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five points. The centroid and the standard deviation of the points about this
centroid were then computed.
3.3.8.3 Vessel Wall Plus Plaque Thickness Map and Its Standard Deviation
A transverse plane was used to slice the average surfaces of the carotid wall and
lumen at 1-mm intervals. The vertices of the resulting 2-D curves were paired
according to the symmetric correspondence algorithm [66]. The 3-D thickness map
was then computed as the difference between the carotid wall and lumen boundary,
which was composed of a stack of 2-D curves, each lying on a transverse plane
where the average surfaces of the wall and the lumen were sliced. As shown in Figure
3.13, the resulting thickness map was coded in gray and superimposed on the mean
vessel wall 3-D boundary. Figure 3.13 shows the vessel wall plus plaque thickness
84 3-D US Imaging of the Carotid Arteries
0.000 0.296 0.592 0.887 1.18 0.000 0.296 0.592 0.887 1.18
0.000 0.296 0.592 0.887 1.18 0.000 0.296 0.592 0.887 1.18
Standard deviation (mm) Standard deviation (mm)
Standard deviation (mm) Standard deviation (mm)
(c) (d)
(a) (b)
Figure 3.12 (a) Manually segmented carotid artery wall contours. (b) Mean carotid artery wall
surface obtained with five manual segmentations. The standard deviation at each location is coded
in gray and mapped onto the mean surface. (c) Manually segmented carotid artery lumen con-
tours. (d) Mean carotid artery lumen surface obtained with five manual segmentations. The stan-
dard deviation at each location is color coded and mapped onto the mean surface.
maps for apatient scanned at baseline and after a 3-month interval, during which he
was treated with 80 mg of atorvastatin.
3.3.8.4 3-D Maps of Carotid Wall Plus Plaque Thickness Change
To study carotid plaque change, we generated the vessel wall plus plaque thickness
map from 3-D US images of carotid arteries obtained at two different times (see
Figures 3.13 and 3.14). To calculate the change in the plaque thickness between the
imaging times, we first registered the vessel contours using a slightly modified ver-
sion of the iterative closest point (ICP) algorithm by Besl et al. [67]. Rather than
aligning the centroids of the two surfaces, as proposed in [67], we aligned the bifur-
cation apex of the carotid vessels. After registering the two thickness maps, we then
3.3 Measurement of Plaque Volume 85
0.000
Thickness (mm)
(a) (b)
2.20 4.41 6.61 0.000
Thickness (mm)
2.20 4.41 6.61 8.82
0.000
Thickness (mm)
2.28 4.55 6.83 0.000
Thickness (mm)
2.28 4.55 6.83 9.109.1
(c) (d)
Figure 3.13 (a) Mean carotid artery wall with thickness of vessel wall plus plaque is coded in gray
and mapped on its surface (obtained at baseline). (b) The thickness map in part (a) is shown
together with the lumen of the carotid artery. (c) Mean carotid artery wall with thickness of vessel
wall plus plaque is coded in gray and mapped on its surface (obtained after 3 months of 80 mg of
atorvastatin daily treatment). (d) The thickness map in part (c) is shown together with the lumen
of the carotid artery.
calculated the difference between them, coded it in gray, and mapped it onto a mean
3-D boundary that was obtained from the 3-D US image at baseline, as shown in
Figure 3.15.
3.3.9 Example Results
Figure 3.13 shows the results for a patient who was treated with 80 mg of
atorvastatin daily for 3 months. The results shown in Figure 3.13(a, b) were
obtained at baseline, and the results in Figure 3.13(c, d) were obtained after 3
months of treatment. The difference in plaque in the vessel wall plus plaque thick-
ness is also apparent in the flattened map of the carotid arteries shown in Figure
3.14. The difference in the vessel wall plus plaque thickness is shown in Figure 3.15,
which shows a pronounced regression on the left side of the CCA with a reduction of
plaque thickness of about 7 mm.
The calculation and the display of the distribution of carotid plaque and the dis-
tribution of plaque changewill help in the study ofthe natural progression of carotid
plaque; more importantly, it will help in the understanding of plaque regression in
response to therapy.
86 3-D US Imaging of the Carotid Arteries
0.000 2.28
Thickness (mm)
4.55
6.83
9.10
0.000 2.28
Thickness (mm)
4.55
6.83
9.10
(b)
(a)
Figure 3.14 The carotid arteries have been cut open and flattened and the thickness of vessel
wall plus plaque is coded in gray and mapped on its surface. (a) Results obtained at baseline. (b)
Results obtained after 3 months of 80 mg of atorvastatin daily treatment.
3.4 Trends and Future Developments
Three-dimensional ultrasound has already demonstrated its clear advantages in
obstetrics (e.g., imaging of the fetus), cardiology (e.g., imaging of the cardiac sys-
tem), and image guidance (e.g., imaging of the prostate during an interventional
procedure) [11, 26, 27]. Advances in 3-D US instrumentation and visualization soft-
ware have advanced sufficiently enough to allow for fast (real-time) 3-D acquisi-
tion, real-time reconstruction, and 3-D visualization with real-time image
manipulation. Technical advances continue to improve both 3-D US image acquisi-
tion and image analysis, promising to make it a routinely available tool on all
ultrasound machines.
The availability of improved 3-D US systems is allowing researchers to focus on
demonstrating its clinical utility. Although 3-D US has been shown to be useful in
imaging the plaques of the carotid arteries, this modality requires further develop-
ment in order for it to become a routine tool for quantifying the progression and
regression of carotid disease. The following four sections discuss the current trends
and needed developments in 3-D carotid ultrasound imaging.
3.4 Trends and Future Developments 87
−7.36 −3.68
Thickness change (mm)
0.000
3.68 7.36
(b)
(a)
Figure 3.15 (a) The thickness of the vessel wall plus plaque difference between the results
obtained at baseline and after atorvastatin treatment (maps shown in Figure 3.14). A negative
value indicates that a plaque thickness reduction was recorded in the second scanning session. (b)
The flattened map of the 3-D results shown in part (a).
3.4.1 The Need for Improved Free-Hand Scanning
Although free-hand scanning techniques are useful, they are still subject to potential
artifacts and inaccuracies. Further technical progress is needed in order for these
techniques to become standard tools for producing high-quality images of the
carotid arteries. Approaches for gating and techniques for ensuring that the carotid
arteries are imaged uniformly need improvement. Real-time 3-D US systems (using
2-D arrays) are useful in cardiology. However, further developments are needed in
order for them to produce 3-D images of the carotid arteries with an image quality
similar to thequality that is currently being produced byconventional 1-Darrays.
3.4.2 The Need for Improved Visualization Tools
Current 3-D US imaging systems use 3-D visualization tools. However, these tools
require not only complicated user interfaces but also special training. In order for
3-D carotid imaging to become widely accepted, we need to develop intuitive and
automated visualization tools to manipulate the 3-D image so that the user may
view the desired section of the carotid arteries using both the texture mapping and
volume rendering approaches. The production of volume-rendered images of the
carotid arteries and the acquisition of volume-rendered images of the plaque surface
require multiple parameters to be manipulated. Most notably, automated tech-
niques are needed to provide immediate optimal rendering without significant user
intervention.
3.4.3 Reduction in the Variability of Plaque Volume Measurement
Landry and Fenster [50] have demonstrated that the greatest source of variability in
measurement of plaque volume (using manual planimetry) is caused by the variabil-
ity of locating the initial and final slices. Outlining the plaque contributes to the vari-
ability; however, this contribution is small. Figure 3.16 shows a plot of five repeated
outlining sessions for one carotid plaque “slice” as well as the standard deviation in
determining the boundary. The mean standard deviation in the plaque outline is
only 0.28 mm. Thus, to reduce the measurement variability of the plaque volume,
we need to improve our strategies for measuring the disease burden.
3.4.4 ECG-Triggered Acquisition of 3-D Carotid US Images
Delcker et al. [49] have shown that ECG-triggered 3-D US acquisition with
free-hand scanning resulted in improved reliability of plaque volume measurements.
However, triggered acquisition prolongs the scanning time, introduces discomfort
for the patient, and adds complexity to the instrumentation. Nonetheless, this
approach is promising for improving the quality of the 3-D US images and reducing
the variability of plaque volume measurements.
88 3-D US Imaging of the Carotid Arteries
Acknowledgments
The authors acknowledge the financial support of the Canadian Institutes for
Health Research and the Ontario Research and Development Challenge Fund. The
first author holds a Canada Research Chair and acknowledges the support of the
Canada Research Chair Program.
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92 3-D US Imaging of the Carotid Arteries
CHAPTER 4
Quality Evaluation of Ultrasound Imaging
of the Carotid Artery
C. P. Loizou
4.1 Introduction
Ultrasound imaging is a powerful noninvasive diagnostic imaging modality in medi-
cine [1]. However, like all medical imaging modalities that exhibit various image
artifacts, ultrasound is subject to a locally correlated multiplicative noise, called
speckle, which degrades image quality and compromises diagnostic confidence [1].
For medical images, quality can be objectively defined in terms of performance in
clinically relevant tasks such as lesion detection and classification, where typical
tasks are the detection of an abnormality, the estimation of some parameters of
interest, or the combination of the above [2]. Most studies today have assessed the
equipment performance by testing diagnostic performance of multiple experts,
which also suffers from intraobserver and interobserver variability. Although this is
the most important method of assessing the results of image degradation, few stud-
ies have attempted to perform physical measurements of degradation [3]. This
chapter was also published in a longer paper in [4].
Image quality is important when evaluating or segmenting atherosclerotic
carotid plaques [5, 6] or the intima-media thickness (IMT) in the carotid artery [7],
where speckle obscures subtle details [8] in the image. Speckle, which is a multipli-
cative noise, is the major performance-limiting factor in visual lesion detection in
ultrasound imaging that makes the signal or lesion difficult to detect [3, 6–8]. In
recent studies [4, 8] we have shown that normalization and speckle reduction
improve the visual perception of the expert in the assessment of ultrasound imaging
of the carotid artery. Traditionally, suspected plaque formation is confirmed using
color blood flow imaging, where the types of the plaque were visually identified,
and the delineations of the plaque and IMT were made manually by medical experts
[6, 7].
To be able to design accurate and reliable quality metrics, it is necessary to
understand what quality means to the expert. An expert’s satisfaction when watch-
ing an image depends on many factors. One of the most important, of course, is
image content and material. Research in the area of image quality has shown that
the quality is dependent on many parameters, such as viewing distance, display size,
93