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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5835_Библиотеки_им_академика_М_И_Перельмана.pdf
the defuzzification procedure, which means that the fuzzy sets obtained by applying
each inference rule to the input data were joined through the add function; the out-
put of the system was then computed as the centroid of the resulting membership
function. The fuzzy inference rules were defined in such a way that the system out-
put (“Edges”) was high only for those pixels belonging to edges in the input image.
In all, 45 rules were written to guarantee edge detection even in low-contrast areas
of a noisy echocardiogram image.
From each image, the input circular gradient vector (CGV) was computed as the
derivatives between the current pixel p(x
i
, y
i
) and its eight neighborhood pixels in a
3×3 neighborhood. This input vector is weighted in the fuzzy system according to
the membership functions and the rules. The current pixel is then assigned the cate-
gory of “edge” or “no edge” based on the weight it received. After the edge detection
process, groupings were done using a distance transform from which the left ventri-
cle alone is delineated (Figure 10.13).
The central idea behind the use of a fuzzy system for edge detection is to avoid
interobserver and intraobserver variability in identifying the endocardial borders
and to automate the entire process. To illustrate the performance of the imple-
mented fuzzy system, this method was applied to a test dataset that contained apical
long-axis view sequences of varying quality and frame rates. It was found that the
approach copes well with the varying image quality and frame rates. The results
were also compared with existing edge detection algorithms, and we found that the
Sobel and Perwitt operator fails to detect edges in the low-contrast region, whereas
in case of the Canny operator, the pixels not belonging to the edges are also
included. But the proposed method detects the edges and hence the endocardial bor-
ders clearly even in low-contrast regions, as shown in Figure 10.14.
264 Echocardiography: A Tool for LVSD Identification
0
0.5
1
−250 −200 −150 −100 −50 0
0
50
Low
Med High
50
100
100
150
150
200
200
250
250
0
0.5
1
Edge
No edge
Output variable “Output 1”
Input variable “Input 1”
Figure 10.12 Fuzzy membership functions.
10.4.2 Quantification of Systolic Dysfunction
The left ventricle as whole is a nonrigid object whose shape changes periodically
over each cardiac cycle due to the global and local deformations during the pump-
ing of the heart. Previous studies of shape abnormalities have demonstrated that
changes in ventricular geometry are associated with worsened status and prognosis
[63, 64]. In 1998 Yettram et al. [65] proposed that the ratio of fiber to cross-fiber
stiffness of the myocardium can be deduced in patients from the pattern of volume
and shape changes during left ventricular filling. Left ventricle shape and shape
10.4 Left Ventricle Systolic Dysfunction Identification 265
(d) (e)
(a) (b) (c)
Figure 10.13 (a) Cropped image, (b) preprocessed image, (c) fuzzy edge detected image,
(d) after grouping, and (e) left ventricle.
Canny operator
Sobel operator
Raw image
Perwitt’s operator
Fuzzy system
Figure 10.14 Edge detection of echocardiogram.
changes have been studied extensively in large patients [66]. The ability to relate
ventricular shape or shape changes to underlying myocardial mechanics could be of
diagnostic value, because ventricular shape is easy to measure noninvasively and is
known not only to change in some disease states but also to predict clinical
prognosis [67].
LV geometry is an important determinant of the pumping performance of the
left ventricle. Better correlation was expected between a morphological index and
the degree of dysfunction, so a geometric index, the eccentricity ratio (ER), was pro-
posed in 2006 [68] and was calculated as follows:
()
ER
p
EI
LL
=×
=
−
Eccentricity index (EI)
Perimeter
100
1
2
2
2
1
L
where L
1
is the anterior to posterior length and L
2
is the septal to endocardial free
wall length in the apical view.
The ER describes the geometry of LV well even when the regional and valvular
dysfunction varies significantly between the two groups measured at end diastole
and end systole (p < 0.001%). In particular, the ER measured at end systole was
found to be low for the dysfunction group (3.142 ± 0.7974) when compared with
the other (5.48 ± 0.739, p < 0.0001). For patients having dysfunction, the systolic
phase exhibits an irregular variation in ER thatillustrates theimproper contraction.
10.4.3 Region of Dysfunction Identification
The assessment of regional left ventricular function is an essential component in the
evaluation of any patient with known or suspected heart disease. Although the title
of the very first description of echocardiography by Edler and Hertz in 1954 [69]
mentioned “recording of the movements of heart walls” as its goal, measuring wall
motion objectively and quantitatively has remained difficult in clinical practice. Sev-
eral methods have been developed to aid cardiologists in their clinical and research
studies on wall motion. Some methods directly analyze image data, according to the
optical flow concept [70], even if noise and temporal undersampling may limit accu-
racy. Other methods track markers that have been either surgically implanted in the
LV wall [71] or noninvasively assigned to it by magnetic resonance tagging [72].
The most common methods [73, 74] analyze the LV contours as extracted from
image sequences by means of manual or automated procedures. The use of contours
makes the analysis independent of the imaging modalities, provided that the particu-
lar 2-D projection from which the heart is viewed is taken into account. In most
cases, contour analysis considers two single contours taken at the end of the systolic
(ES) and diastolic (ED) phases of a representative cardiac cycle. In a few cases, a
complete contour sequence, spanning an entire cardiac cycle, is considered
(frame-to-frame analysis).
The most successful current technique, tissue Doppler-based analysis of motion
[75] and deformation [76], is hampered by the critical dependence of both measure
-
266 Echocardiography: A Tool for LVSD Identification
ments on the ultrasound beam direction, leading to signal dropout and velocity
underestimation. To provide an objective framework, a quantitative motion
description of wall segments is usually built by estimating the regional excursion of
LV wall motion, and seldom by considering velocity and asynchrony indices. In the
measurement of these parameters, geometric features of the LV, such as the LV long
axis or centroid at ED or ES, or the centerline lying between the ED and ES contours
[77], are usually exploited to establish a reference system for the correspondence of
points belonging tosubsequent frames. In recent years, new methods have been pro-
posed that consider not only LV motion but also LV contour shape, which is
independent from any reference system.
In diastole–systole analysis, a good discrimination capability of normal and
abnormal contours has been provided through curvature-based descriptions of digi-
tized contours [74]. In frame-to-frame analysis, some methods are based on a static
approach, as shape descriptions in single images are used for the tracking of contour
points or segments in an image sequence [78]; other computer vision methods are
based on a dynamic approach in that they exploit spatiotemporal models for esti-
mating nonrigid motion from 2-D contours [79, 80] or from 3-D data [81]. Another
issue concerns the interpretation of results. In diastole–systole analysis, regional
nonuniformity is commonly taken into account by assessing the variability range
for the motion excursion of a number N of wall segments, in a group of normal sub-
jects. Then, abnormalities are detected and evaluated by comparing N measured
values of the patient against the normal range and by using statistical decision tests.
The movement of any given ventricular segment is directly influenced by the adja-
cent segments, which means that any dysfunction can be masked by compensating
motion from a neighboring segment.
Regional wall motion analysis can also be based on the wall thickening rather
than endocardial motion. Wall thickening is insensitive to cardiac translation, is ori-
entation model independent, and produces a single parameter of regional function.
However, the thickness is difficult to measure on stop-frame 2-D images due to the
reduction in endocardial and epicardial definition resulting from the loss of tempo-
ral continuity and the loss in spatiotemporal information provided by the preceding
and succeeding frames [82]. Interpretation of the results of a frame-by-frame analy-
sis is more difficult, because both spatial and temporal nonuniformity must be
considered.
The qualitative evaluation of LV systolic function is based on the division of the
LV into a number of segments, after which each segment is scored as normal, hypo-
kinetic, akinetic, or dyskinetic. The main problems pertain to the distinction of
hypokinesia from akinesia. Akinesia was considered to be present whenendocardial
excursion is 2 mm, and hypokinesia with endocardial excursion is 5 mm. However,
movement may be passive, and thickening is the more reliable marker of
contractility.
The standard 16-segment model of the ASE (septal, lateral, anterior, and infe-
rior at the apex, with these segments as well as the anteroseptal and posterior seg-
ments at the base and midpapillary muscle level; Figure 10.15) is likely to remain in
widespread use because the suggested 17-segment model (which includes a true
apical segment) ignores the small but important detail that most echocardiograms
fail to identify the true apex of the heart and so this can be modified. With this, a
10.4 Left Ventricle Systolic Dysfunction Identification 267
score of 1 is given for normal regions; with scores of 2, 3, and 4 for hypokinesis,
akinesis, and dyskinesis, respectively; 5foraneurysm;and6and7forakinesisor
dyskinesis with thinning, respectively. The wall motion “score index” (obtained by
averaging the scores of individual segments) gives a semiquantitative index of
global systolic function, analogous to the ejection fraction and with similar prog-
nostic significance [83].
Regional wall motion scoring is highly reproducible within individual sites,
reflecting common reading styles. However, reproducibility of wall motion assess-
ment between centers may be quite limited, especially during stress 2-D echo. Con-
cordance may be improved with the use of standard reading criteria and harmonic
imaging. Although it is unlikely that echocardiographers will stop visually assessing
the LV, an objective measure that supplemented this assessment with objective crite-
ria could act as a “common language” to reduce variation between readers.
A number of echo and Doppler modalities are able to offer quantitation of
regional function, and most are outside the remit of this review (Table 10.1). The
2-D echo-based techniques include techniques for assessing radial displacement
(using the centerline method or color kinesis) or thickening (anatomical M-mode).
10.4.3.1 Centerline Method
The centerline method is based on three steps: tracing the LV end-diastolic and
end-systolic borders; superimposition of the traces with interpolation of the center-
line; and measurement of the excursion from this line in a series of chords perpendic-
ular to the centerline, which can be compared to a normal range of displacement.
Each step poses potential pitfalls.
Tracing of contours, preferably in two orthogonal planes (usually apical four-
and two-chamber views), is dependent on good quality border definition, and the
reliance on apical views may compromise edge detection because of the parallel ori-
entation of the echocardiographic beam with the endocardium.
268 Echocardiography: A Tool for LVSD Identification
Long axis view
Short axis view
4 chamber view 2 chamber view
LCx
LCx
LCx
LCx
LAD
LAD
LAD
LAD
LAD
Diag
RCA/LCx
RCA/LCx
RCA/LCx
RCA/LCx
1 = normal; 2 = hypokinetic; 3 = akinetic; 4 = dyskinetic
PDA
(RCA/LCx)
Figure 10.15 The 16-segment ASE model for characterization of regional LV function, and the
normal coronary artery distribution of the segments.
Although good border definition has become more available with the develop-
ment of harmonic and contrast imaging, tracing the edge may still involve an ele-
ment of guesswork. More than a single frame may need to be traced at both systole
and diastole, making the procedure time consuming, although automated and
semiautomated methods of tracking the wall have been developed (Figure 10.16).
The superimposition of systole and diastole may have a critical effect on the mea-
surement of excursion from the centerline. Either fixed or floating frames of refer-
ence can be used to compensate for rotational or translational movement of the
heart. Failure tocorrect for suchmovements may cause false positives, but the useof
correction may hinder the detection of milder abnormalities. Finally, different vari-
ations of the techniquemeasure thechords relative to the centerline or relative to the
center of LV mass.
10.4.3.2 Color Kinesis Method
The color kinesis method uses acoustic quantification to define the border, based on
the difference in backscatter between the LV wall and cavity. This has the benefit of
avoiding the onerous process of tracing the border in every frame, although the
frame rate is somewhat limited, compared to standard 2-D imaging. The excursion
of the myocardium from each frame to the next is filled with a different color, and
the resulting display overlaid on the 2-D image (“color kinesis”) [84]. The displace-
ment is portrayed as segmental area shrinkage and is arranged in stacked histo-
10.4 Left Ventricle Systolic Dysfunction Identification 269
Figure 10.16 Centerline approach to quantification of regional LV function [73].
Table 10.1 Alternative Techniques for the Quantification of Regional
LV Function
Radial Longitudinal
Displacement Center line (from 2-D echo)
Color kinesis
Annular M mode
Tissue tracking
Thickening Anatomical M mode
Integrated backscatter
Velocity Velocity from displacement
Longitudinal velocity
Tissue Doppler velocity
Strain
Timing Time to peak systole
Time to onset of diastole
Time to peak systole
Time to onset of diastole
grams, as shown in Figure 10.17, which can be compared to normal ranges. This
approach has been particularly applied during stress echo, where it correlates with
expert wall motion analysis and may be of value to less expert readers. This tech-
nique is heavily dependent on image quality, and appears to be more feasible with
the use of myocardial contrast for LV opacification. Measurements show a variation
of 10% to 20%. As with any technique that measures endocardial motion, this is
sensitive to extrinsic cardiac movement.
10.4.3.3 Anatomic M-Mode
Myocardial thickening is the optimal parameter for measurement because, unlike
excursion, it is independent of cardiac rotation or translation. However, the mea-
surement of thickening requires definition of both the endocardium and the
epicardium—and the latter can pose a problem in the apical views. M-mode ultra-
sound has conventionally been used for gathering wall thickening data, but has been
constrained by the angle dependence of standard M-mode imaging. Two-dimen-
sional images at high temporal and spatial resolution have been used to reconstruct
M-mode images in any plane (anatomic M-mode), although caution has to be
applied with angle corrections of 60° to 70°. The results correlate well with visual
assessments, but it has been difficult to designate a normal range, because of varia-
tions of baseline thickening, and the clinical benefit of this approach is not well
defined.
Cardiologists refer to qualitative properties, such as late/early and inward/out-
ward movements, rather than to numerical quantities as provided by most methods.
This is the main reason why computer-aided analysis is seldom performed frame by
frame. The model validation may have limitations due to the linearity assumption
about deformation, which is, however, satisfied for small segments. On the other
hand, validation with markers may also suffer from drawbacks, as the endocardial
surface exhibits movements different from those of the midwall or epicardium,
where markers are usually implanted. Nevertheless, both the radial method and the
equidivision matching were validated with implanted markers. The centerline
method has limited capabilities at basal regions, where the perpendicular chords do
270 Echocardiography: A Tool for LVSD Identification
Figure 10.17 Quantitation of radial function using color kinesis [73].
not always match the first and last contour points. In fact, disagreements between
different methods arise in borderline cases [85].
10.4.3.4 Radial Line Method
In Figure 10.18, radial lines have been drawn in each delineated LV from a reference
point toward the endocardium. From the variations in the length of these lines over
a cardiac cycle, the region of dysfunction can be identified. Sixteen lines (L1–L16)
were drawn from the center of the delineated LV toward the endocardium, and its
span of variation was measured between end of diastole and end of systole. If the
muscles are active the variations will be greater. The number of lines drawn
increases the efficiency of the diagnosis; normally, it is in between 8 and 24, with 32
being the maximum. This method was tested over two groups of subjects with and
without regional dysfunctions, as shown in Figure 10.19.
In Figure 10.19, we can observe the difference in contractility of LV in terms of
variation in radial line length between the two groups. In the dysfunctional group,
the span of the lines is reduced when compared with the other group that shows the
poor contractility of the muscles.
10.4 Left Ventricle Systolic Dysfunction Identification 271
Figure 10.18 Radial line method.
Frame no. Frame no.
0
24
6810
12 14
16 18
0
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0.20
0.22
0.24
0.26
0.28
Distance
24
6810
12 14
L16
L15
L14
L13
L12
L11
L10
L9
L8
L7
L6
L5
L4
L3
L2
L1
L16
L15
L14
L13
L12
L11
L10
L9
L8
L7
L6
L5
L4
L3
L2
L1
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0.20
0.22
Distance
(a) (b)
Figure 10.19 Radial line method: (a) normal (i.e., without dysfunction) and (b) with dysfunction.
10.4.3.5 Regional Area Method
In the regional area method, the LV is divided into different regional areas by draw-
ing the radial lines from a fixed reference point (Figure 10.20). Then the area of each
region was determined and normalized against the total endocardial length. The
change in this area of each segment is directly related to the muscle contractility.
Usually the number segments used for this analysis is 8 or 12.
The entire LV was divided in to eight segments (A1–A8) with respect to a fixed
reference point. Then the areas were calculated for each segment and normalized
against endocardial length, as shown for two cases in Figure 10.21. This figure
shows that the LV muscles of the dysfunctions are not active as that of the other
groups, because the spans have lesser heights of variation.
10.5 Real-Time 3-D Echocardiography
Routine clinical use of 3-D echocardiography has been hindered by the prolonged
and tedious nature of data acquisition. Image processing is not only time consuming
but also requires dedicated manpower to generate 3-D reconstructions and useful
quantitative data. Accordingly, the recent introduction of real-time 3-D
echocardiographic imaging techniques is of great interest since this methodology
may circumvent many of the previously mentioned limitations.
272 Echocardiography: A Tool for LVSD Identification
0
24
6810
12 14
16 18 20 0
24
6810
12 14
16
Frame no.X axis title
Area
0
A8
A8
A7
A7
A2
A2
A5
A5
A4
A4
A3
A3
A1
A1
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
Area
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
A6
A6
(a) (b)
Figure 10.21 Regional area method: (a) normal (i.e., without dysfunction) and (b) with dysfunc
-
tion.
Figure 10.20 Regional area method.
10.5.1 Sparse Array Transducer
Real-time 3-D imaging was initially performed using a sparse array matrix trans-
ducer (2.5 or 3.5 MHz), which consisted of 256 nonsimultaneously firing elements.
This transducer acquired a pyramidal volume dataset measuring 60°660° within a
single heartbeat. Echocardiographic images were then displayed online using simul-
taneous orthogonal (B-scan images) as well as twoto threeparallel short-axisplanes
(C-scans) [45, 46]. Similar to other 3-D methods, the sparse array transducer
resulted in accurate left ventricular volumes, ejection fraction, and mass measure-
ments when compared to gold standard techniques, such as MRI and RNA. The
sparse array transducer was also used advantageously during stress testing, because
all postexercise images were simultaneously acquired during a single heartbeat,
which was at higher peak stress heart rates compared to conventional stress tests.
Although 3-D real-time images were acquired using this novel transducer, it
continued to have several disadvantages that precluded its routine clinical use. The
ultrasound images were of relatively poor quality, frame rates were low, and the
pyramidal volume had a relatively narrow sector angle of 60°, which resulted in the
inability to accommodate larger ventricles. Moreover, the images obtained with this
system were not volume rendered online; instead, they consisted of computer-gener-
ated, 2-D cut planes derived from the 3-D volume dataset.
10.5.2 Full Matrix Array
Recently, a full matrix array transducer (X4, Phillips Medical Systems, Andover,
Massachusetts), which utilizes 3,000 elements in contrast to the 256 elements of the
sparse matrix array probe, has been developed. This new development in transducer
technology has resulted in: (1) improved sidelobe performance (contrast resolu-
tion), (2) higher sensitivity and penetration, and (3) harmonic capabilities that may
be used for both grayscale and contrast imaging. In addition, this transducer dis-
plays online 3-D volume rendered images and is also capable of displaying two
simultaneous orthogonal 2-D imaging planes (that is, biplane imaging).
10.5.3 Real-Time Volume Rendered 3-D Imaging
The full matrix array transducer has several modes of data acquisition (Figure
10.1): (1) narrow-angle acquisition, which consists of 60°630° pyramidal volumes
displayed in a volume rendered manner in real time without the need for respiratory
gating; (2) the “zoom mode,” which allows a magnified view of a subsection of the
pyramidal volume (30°630° sector in high resolution); and, last, (3) wide-angled
acquisition, which is used to collect the entire left ventricular volume. In this acqui-
sition mode, four wedges (15°660°) are obtained over eight consecutive cardiac
cycles during a breath hold with ECG gating. The first two modes of data acquisi-
tion are predominantly used to visualize cardiac and valvar morphology. Images of
heart valves acquired in this manner provide unique views, which are not always
readily obtainable using conventional 2-D echocardiography. In contrast, the
wide-angled acquisition mode is often used to acquire the entire left ventricular vol-
ume in order to perform detailed analysis of global and regional wall motion. This
matrix array is capable of acquiring data at three levels of image resolution during
10.5 Real-Time 3-D Echocardiography 273