of interactivity
with the user. This is obtained by a graphical user interface
(GUI) enabling the cardiologist to supervise the whole process
and to eventually reset the contour extraction at any point in time.
The system was validated and the accuracy, reproducibility, and
repeatability of the measurements were assessed with extensivein vivo experiments. Jointly with the user friendliness, low cost,
and robustness, this makes the system suitable for both research
and daily clinical use.


---

## Body

## 1. Introduction

The assessment
and characterization of the endothelial function (i.e., the production of
protective factors from the vascular endothelium) is a topic of both clinical
and research importance. Techniques that assess this function have been
proposed as useful tools in the diagnosis and management of cardiovascular
diseases [1]. In
particular, endothelium-dependent changes in hemodynamics (blood flow, blood
pressure, vascular diameter, and tone) have been used as surrogate markers of
vascular health and in the management of patients with cardiovascular diseases
[2–4]. In detail, the endothelium
responds to changes in intravascular shear stress by releasing several
compounds which determine relaxation of smooth muscle cells and, subsequently,
vasodilation [5]. In humans in
vivo, such changes in shear stress can be experimentally determined by
inflating to suprasystolic pressure a pneumatic cuff around the forearm for 4
minutes and 30 seconds. Upon release of the cuff, the sudden increase in blood
flow (and shear stress) that follows reperfusion is a potent stimulus for endothelium-dependent
vasorelaxation, which can be observed using ultrasounds [6, 7]. In sum, the endothelium
shows measurable responses to flow changes, determining endothelium dependent,
flow-mediated dilation (FMD). These measures might have a clinical potential,
as several studies show an association between impaired FMD and poorer
prognosis [2–4].High-resolution B-mode ultrasonography (US) is a cheap
and noninvasive technique that permits depiction of peripheral arteries. Image
analysis techniques allow accurate, objective, and repeatable measurement of
the diameter of such arteries. Several methods based on the detection of the
edges of the arterial wall have been proposed over the last ten years. The
first studies used a tedious manual procedure, which had a high intra- and
interobserver variability. Some interactive methods tried to reduce this variability
by attracting manually drawn contours to image features, like the maximum image
gradient, where the vessel bound is assumed to be localized. Some more recent
efforts are focused on dynamic programming or deformable models and neural
networks [8–15].All these methods present some common limitations.
First, edge detection techniques are often undermined by speckle noise. Second,
most methods require expert intervention to manually guide or correct the
measurements, thus being prone to introduce operator-dependent variability. As
well, temporal continuity of the measurements (as compared to measurements at
predetermined time points) is another aspect that has not been exploited enough
in previous work. Finally, there is a general lack of large-scale validation
studies in most of these techniques.However, getting rid of the first two factors out of
the scopes of this contribution as, in general, it is not desired by
cardiologists. The presence of speckle noise cannot be avoided unless denoising
is performed on the images, which could alter the diagnostically relevant
information. Furthermore, the doctor must be enabled to intervene in the
segmentation process in case some causes of degradation (like a sudden movement
of the patient) make the algorithm diverge.Accordingly, in the present contribution we propose a
new technique for the detection of artery boundaries which has the advantages
of (i) improved accuracy and robustness to noise in the contour identification
due to the use of a spline-based model for the artery contours; (ii) improved
human-machine interaction through the design of an ad hoc graphical user
interface (GUI); (iii) low-computational complexity; (iv) portability, and (v)
low cost. In this respect, it is worth mentioning that currently there is no
commercially available system able to perform both the FMD and IMT measurements
automatically. Furthermore, the solution proposed in [11] for assessing the FMD
functionality is proprietary and expensive, besides not being portable since it
is implemented on device.This paper is organized as follows. Section2
describes the proposed system, including the experimental setup, preprocessing,
and contour extraction. Section 3 illustrates the system validation from the
clinical point of view as well as the performance, and Section 4 derives
conclusions. Finally, the GUI is illustrated in the appendix.


## 2. Methods

### 2.1. Experimental Setup

The technique
that was used for measuring the FMD is described in
detail in [16–18]. Briefly, the left arm is immobilized using a
deflation pillow and a pneumatic cuff is placed at the wrist (i.e., distally to
the imaged site). The radial artery is imaged 10–15cm below the
elbow at rest for 60 seconds to acquire the baseline diameter. A pneumatic cuff
positioned around the wrist is then inflated to 250mmHg. After an
interval of 4′30′′, the cuff is deflated to achieve reactive hyperemia.
The artery is imaged for the following 4′30′′. Studies have shown that FMD does not change for
occlusion times comprised between 4′30′′ and 10′ [19]. ECG-triggered
end-diastolic frames are captured at a frame rate of 1 second by the Acuson
Sequoia 512 high-resolution echograph from Sonoma Health, Calif, USA
(http://www.sonomahealth.com/).Each FMD movie consists of600 frames: 60 for the
baseline period, 270 during cuff
inflation, and 270 after cuff
deflation. FMD is defined as 100 times (maximum diameter after cuff deflation
minus baseline diameter) divided by the baseline diameter. FMD is expressed as
percentage increase from this resting diameter.For intima-media thickness (IMT) analysis, an image of
the posterior common carotid wall1cm proximal to
the aortic bulb was taken using a linear 15 MHz probe and an Acuson Sequoia 512
ultrasound equipment. IMT was calculated as the mean distance between the two
spline guides positioned at the lumen-intima and the media-adventitia interfaces (“leading
edge” principle) [20].


### 2.2. Model-Based Segmentation of the Vessels Boundaries

The vessel
segmentation consists in modeling the artery boundaries by a couple of cubic
splines which are independently fitted to the contours following the
minimization of a cost function. Preprocessing on the echographic images is
performed in order to (i) improve the visual quality of the frames for visual
inspection; (ii) support the segmentation algorithm; (iii) speed up the
processing to enable real-time functionality. However, this does not affect the
accuracy of the segmentation, as will be discussed in what follows.


### 2.3. Preprocessing

Data were
acquired by a workstation and put in AVI format by a freeware software tool (VirtualDub).Figure1 shows a typical image. In order to reduce the
required memory storage and to speed up the subsequent processing, the user can
choose to low-pass filter and downsample the frames by a factor two along both
dimensions. This reduction in size does not compromise the accuracy of the
measurement of the vessel diameter. This was proved by comparing the output
parameters (FMD, diameters, and IMT) obtained by processing some test videos
with and without subsampling and calculating the impact on accuracy. Results
show that this introduces a variability of about 0.1–0.3% in the FMD
measure, which is negligible with respect to the other sources of variability
(like the patient movement during the acquisition) and it is of the same order
of magnitude of others (the positioning of the observation window and the
setting of the metric units). However, we would like to emphasize that
subsampling is an option and can be switched off. The results presented here
were obtained using original size full images.Figure 1
Typical FMD
image resulting from the echo scanner.Two preprocessing steps are performed before boundary
detection: contrast enhancement by histogram stretching and sharpening via a
negative of Laplacian filter of size3×3. Such operations are functional to both visual
inspection and boundary detection.In order to further speed up the processing, the
graphical interface enables the selection of a region of interest (ROI). Again,
this is based on the fact that cardiologists identify a region in the image
which corresponds to the portion of the vessel that is suitable for the
measurement under way. However, the option can be disabled and the analysis
can be performed on the entire images. The
selection of the ROI is performed on a representative image obtained by
averaging of all the images along the sequence. In this way, the positioning of
the observation window in an artifact-free region is ensured. The window is
then propagated along the sequence.The effects of the preprocessing steps are illustrated
in Figure2. The original cropped image is shown in Figure 2(a), the effect of
contrast enhancement through histogram stretching is illustrated in Figure
2(b), and the final image after sharpening is shown in Figure 2(c). As
mentioned above, these steps support the subsequent contour extraction process
as they enhance the contrast without compromising the accuracy of the target
measurement, as discussed above.Preprocessing. (a) Original image; (b) after histogram
stretching; (c) after sharpening.
(a)(b)(c)


### 2.4. Vessel Segmentation

The  a priori knowledge about the shape of the
vessels is exploited for segmentation. It is a fact that the vessel contours
are smooth, have very small curvature, and are almost perfectly horizontal.
Accordingly, they can be conveniently modeled by low-order polynomials. The use
of a cubic spline proved to be suitable to the purpose. Importantly, as
mentioned above, local tissue modifications corresponding to high curvatures
must be discarded for the analysis as they would produce spurious changes in
the estimated diameter that do not hold any information relevant to the
measurement of the endothelial function. The choice of using smooth functions
to model the contour of the vessel solves this problem as local high curvature
segments are automatically disregarded. Should a more complex border be
identified, it would be sufficient to add more knots in the interpolation
procedure. We refer to [21, 22] for a more detailed discussion about spline-based
interpolation.Two cubic splines are independently fitted to the
artery walls and progressively propagated along the video sequence such that
the final estimation of the contours in imagen is used for
initialization in image n+1.The spline interpolating curveS(x) is a piecewise
continuous function consisting of a set of
polynomial segments Sk(x). Each segment can be written as a polynomial of third
degree:(1)Sk(x)=∑i=03aki(x−xk)i,∀x∈[xk,xk+1]. The set of
points {xk,yk,k=1,…,n} is the control
points of the curve (the knots)
which are set manually on the first image of the video sequence for initialization.
Results (that are not reported here) show that three to five points allow
capturing the shape of the artery wall with the desired accuracy. Due to the
smoothness of the contours, the knots were equally spaced along the horizontal
dimension (i.e., the {xk} coordinates
were kept unchanged) while the vertical positions {yk} of the knots
are the free parameters. The value of the {aki} coefficients
follow.The objective of the cost function is the maximization
of the contrast between the image regions that are above and below each curve,
respectively, within a predefined radius, as illustrated in Figure3.Figure 3
Search
region for positioning the spline curve.Letr be the radius
of the circular-section tube centered on the current estimation of the
boundary. The radius was set to either 12 or 6pixels in case
the downsampling option is switched off and on, respectively. Let then Ωu and Ωl be the two
regions intercepted by the tube above and below the spline curve. The average
gray values of these regions are(2)g¯ν=1|Ων|∑ΩνI(i,j),where I(i,j) is the gray
level at position (i,j) in the image, |Ων| is the
cardinality of the set, and ν=u,l, where u stands for upper and v for lower, respectively. The cost function f is defined as
the difference of the average gray levels in the two regions, that is, the
measure of the contrast that we use here:(3)f=−|g¯u−g¯l|.The average intensity is
calculated columnwise such that(4)f=−∑j=1Ncol|g¯u(j)−g¯l(j)|,g¯u(j)=∑i=y^jy^j+rI(i,j),g¯l(j)=∑i=y^j−ry^jI(i,j)with y^j being the
estimation of the vertical coordinate of the spline point at horizontal
position j at the current
iteration. A multidimensional unconstrained nonlinear minimization procedure is
used to determine the y^k,opt optimal values
for the y coordinates of
the splines knots:(5)y^k,opt=miny^k{f}.Given the definition of the cost
function, it is straightforward to conclude that contrast enhancement improves
the performance. An example of the result is shown in Figure 4, where the model
is superimposed to the ROI image. To reach the segmentation of the entire set
of images, the model is propagated through the sequence such that the
boundaries that have been determined in frame n serve as
initialization of the search procedure in frame n+1. However, the GUI allows the user to supervise the
process and eventually stop it at any time in case of unsatisfactory result. In
this case, the spline knots can be repositioned manually for the remaining of
the analysis. Alternatively, the user can activate a temporal filtering
operation to eliminate badly estimated contours in one or a group of frames and
replace them with an  a
posteriori prediction. The cardiologist can then decide about the
suitability of the result and thus decide to repeat the measurement or to drop
the corresponding set of frames.Figure 4
Results: the modeling splines corresponding
to the upper and lower boundaries are represented as red curves.


### 2.5. Diameter Estimation

Once the models
are fitted to the boundaries, the artery diameter is estimated as the average
of the columnwise difference among the vertical coordinates of the two splines
over the frame (the subscriptopt was omitted for simplicity of notations):(6)dm=1Ncol∑j=1Ncol(y^j,u−y^j,l),where m is the frame
index and {y^j,u,y^j,l} are the y coordinates of
the two splines at column j.The reason why it is expected that relation (6)
provides a good estimation of the diameter is that in these images the artery
wall is almost perfectly horizontal, especially in case an ROI is selected.
Indeed, the correctness of this assumption was confirmed a posteriori by the
validation of the method, showing a very good correlation among the values of
the diameters and FMD with those of the gold standard.The other parameters which characterize the FMD
function are (i) the time interval between cuff deflation and peak diameter, (time to peak diameter) and (ii) the FMD percent diameter dilation, given by
the percent difference of the peak diameter with respect to the
baseline:(7)FMD=dpeak−dbasaldbasal[%].The value of the diameter as a function of the frame
position within the movie (or, equivalently, as a function of time) is stored
for further analysis. Following the request of the cardiologist, a smoothed
version of the diameter plot is also made available as an option. This is
obtained by low-pass filtering of the original profile with a kernel of length
three.


## 2.1. Experimental Setup

The technique
that was used for measuring the FMD is described in
detail in [16–18]. Briefly, the left arm is immobilized using a
deflation pillow and a pneumatic cuff is placed at the wrist (i.e., distally to
the imaged site). The radial artery is imaged 10–15cm below the
elbow at rest for 60 seconds to acquire the baseline diameter. A pneumatic cuff
positioned around the wrist is then inflated to 250mmHg. After an
interval of 4′30′′, the cuff is deflated to achieve reactive hyperemia.
The artery is imaged for the following 4′30′′. Studies have shown that FMD does not change for
occlusion times comprised between 4′30′′ and 10′ [19]. ECG-triggered
end-diastolic frames are captured at a frame rate of 1 second by the Acuson
Sequoia 512 high-resolution echograph from Sonoma Health, Calif, USA
(http://www.sonomahealth.com/).Each FMD movie consists of600 frames: 60 for the
baseline period, 270 during cuff
inflation, and 270 after cuff
deflation. FMD is defined as 100 times (maximum diameter after cuff deflation
minus baseline diameter) divided by the baseline diameter. FMD is expressed as
percentage increase from this resting diameter.For intima-media thickness (IMT) analysis, an image of
the posterior common carotid wall1cm proximal to
the aortic bulb was taken using a linear 15 MHz probe and an Acuson Sequoia 512
ultrasound equipment. IMT was calculated as the mean distance between the two
spline guides positioned at the lumen-intima and the media-adventitia interfaces (“leading
edge” principle) [20].


## 2.2. Model-Based Segmentation of the Vessels Boundaries

The vessel
segmentation consists in modeling the artery boundaries by a couple of cubic
splines which are independently fitted to the contours following the
minimization of a cost function. Preprocessing on the echographic images is
performed in order to (i) improve the visual quality of the frames for visual
inspection; (ii) support the segmentation algorithm; (iii) speed up the
processing to enable real-time functionality. However, this does not affect the
accuracy of the segmentation, as will be discussed in what follows.


## 2.3. Preprocessing

Data were
acquired by a workstation and put in AVI format by a freeware software tool (VirtualDub).Figure1 shows a typical image. In order to reduce the
required memory storage and to speed up the subsequent processing, the user can
choose to low-pass filter and downsample the frames by a factor two along both
dimensions. This reduction in size does not compromise the accuracy of the
measurement of the vessel diameter. This was proved by comparing the output
parameters (FMD, diameters, and IMT) obtained by processing some test videos
with and without subsampling and calculating the impact on accuracy. Results
show that this introduces a variability of about 0.1–0.3% in the FMD
measure, which is negligible with respect to the other sources of variability
(like the patient movement during the acquisition) and it is of the same order
of magnitude of others (the positioning of the observation window and the
setting of the metric units). However, we would like to emphasize that
subsampling is an option and can be switched off. The results presented here
were obtained using original size full images.Figure 1
Typical FMD
image resulting from the echo scanner.Two preprocessing steps are performed before boundary
detection: contrast enhancement by histogram stretching and sharpening via a
negative of Laplacian filter of size3×3. Such operations are functional to both visual
inspection and boundary detection.In order to further speed up the processing, the
graphical interface enables the selection of a region of interest (ROI). Again,
this is based on the fact that cardiologists identify a region in the image
which corresponds to the portion of the vessel that is suitable for the
measurement under way. However, the option can be disabled and the analysis
can be performed on the entire images. The
selection of the ROI is performed on a representative image obtained by
averaging of all the images along the sequence. In this way, the positioning of
the observation window in an artifact-free region is ensured. The window is
then propagated along the sequence.The effects of the preprocessing steps are illustrated
in Figure2. The original cropped image is shown in Figure 2(a), the effect of
contrast enhancement through histogram stretching is illustrated in Figure
2(b), and the final image after sharpening is shown in Figure 2(c). As
mentioned above, these steps support the subsequent contour extraction process
as they enhance the contrast without compromising the accuracy of the target
measurement, as discussed above.Preprocessing. (a) Original image; (b) after histogram
stretching; (c) after sharpening.
(a)(b)(c)


## 2.4. Vessel Segmentation

The  a priori knowledge about the shape of the
vessels is exploited for segmentation. It is a fact that the vessel contours
are smooth, have very small curvature, and are almost perfectly horizontal.
Accordingly, they can be conveniently modeled by low-order polynomials. The use
of a cubic spline proved to be suitable to the purpose. Importantly, as
mentioned above, local tissue modifications corresponding to high curvatures
must be discarded for the analysis as they would produce spurious changes in
the estimated diameter that do not hold any information relevant to the
measurement of the endothelial function. The choice of using smooth functions
to model the contour of the vessel solves this problem as local high curvature
segments are automatically disregarded. Should a more complex border be
identified, it would be sufficient to add more knots in the interpolation
procedure. We refer to [21, 22] for a more detailed discussion about spline-based
interpolation.Two cubic splines are independently fitted to the
artery walls and progressively propagated along the video sequence such that
the final estimation of the contours in imagen is used for
initialization in image n+1.The spline interpolating curveS(x) is a piecewise
continuous function consisting of a set of
polynomial segments Sk(x). Each segment can be written as a polynomial of third
degree:(1)Sk(x)=∑i=03aki(x−xk)i,∀x∈[xk,xk+1]. The set of
points {xk,yk,k=1,…,n} is the control
points of the curve (the knots)
which are set manually on the first image of the video sequence for initialization.
Results (that are not reported here) show that three to five points allow
capturing the shape of the artery wall with the desired accuracy. Due to the
smoothness of the contours, the knots were equally spaced along the horizontal
dimension (i.e., the {xk} coordinates
were kept unchanged) while the vertical positions {yk} of the knots
are the free parameters. The value of the {aki} coefficients
follow.The objective of the cost function is the maximization
of the contrast between the image regions that are above and below each curve,
respectively, within a predefined radius, as illustrated in Figure3.Figure 3
Search
region for positioning the spline curve.Letr be the radius
of the circular-section tube centered on the current estimation of the
boundary. The radius was set to either 12 or 6pixels in case
the downsampling option is switched off and on, respectively. Let then Ωu and Ωl be the two
regions intercepted by the tube above and below the spline curve. The average
gray values of these regions are(2)g¯ν=1|Ων|∑ΩνI(i,j),where I(i,j) is the gray
level at position (i,j) in the image, |Ων| is the
cardinality of the set, and ν=u,l, where u stands for upper and v for lower, respectively. The cost function f is defined as
the difference of the average gray levels in the two regions, that is, the
measure of the contrast that we use here:(3)f=−|g¯u−g¯l|.The average intensity is
calculated columnwise such that(4)f=−∑j=1Ncol|g¯u(j)−g¯l(j)|,g¯u(j)=∑i=y^jy^j+rI(i,j),g¯l(j)=∑i=y^j−ry^jI(i,j)with y^j being the
estimation of the vertical coordinate of the spline point at horizontal
position j at the current
iteration. A multidimensional unconstrained nonlinear minimization procedure is
used to determine the y^k,opt optimal values
for the y coordinates of
the splines knots:(5)y^k,opt=miny^k{f}.Given the definition of the cost
function, it is straightforward to conclude that contrast enhancement improves
the performance. An example of the result is shown in Figure 4, where the model
is superimposed to the ROI image. To reach the segmentation of the entire set
of images, the model is propagated through the sequence such that the
boundaries that have been determined in frame n serve as
initialization of the search procedure in frame n+1. However, the GUI allows the user to supervise the
process and eventually stop it at any time in case of unsatisfactory result. In
this case, the spline knots can be repositioned manually for the remaining of
the analysis. Alternatively, the user can activate a temporal filtering
operation to eliminate badly estimated contours in one or a group of frames and
replace them with an  a
posteriori prediction. The cardiologist can then decide about the
suitability of the result and thus decide to repeat the measurement or to drop
the corresponding set of frames.Figure 4
Results: the modeling splines corresponding
to the upper and lower boundaries are represented as red curves.


## 2.5. Diameter Estimation

Once the models
are fitted to the boundaries, the artery diameter is estimated as the average
of the columnwise difference among the vertical coordinates of the two splines
over the frame (the subscriptopt was omitted for simplicity of notations):(6)dm=1Ncol∑j=1Ncol(y^j,u−y^j,l),where m is the frame
index and {y^j,u,y^j,l} are the y coordinates of
the two splines at column j.The reason why it is expected that relation (6)
provides a good estimation of the diameter is that in these images the artery
wall is almost perfectly horizontal, especially in case an ROI is selected.
Indeed, the correctness of this assumption was confirmed a posteriori by the
validation of the method, showing a very good correlation among the values of
the diameters and FMD with those of the gold standard.The other parameters which characterize the FMD
function are (i) the time interval between cuff deflation and peak diameter, (time to peak diameter) and (ii) the FMD percent diameter dilation, given by
the percent difference of the peak diameter with respect to the
baseline:(7)FMD=dpeak−dbasaldbasal[%].The value of the diameter as a function of the frame
position within the movie (or, equivalently, as a function of time) is stored
for further analysis. Following the request of the cardiologist, a smoothed
version of the diameter plot is also made available as an option. This is
obtained by low-pass filtering of the original profile with a kernel of length
three.


## 3. Results and Discussion

The gold
standard for validation was obtained by manual segmentation of the artery by an
expert.The criteria used for performance assessment areaccuracy, reproducibility, and repeatability of the measurements. It is
worth to outline that the only source of variation in the performance of the
algorithm is due to the human intervention, namely, the manual positioning of
the ticks setting the units, the choice of the observation window, and the
patient movement. The first one introduces a systematic scaling in the
measurements expressed in metric units (micron), while leaves unchanged the
ones expressed as numbers of pixels. This also applies to the comparison of the
measured values with those obtained by the gold standard. Accordingly, the
system validation presented hereafter refers to repeatability and
reproducibility in such respect.Concerning the preprocessing, the only step that could
compromise the accuracy of the measurements is downsampling. In order to assess
this, we performed the diameter measurements on a typical video sequence and
compared the results obtained without subsampling. The ticks were fixed on the
full-resolution reference image and kept in the same position after
downsampling to avoid the systematic error mentioned above. The full images
were used (i.e., the ROI functionality was disabled).The correlation coefficient between the two sets of
measurement of the diameter was0.99841, and the one-way ANOVA analysis confirmed that the
two sets of samples are obtained from two distributions having the same mean (P=.57). The signal-to-noise
ratio (SNR) between the two curves is 62.77dB. For the
FMD, according to relation (7), the accuracy depends on that of the diameter
estimation as well as on the positioning of the peak. Tests performed on a set
of typical sequences revealed that downsampling leads to a change in the FMD in
the range 0.1–0.3%, which is smaller than the variation due to the other
causes mentioned above. Accordingly, switching on the downsampling option would
not compromise the accuracy of the measurements.In what follows, the setup used for validation is
described.(i)
Accuracy:100 ultrasound
images of an artery were analyzed manually using a modified version of  Image   J [16]. The average of two such measurements was considered
as the gold standard.(ii)
Repeatability:25 healthy young
volunteers (age range 24–37, 9 females)
underwent measurement of arterial diameter and FMD twice with a delay of 24 hours.(iii)
Reproducibility in FMD studies:65 ultrasound
images of the artery and 65 complete FMD
movies were analyzed twice using our software. The movies were acquired in 27 healthy
volunteers (age 23 to 30 years, 12 males), 12 hypertensive
patients (age 40–60, 6 males), 8 patients with
coronary artery disease (age 45–65, all males), 8 smokers (age 28–30, 5 females), and 10 patients with
congestive heart failure (age 60–78, 7 males).(iv)
Reproducibility in IMT studies:60 US images of
the posterior wall of the common carotid artery were analyzed twice using our
software. The images were acquired in 15 healthy volunteers (age 23 to 30
years, 7 males) and 45 patients with coronary artery disease (age 45–65, 40
males).Healthy volunteers included medical residents with
negative anamnesis of any active disease, nonsmokers, with a systolic blood
pressure lower than130mmHg, and a
diastolic blood pressure lower that 80 mmHg. Smokers
had a history of 5–10 cigarettes/day
for 3–10 years and were
asked to smoke one cigarette immediately before FMD measurement. Hypertensive
subjects (age 40–60, 6 males) had
undergone 24-hour blood
pressure monitoring which documented average systolic values larger than 140 and diastolic
values larger than 90mmHg. FMD was
measured before initiation of antihypertensive therapy. The diagnosis of
coronary artery disease was made based on coronary angiography. These patients
were on treatment with aspirin. All other active treatments were suspended for
at least 24 hours.
Congestive heart failure patients included subjects with symptoms of heart
failure (NYHA class II-IV) and an ejection fraction lower than 40%.


### 3.1. Performance Analysis

To characterize
the system performance, the following descriptors were used.(i)
Intraclass
correlation coefficient (ICC);(ii)
coefficient of
variation (CV). It is a measure of the dispersion of a probability
distribution and it is calculated as the standard deviation (SD) of repeated
measurements divided by their mean:(8)CV=100×σμ,where σ and μ are the
standard deviation and the mean of the measurement distribution, respectively;(iii)
range of
variation (R). It is calculated for repeatability and
reproducibility studies as the mean of the absolute differences between
repeated measurements;(iv)
bias (B) across separate measurements. Bias is given by the
mean of these differences. Bland and
Altman plots [23] were
derived. In all cases, analysis was performed in a randomized, blinded fashion.
Statistical analysis for the reproducibility studies was performed as
recommended by published guidelines [24] using Statview (SAS
Institute, NC, USA). FMD was calculated as in (7).  Besides the patient
movement, there are two potential causes of variability in the measurements.
The first one is the positioning of the observation window (ROI), and the
second is the manual positioning of the tags setting the units. The following
sections provide the characterization of the system.


#### 3.1.1. Accuracy

In order to
assess the accuracy,100 frames were
analyzed both manually and using the new software. The two sets of measurements
are highly correlated, as illustrated in Figure 5(a). The intraclass
coefficient was ICC=0.97 (P<.0001). The range of variation was equal to RV=76(2–220)μm for arteries
with an average diameter of 2.35mm. The Bland
and Altman plot is shown in Figure 5(b).FMD analysis. (a) Correlation between manual (gold
standard, horizontal axis) and automatic (vertical axis) analyses; (b) Bland and Altman plot: difference between software
and manual measurements versus their average.
(a)(b)


#### 3.1.2. Repeatability

The intraclass
coefficient for diameter measurements repeated with a delay of24 hours on the
same 25 subjects was ICC=0.85 (Figure 6(a)).
The intraclass correlation coefficient for repeated FMD measurements following
the same paradigm was ICC=0.63 (Figure 6(b)).
The RV for each of
these variables was 0.15(0.01–0.40)mm for the
diameter and 1.7(0.1–5.2)% for FMD,
respectively. CV was 4.5% for the
diameter measurements.Correlation between repeated measurements in 25 healthy subjects studied
twice with a delay of 24 hours. (a) Resting diameter; (b) FMD. The interclass
correlation coefficients areICC=0.85 and ICC=0.63, respectively.
(a)(b)


#### 3.1.3. Reproducibility

(i)
Diameter:65 frames were
analyzed twice using the software. The mean arterial diameter was 2.55±0.05mm and 2.54±0.05mm,
respectively. The intraclass coefficient across the two measurements was ICC=0.998 (P<.0001). The coefficient of variation for repeated measures
of arterial diameter was 0.8(0.0–3.5)%, and the mean
range of variation between the two sets of measurements was 28(0–143)μm. The bias
across the two sets of measurements was B=7μm with a
standard deviation of the differences of 39μm. The Bland
and Altman plot is presented in Figure 8(a).(ii)
FMD: when 65 FMD studies were analyzed
twice, the intraclass coefficient was ICC=0.969(P<.0001), and the mean range of variation between the two sets
of measurements was RV=0.6(0.2–3.5)% (Figure 7(a)).
The bias across the two sets of measurements was 0.3% with a standard
deviation of the differences of 0.9%. The Bland and Altman plot is presented in Figure
8(b). When all subjects were considered, the coefficient of variation for FMD
was 16%. The coefficient of variation in healthy volunteers (FMD=6.0±0.8%) was 8.3%. FMD was significantly higher in healthy volunteers
compared to the other groups (smokers: 4.9±1.9; hypertensive: 4.5±0.7%; coronary artery disease: 1.7±0.6%; heart failure: 2.9±1.4%, P<.05 among groups).(iii)
IMT: sixty IMT images were analyzed
twice. The intraclass coefficient for these measurements was ICC=0.99, (P<.0001, Figure 7(b)), and the mean range of variation between
the two sets of measurements was 0.03mm; the bias
across the two sets of measurements was 0.017mm with the SD
of the differences 0.037mm. The
coefficient of variation was 3.6%. These parameters of reproducibility compare
favorably with those of previously published methods [20]. IMT was significantly
lower in healthy volunteers compared to coronary artery disease
patients (coronary artery disease: 0.8370.27 mm; healthy: 0.5060.11 mm, P<.00001).Repeatability. (a) FMD; (b) IMT.
(a)(b)Bland and Altman plots of repeated
measurements. The horizontal axis reports the average of two consecutive
measurements, the vertical axis their difference. The dotted lines represent1.96 standard
deviation from the mean. (a) Diameter; (b) FMD.
(a)(b)


## 3.1. Performance Analysis

To characterize
the system performance, the following descriptors were used.(i)
Intraclass
correlation coefficient (ICC);(ii)
coefficient of
variation (CV). It is a measure of the dispersion of a probability
distribution and it is calculated as the standard deviation (SD) of repeated
measurements divided by their mean:(8)CV=100×σμ,where σ and μ are the
standard deviation and the mean of the measurement distribution, respectively;(iii)
range of
variation (R). It is calculated for repeatability and
reproducibility studies as the mean of the absolute differences between
repeated measurements;(iv)
bias (B) across separate measurements. Bias is given by the
mean of these differences. Bland and
Altman plots [23] were
derived. In all cases, analysis was performed in a randomized, blinded fashion.
Statistical analysis for the reproducibility studies was performed as
recommended by published guidelines [24] using Statview (SAS
Institute, NC, USA). FMD was calculated as in (7).  Besides the patient
movement, there are two potential causes of variability in the measurements.
The first one is the positioning of the observation window (ROI), and the
second is the manual positioning of the tags setting the units. The following
sections provide the characterization of the system.


### 3.1.1. Accuracy

In order to
assess the accuracy,100 frames were
analyzed both manually and using the new software. The two sets of measurements
are highly correlated, as illustrated in Figure 5(a). The intraclass
coefficient was ICC=0.97 (P<.0001). The range of variation was equal to RV=76(2–220)μm for arteries
with an average diameter of 2.35mm. The Bland
and Altman plot is shown in Figure 5(b).FMD analysis. (a) Correlation between manual (gold
standard, horizontal axis) and automatic (vertical axis) analyses; (b) Bland and Altman plot: difference between software
and manual measurements versus their average.
(a)(b)


### 3.1.2. Repeatability

The intraclass
coefficient for diameter measurements repeated with a delay of24 hours on the
same 25 subjects was ICC=0.85 (Figure 6(a)).
The intraclass correlation coefficient for repeated FMD measurements following
the same paradigm was ICC=0.63 (Figure 6(b)).
The RV for each of
these variables was 0.15(0.01–0.40)mm for the
diameter and 1.7(0.1–5.2)% for FMD,
respectively. CV was 4.5% for the
diameter measurements.Correlation between repeated measurements in 25 healthy subjects studied
twice with a delay of 24 hours. (a) Resting diameter; (b) FMD. The interclass
correlation coefficients areICC=0.85 and ICC=0.63, respectively.
(a)(b)


### 3.1.3. Reproducibility

(i)
Diameter:65 frames were
analyzed twice using the software. The mean arterial diameter was 2.55±0.05mm and 2.54±0.05mm,
respectively. The intraclass coefficient across the two measurements was ICC=0.998 (P<.0001). The coefficient of variation for repeated measures
of arterial diameter was 0.8(0.0–3.5)%, and the mean
range of variation between the two sets of measurements was 28(0–143)μm. The bias
across the two sets of measurements was B=7μm with a
standard deviation of the differences of 39μm. The Bland
and Altman plot is presented in Figure 8(a).(ii)
FMD: when 65 FMD studies were analyzed
twice, the intraclass coefficient was ICC=0.969(P<.0001), and the mean range of variation between the two sets
of measurements was RV=0.6(0.2–3.5)% (Figure 7(a)).
The bias across the two sets of measurements was 0.3% with a standard
deviation of the differences of 0.9%. The Bland and Altman plot is presented in Figure
8(b). When all subjects were considered, the coefficient of variation for FMD
was 16%. The coefficient of variation in healthy volunteers (FMD=6.0±0.8%) was 8.3%. FMD was significantly higher in healthy volunteers
compared to the other groups (smokers: 4.9±1.9; hypertensive: 4.5±0.7%; coronary artery disease: 1.7±0.6%; heart failure: 2.9±1.4%, P<.05 among groups).(iii)
IMT: sixty IMT images were analyzed
twice. The intraclass coefficient for these measurements was ICC=0.99, (P<.0001, Figure 7(b)), and the mean range of variation between
the two sets of measurements was 0.03mm; the bias
across the two sets of measurements was 0.017mm with the SD
of the differences 0.037mm. The
coefficient of variation was 3.6%. These parameters of reproducibility compare
favorably with those of previously published methods [20]. IMT was significantly
lower in healthy volunteers compared to coronary artery disease
patients (coronary artery disease: 0.8370.27 mm; healthy: 0.5060.11 mm, P<.00001).Repeatability. (a) FMD; (b) IMT.
(a)(b)Bland and Altman plots of repeated
measurements. The horizontal axis reports the average of two consecutive
measurements, the vertical axis their difference. The dotted lines represent1.96 standard
deviation from the mean. (a) Diameter; (b) FMD.
(a)(b)


## 3.1.1. Accuracy

In order to
assess the accuracy,100 frames were
analyzed both manually and using the new software. The two sets of measurements
are highly correlated, as illustrated in Figure 5(a). The intraclass
coefficient was ICC=0.97 (P<.0001). The range of variation was equal to RV=76(2–220)μm for arteries
with an average diameter of 2.35mm. The Bland
and Altman plot is shown in Figure 5(b).FMD analysis. (a) Correlation between manual (gold
standard, horizontal axis) and automatic (vertical axis) analyses; (b) Bland and Altman plot: difference between software
and manual measurements versus their average.
(a)(b)


## 3.1.2. Repeatability

The intraclass
coefficient for diameter measurements repeated with a delay of24 hours on the
same 25 subjects was ICC=0.85 (Figure 6(a)).
The intraclass correlation coefficient for repeated FMD measurements following
the same paradigm was ICC=0.63 (Figure 6(b)).
The RV for each of
these variables was 0.15(0.01–0.40)mm for the
diameter and 1.7(0.1–5.2)% for FMD,
respectively. CV was 4.5% for the
diameter measurements.Correlation between repeated measurements in 25 healthy subjects studied
twice with a delay of 24 hours. (a) Resting diameter; (b) FMD. The interclass
correlation coefficients areICC=0.85 and ICC=0.63, respectively.
(a)(b)


## 3.1.3. Reproducibility

(i)
Diameter:65 frames were
analyzed twice using the software. The mean arterial diameter was 2.55±0.05mm and 2.54±0.05mm,
respectively. The intraclass coefficient across the two measurements was ICC=0.998 (P<.0001). The coefficient of variation for repeated measures
of arterial diameter was 0.8(0.0–3.5)%, and the mean
range of variation between the two sets of measurements was 28(0–143)μm. The bias
across the two sets of measurements was B=7μm with a
standard deviation of the differences of 39μm. The Bland
and Altman plot is presented in Figure 8(a).(ii)
FMD: when 65 FMD studies were analyzed
twice, the intraclass coefficient was ICC=0.969(P<.0001), and the mean range of variation between the two sets
of measurements was RV=0.6(0.2–3.5)% (Figure 7(a)).
The bias across the two sets of measurements was 0.3% with a standard
deviation of the differences of 0.9%. The Bland and Altman plot is presented in Figure
8(b). When all subjects were considered, the coefficient of variation for FMD
was 16%. The coefficient of variation in healthy volunteers (FMD=6.0±0.8%) was 8.3%. FMD was significantly higher in healthy volunteers
compared to the other groups (smokers: 4.9±1.9; hypertensive: 4.5±0.7%; coronary artery disease: 1.7±0.6%; heart failure: 2.9±1.4%, P<.05 among groups).(iii)
IMT: sixty IMT images were analyzed
twice. The intraclass coefficient for these measurements was ICC=0.99, (P<.0001, Figure 7(b)), and the mean range of variation between
the two sets of measurements was 0.03mm; the bias
across the two sets of measurements was 0.017mm with the SD
of the differences 0.037mm. The
coefficient of variation was 3.6%. These parameters of reproducibility compare
favorably with those of previously published methods [20]. IMT was significantly
lower in healthy volunteers compared to coronary artery disease
patients (coronary artery disease: 0.8370.27 mm; healthy: 0.5060.11 mm, P<.00001).Repeatability. (a) FMD; (b) IMT.
(a)(b)Bland and Altman plots of repeated
measurements. The horizontal axis reports the average of two consecutive
measurements, the vertical axis their difference. The dotted lines represent1.96 standard
deviation from the mean. (a) Diameter; (b) FMD.
(a)(b)


## 4. Summary and Conclusions

We describe a
simple, accurate, and highly reproducible edge-detection wall-tracking software
for the analysis of peripheral endothelial function and IMT. As compared to
manual and semiautomatic analyses [16], the proposed software
limits significantly measurement errors, reducing the sample size necessary for
studies and increasing the reliability of FMD measurements in clinical
practice. As well, as compared to previously presented software with the same
functions, the one presented here is more rapid and allows manual correction at
any point during the automatic analysis. In particular, the user friendliness
and rapidity of the analysis (an average of 60 seconds for a full analysis as
compared to other software requiring up to 10 minutes [12]) are particularly important characteristics when
planning to use this type of analysis in large population studies. Furthermore,
the software presented here has the unique advantage that it allows saving both
numerical results and a movie containing the echo-tracking analysis. This
provides researchers with the possibility of auditing results at any time,
which in our experience improves significantly the quality of the data and
facilitates interinstitution cooperation. Because in our system image
acquisition and analysis are digital, picture quality and resolution are
preserved as compared to other systems that require transfer of images from
digital to analog and back to digital.Other features of clinical relevance are as follows.(i)
Continuous
analysis of FMD movies. The proposed system enables the continuous analysis of
FMD movies. In the considered setup, the duration of a typical FMD movie is 10
minutes: 1 minute baseline diameter recording, 4.5 minutes cuff inflation, and
4.5 minutes cuff deflation. The software presented here performs arterial
diameter measurement throughout these 10 minutes in order to construct a
continuous FMD curve. Studies have shown that, as compared to healthy subjects,
the FMD response in patients with cardiovascular disease is both lower and
delayed in time [25].
Therefore, this time shift should be considered an important component in the
characterization of the endothelial dysfunction and the importance of the detection
of true peak responses (and of the measurement of the time delay between cuff
deflation and peak diameter) should be emphasized. The software proposed here
calculates the FMD, the time lag, and the slope between cuff deflation and peak
arterial diameter, providing both spatial and temporal parameters
characterizing the endothelial function.(ii)
Improved
reproducibility. Previous reproducibility studies have reported that observer
error accounts for as much as 60% of the within subject variation for both FMD
and IMT measurements [6]. As compared to manual analysis, automatic
edge-detection is not affected by interobserver variability, which allows a
substantial reduction in the number of subjects necessary for clinical studies
under the assumption that this is the sole factor limiting the reproducibility.
However, it has to be recognized that US resolution, artefacts, and patient
movements are the major sources of variation in FMD
studies. The range of variation for repeated measures performed using modern
software, including the one presented here, approximates the maximum reachable
with currently available US technologies. Use of a stereotactic clamp with
micrometric movements for the ultrasound probe, ECG triggering as well as
optimal immobilization of the arm, a quiet and temperature-controlled
environment, and a fasting state remain of great importance. In our laboratory,
the radial artery is preferred over the brachial because it can be more solidly
immobilized, minimizing movement artefacts. While having a comparable FMD (6-7%
in healthy volunteers), the smaller diameter of the radial artery represents a
challenge for the type of software presented here.(iii)
Online versus
offline analyses. Previously published software allows online image analysis of
vessel diameter, which has the advantage of reducing study times [11]. In contrast, our offline
technique requires vessel images to be stored digitally for later analysis.
While this prolongs analysis time (by 60–90 seconds in
average), the advantage of offline analysis is portability (e.g., stored images
can be analyzed at any time in randomized batches). As well, an online analysis
system implies that the region of interest is selected at the beginning of the
study, which is limited by the unpredictability of artifacts. The first window
of our software gives a reference frame obtained from the whole 10 minutes of
registration, allowing to choose the most artefact-free region of interest.
Furthermore, our software is designed to allow interrupting and editing the
analysis (including changing the ROI) in the case of patient movement. This
source of artifacts is an important limitation of online and previous offline
analysis methods [26].
The possibility of such “interactivity” dramatically improves the
robustness (i.e., the ability to provide results without failures due to movement
artifacts) of the analysis as recently shown in [11]. Both online and offline
systems have relative advantages and disadvantages. Our opinion is that a rapid
offline analysis that allows operator interaction is an alternative to online
analysis facilitating and speeding up both data collection and analysis.
Overall, we believe that the proposed solution will
trigger new interest in the field because of its low cost, portability,
interactivity, and user friendliness.



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*Source: 738545-2009-04-06.xml*