Assessment of ventricular mechanical dyssynchrony by shortaxis MRI.
ABSTRACT Nowadays, patients with symptomatic heart failure and intraventricular conduction delay can be treated with a cardiac resynchronization therapy. Electrical dyssynchrony is typically adopted to represent myocardial dyssynchrony, to be compensated by cardiac resynchronization therapy. One third of the patients, however, does not respond to the therapy. Therefore, imaging modalities aimed at the mechanical dyssynchrony estimation have been recently proposed to improve patient selection criteria. This paper presents a novel fullyautomated method for regional mechanical leftventricular dyssynchrony quantification in shortaxis magnetic resonance imaging. The endocardial movement is described by timedisplacement curves with respect to an automaticallydetermined reference point. These curves are analyzed for the estimation of the regional contraction timings. Four methods are proposed and tested for the contraction timing estimation. They were evaluated in two groups of subjects with and without left bundle branch block. The standard deviation of the contraction timings showed a significant increase for left bundle branch block patients with all the methods. However, a novel method based on phase spectrum analysis shows a better specificity and sensitivity. This method may therefore provide a valuable prognostic indicator for heart failure patients with dyssynchronous ventricular contraction, adding new possibilities for regional timing analysis.

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ABSTRACT: We develop a brainguided approach to autonomous adaptive systems (AAS). This is especially to determine the various components of information processing an AAS need possess in order to be able to be flexible and adaptive in new environments and to be able to possess values of objects in the world and actions on them. These components are based on the faculties of perception and categorization, of attention, of object and action valuations and of longterm memory. Further high level executive processes are assumed to be available to an AAS. This is so that it can be effective in its environment in terms of its guidance, through learnt valuations, of decisions to be made of actions on that environment. Finally we raise ethical questions about the value of such autonomy to modern society and the problems it may cause.Neural Networks (IJCNN), The 2010 International Joint Conference on; 08/2010
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Assessment of ventricular mechanical dyssynchrony by shortaxis MRI
M. Mischi, Member, IEEE, H.C.M. v.d. Bosch, A.H.M. Jansen, R.M. Aarts, Fellow, IEEE, and H.H.M. Korsten
Abstract—Nowadays, patients with symptomatic heart fail
ure and intraventricular conduction delay can be treated with
a cardiac resynchronization therapy. Electrical dyssynchrony is
typically adopted to represent myocardial dyssynchrony, to be
compensated by cardiac resynchronization therapy. One third
of the patients, however, does not respond to the therapy. There
fore, imaging modalities aimed at the mechanical dyssynchrony
estimation have been recently proposed to improve patient
selection criteria. This paper presents a novel fullyautomated
method for regional mechanical leftventricular dyssynchrony
quantification in shortaxis magnetic resonance imaging. The
endocardial movement is described by timedisplacement curves
with respect to an automaticallydetermined reference point.
These curves are analyzed for the estimation of the regional
contraction timings. Four methods are proposed and tested for
the contraction timing estimation. They were evaluated in two
groups of subjects with and without left bundle branch block.
The standard deviation of the contraction timings showed a
significant increase for left bundle branch block patients with all
the methods. However, a novel method based on phase spectrum
analysis shows a better specificity and sensitivity. This method
may therefore provide a valuable prognostic indicator for heart
failure patients with dyssynchronous ventricular contraction,
adding new possibilities for regional timing analysis.
I. INTRODUCTION
Almost two million people in the United States suffers
from conduction system disease, i.e., the electrical depo
larization signal that induces the muscle contraction is not
properly conducted over the myocardium [1]. This problem,
which is typically characterized by a prolongation of the QRS
complex in the electrocardiographic measurement, leads to
a dyssynchronous contraction of the left ventricle (LV). The
major effects are a reduction of the cardiac efficiency and
ejection fraction, which results in a progressive deterioration
of the LV function.
Cardiac resynchronization therapy (CRT) is an established
therapy for patients with symptomatic heart failure and
prolonged QRS duration [2]. Nevertheless, not all patients
respond to CRT and LV reverse remodeling is appreciated in
slightly more than 50 % of patients [3]. This lack of response
is typically attributed to inappropriate patient selection and,
in particular, to the poor predictive value of QRS prolun
gation [4], [5]. Several studies on mechanical dyssynchrony
M. Mischi is with the Eindhoven University of Technology, the Nether
lands.
H.C.M. v.d. Bosch and A.H.M. Jansen are with the Depts of Radiology
and Cardiology of the Catharina Hospital in Eindhoven, the Netherlands.
R.M. Aarts is with Philips Research in Eindhoven and with the Eindhoven
University of Technology, the Netherlands.
H.H.M. Korsten is with the Catharina Hospital in Eindhoven and with
the Eindhoven University of Technology, the Netherlands.
suggest that mechanical dyssynchrony is a better predictor
for CRT response than electrical dyssynchrony [6], [7].
Mechanical dyssynchrony can be assessed using imaging
modalities based on magnetic resonance imaging (MRI) or
ultrasound echoDoppler. In general, myocardial velocity
timing can be evaluated by ultrasound tissue doppler imaging
(TDI) [6], [7] and myocardial strain timing can be evaluated
by MRI tagging [8]. However, TDI is limited by the acoustic
windows through the ribs and the anisotropic sensitivity
of the method (Doppler frequency shifts only occur for
longitudinal motion), while MRI tagging requires extensive
computation and shows a low sensitivity for reduced wall
thickening (hypokinetic ventricle).
This paper presents a novel method for regional mechani
cal LV dyssynchrony quantification based on image analysis.
The method is fully automated. Shortaxis MRI cines are
analyzed by an algorithm that is developed on the radial
framework of the image segmentation approach proposed
in [9], where the endocardium was detected by highpass
filtering and thresholding along a beam of rays originating
in the ventricular center, referred to as seed point. The seed
point was manually determined. The threshold was defined
either manually or on the basis of the image histogram.
The proposed segmentation algorithm is based on dynamic
morphology and radial multiscale analysis of the LV basal
view [10], [11]. This results in a substantial advantage with
respect to the method in [9] and several other methods
proposed in the literature, as no user interaction or threshold
definition is required.
MRI is chosen for the high contrast along the endocar
dium, which results in an increased segmentation robustness.
A short axis basal view permits to assess the synchronicity
of the basal segments, which is reported to be of prognostic
value in CRT [5], [6]. This allows reducing the complexity
of the diagnostic system from three to two dimensions.
In this study, segmentation is only a preprocessing step
for the dyssynchrony analysis. Based on the endocardial
tracking, the regional endocardial timedisplacement curves
(TDCs) are determined. Several methods can then be im
plemented to assess the contraction dyssynchrony along the
endocardium. To this end, we have adapted and implemented
the systolic time method [5], [12] and the inner product
method [13], [14]. In the latter, the entire cardiac cycle is
used to the determine the local contraction timing. We also
propose two alternative methods that are based on the cross
correlation of the entire TDCs.
The developed dyssynchrony analysis was validated on
two groups of patients with and without left bundle branch
Proceedings of the 29th Annual International
Conference of the IEEE EMBS
Cité Internationale, Lyon, France
August 2326, 2007.
SaD03.5
1424407885/07/$20.00 ©2007 IEEE 6011
Page 2
(a) Motion
(b) Tracked contour
Fig. 1.
in the motion image (a) used for detecting and tracking the endocardium (b)
is circumscribed by inner and outer borders on a beam of rays originating
in the ventricular center, i.e., the seed point. For illustrative purpose the
number of displayed rays is limited to 8.
A shortaxis basal view of the left ventricle. The region of interest
block (LBBB). All the methods revealed a significant in
crease of the standard deviation of the contraction timings
as a LBBB was present. However, one of the proposed
cross correlation methods showed a better sensitivity and
specificity for LBBB diagnosis.
II. METHODOLOGY
A. MRI cine acquisition and regularization
The MRI cines for the dyssynchrony analysis were ac
quired at high temporal resolution (one hundredth of the
cardiac period) on a 1.5 T MRI scanner (Gyroscan In
tera, Philips Medical Systems) equipped with a fiveelement
phased array coil. A balanced steadystate free precession
pulse sequence was adopted. Breath hold was used to avoid
motion artifacts. Going from base to apex, the first slice
that did not show the mitral valve structures was chosen for
the dyssynchrony analysis (see Fig. 1). In order to avoid
contour blurring, image regularization was performed by
combination of morphological properclosing and proper
opening operators with a nine element kernel [15].
B. Contour detection
The detection of the endocardial contour in the selected
MRI slice is fully automatic. The standard deviation of the
pixel intensities during the cine is analyzed and a binary
image generated by applying the threshold proposed by Otsu
[16]. The resulting binary image facilitates the automatic
detection of a region for the LV endocardium search, as this
is the structure that shows the most of the movement and,
therefore, the largest graylevel variations.
For the detection of the endocardial region, the Hough
Transform (HT) is employed [17]. The HT is a method that
tests the foreground pixels in a binary image against a defined
curve equation, which in our case is a circle. In fact, the
LV motion in a short axis view has a circular symmetry.
The 21HT implementation is adopted due to its efficient
computation [18]. The results are the seed point coordinates
and the average radius of the endocardial motion circle.
Based on the 21HT results, a region of interest (ROI) for
the endocardium search is defined as the endocardial range of
motion, i.e., the inner and outer borders of the endocardium
in the binary motion image (see Fig. 1). The border coordi
nates are defined along the rays originating in the seed point.
Possible outliers, e.g., due to the papillary structures, are
corrected by means of a continuity condition on the border
derivative in polar coordinates, which is bounded within the
values assumed in case the seed point is located on the border
itself (worst condition). Once the borders are defined, the
seed point is relocated in the center of mass of the inner
border to increase the localization accuracy of the following
analysis. The defined ROI is expanded (20 % inwards and
outwards) before the contour tracking.
C. Contour tracking
Once a ROI for the contour detection in the original cine
is defined, the endocardium could be tracked along the rays
originating from the seed point by means of a highpass filter
and a threshold [9]. However, as the definition of a threshold
is always critical, here we propose the replacement of the
radial filter in [9] by a more elaborated algorithm, which
does not require any threshold.
The multiscale analysis for edge detection proposed by
Canny et al. [11] is modified and adapted to the proposed
application [10]. A multiscale analysis with a Gaussian deriv
ative mother wavelet is performed on each ray originating
from the seed point with an angular resolution of 5 degrees
and on each frame of the MRI cine. For each ray the
multiscale analysis produces a scalogram W(n,s) where n
is the distance (in pixels) from the seed point and s is the
scale. A linear function of n is used to weigh W(n,s) and
enhance the values at shorter distance from the seed point.
This is performed to reduce the sensitivity of the algorithm
to fatty structures at larger distance around the heart.
The edges (with negative slope) for each scale correspond
to the (local) maxima of W(n,s) along a ray. Without the
need for a threshold, the location ? n along a ray of the
? n = (n : µ(n) > µ(m),∀n ?= m),
s ∈ S of the normalized W(n,s), i.e.,
µ(n) = min
S
dominant edge, i.e., the endocardium, can be defined as
(1)
where µ(n) is defined as the minimal value for all the scales
?
W(n,s)
?
max
N
[W(n,s)]
?−1?
.
(2)
The solution of (1) results in an efficient compromise be
tween localization accuracy and noise sensitivity.
In order to increase the robustness of the system, a
temporal continuity constraint is integrated in the system by
calculating µ?(n) over the output scales of three subsequent
frames together. The temporal continuity condition results
from the use of high temporal resolution MRI scans.
D. Dyssynchrony analysis
The result of the segmentation algorithm is a function
r(θ,k), which defines the edge distance from the seed
point along a θ degree ray at the frame k. Dyssynchrony
estimations result from the analysis of the TDCs r(θ,k) for
different rays.
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To improve the accuracy of the timing analysis, the TDC
time resolution is doubled by linear upsampling. The TDCs,
which include both systole (contraction) and diastole (ex
pansion), may resemble one period of a sinusoidal function.
When no conduction system dysfunction is present, the
ventricle contracts synchronously along its basal segments
and r(θ,k) shows a similar behavior in time for each angle.
Instead, when a conduction system block is present, the
TDCs for different rays are dyssynchronous.
Most segmentationbased methods for dyssynchrony eval
uation are based on the estimation of the systolic time [12].
This approach might be more sensitive to noise as it focuses
on one moment of the cardiac cycle (end systole). The
analysis of the complete cardiac cycle was also proposed
to compare lateral to septal phase differences [13], [14].
Under the assumption of a sinusoidal TDC, the mutual phase
difference was defined as the arccosine of the inner product
between the lateral and septal TDCs [14]. Another approach
used the inner product with a reference sine and cosine
curve (one cycle) to derive an absolute phase delay [13].
These methods are however based on the assumption of
a sinusoidal TDC and they are therefore sensitive to TDC
shape variations.
For a complete validation of the proposed methods, both
the systolic time and the inner product approaches are
adapted and integrated in our MRI shortaxisview analysis.
The systolic time is defined as the time to the minimum
value of the TDC. The inner product method provides a
relative time delay (fraction of the cardiac period) between
two TDCs corresponding to the angles θi and θj that is
given as arccos(< r(θi,k),r(θj,k) >)/2π, where < .,. >
represents the inner product. This delay is estimated for all
the TDCs with respect to the TDC with the largest SNR.
Here we also propose the use cross correlation methods
for the assessment of the mutual delays between TDCs from
different angles. The same reference defined for the inner
product method is also adopted for the cross correlation
method and the regional contraction timing for each angle
is defined as the time at which the cross correlation with
the reference TDC shows its maximum. Typically 72 rays
are employed to span the entire circle. Figure 2 shows a
regional contraction timing plot for two subjects with and
without a LBBB. The contraction timing in the presence of
a LBBB shows increased variations.
The time resolution of this cross correlation method is
limited by the sampling period of the signals. This problem
can be overcome by phase spectrum analysis. If a Discrete
Fourier Transform of two TDCs r(θi,k) and r(θj,k) is
performed, the difference of the phase spectra results ap
proximately in a line. This is due to the fact that r(θi,k)
and r(θj,k) are time translations of the same curve. If the
slope of the phase difference line is determined, then the
delay between r(θi,k) and r(θj,k) can be estimated with
no time resolution constraints.
The phase analysis is performed after the cross correlation
realignment. As a result, the phase difference is bounded
between −π and π with no phase wrapping issues. Ob
Fig. 2.
without (dashed) LBBB.
The regional contraction timings for two subjects with (solid) and
viously, the assumption of a pure temporal translation is
not completely fulfilled, and the phase difference line may
show a poor SNR. A least square error method is there
fore employed for the interpolation of the phase difference
line, whose slope can be estimated by a weighted linear
regression. The weights for the linear regression are derived
by the analysis of the amplitude of the frequency spectrum
[19]. They are determined as the minimum value between
the amplitudes of the spectra of the two TDCs. As a result,
the higher energy components, which typically represent the
myocardial movement, provide a larger contribution to the
regression line determination.
III. RESULTS
Ten subjects (five normals and five with LBBB) were
tested for quantification of the regional contraction timings.
LBBB was diagnosed by analysis of the QRS duration.
The performance of the proposed segmentation method was
validated on four evenly distributed image frames per cardiac
cycle. A total number of 40 images was evaluated by
measuring the percent area difference between manually
and automatically delineated endocardial boundaries. The
area was defined as the surface that was delimited by the
delineated boundary and the absolute area difference (error)
was defined as the area that was covered by only one of the
two delineations. The manual segmentation was taken as the
reference method since no gold standard for segmentation
of cardiac images exist. The results show a correlation
coefficient between the two methods R = 0.99. Bland
Altman [20] analysis resulted in an average area error equal
to 9.8 % while the corresponding standard deviation was less
than 3.8 %. Therefore, the segmentation algorithm is suitable
for the following cardiac dyssynchrony analysis [9].
The validation of the dyssynchrony analysis was per
formed by estimating the standard deviation of the regional
contraction timing assessed by the systolic time, inner prod
uct, cross correlation, and phase difference method for each
subject. In Table I the population averages and standard
deviations for the four indicators are given. All the values
are reported as a fraction of the cardiac cycle. According
to the results, separable ranges can be defined by each
method for either population (with and without LBBB).
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TABLE I
MEAN AND STANDARD DEVIATION OF THE FOUR INDICATORS.
Method
Systolic time
Inner product
Cross correlation
Phase difference
LBBB
0.1500 ± 0.0365
0.1178 ± 0.0436
0.1422 ± 0.0317
0.1468 ± 0.0191
Normals
0.0316 ± 0.0121
0.0436 ± 0.0275
0.0246 ± 0.0220
0.0268 ± 0.0171
TABLE II
SPECIFICITY AND SENSITIVITY OF THE FOUR INDICATORS.
Method
Systolic time
Inner product
Cross correlation
Phase difference
Specificity
99.71 %
90.68 %
98.89 %
99.96 %
Sensitivity
99.00 %
89.28 %
98.30 %
99.95 %
The evaluation of a diagnostic method is typically based on
sensitivity and specificity analysis. To this end, a threshold
must be defined that is used to decide whether a patient
has a LBBB. Assuming our statistical samples to be well
represented by Gaussian distributions defined by the means
and standard deviations in Table I, inference by Bayes rule
can be used to obtain the posterior class probability for each
class (normals and LBBB) by each of the four methods. As
a result, a threshold can be derived such that the posterior
class probability is equal for the two classes. The thresholds
for the systolic time, inner product, cross correlation, and
phase difference method are 0.0650, 0.0800, 0.0749, and
0.0838, respectively. Based on these thresholds, specificity
and sensitivity can be calculated for each method as given
in Table II, where the phase difference method shows the
best results.
IV. DISCUSSION AND CONCLUSION
A novel method is presented that permits the automatic
evaluation of the intraventricular mechanical dyssynchrony
based on high timeresolution MRI cines of a LV short
axis basal view. The analysis is fully automatic and does
not require any user interaction.
It is known that the ventricular movement comprises a
twist about its long axis. This twist is however limited to
less than 5 degrees in the basal plane [21], and it is therefore
negligible for the presented analysis. Ventricular translations
due to respiration are also negligible since breathhold MRI
is performed.
Four algorithms have been implemented for the timing
analysis. They are based respectively on systolic time, inner
product, cross correlation, and phase difference analysis.
Validation on ten patients showed that all methods are
suitable for LBBB detection based on the standard deviation
of the estimated regional contraction timings. The analysis of
the entire cardiac cycle by phase difference analysis seems
however to be the most promising in terms of sensitivity and
specificity, although a more extensive validation is necessary.
The improved results obtained by the phase difference
against the inner product and cross correlation methods were
expected. In fact, the first one assumes the TDC to be fully
represented by a sinusoidal function while the second one
has a limited time resolution. Compared to the systolic time
method, the analysis of the complete cardiac cycle, also
including diastole, represents a valid alternative, which might
show interesting predictive value for the selection of CRT
responders.
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