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Vascular and Intravascular Imaging Trends, Analysis, and Challenges, Volume 1
https://t.me/medicina_free
Figure 11.5. Full system workflow diagram.
The IVUS images required processing to enhance the appearance of the vessel
lumen and to remove noise and labels. As a first step, a region of interest (ROI) was
defined to reduce the size of the image to only the IVUS output. To remove the
region defined by the catheter silhouette, appearing as a constant circle in the centre
of the IVUS images, each image was transformed first to polar coordinates. A mask
of minimum intensity (usually zero) pixels [21], of a width defined by the known
catheter diameter, was then applied to the upper part of each polar image to remove
this catheter artefact. The resulting image was then transferred back to Cartesian
coordinates. To remove the grid markings in the IVUS images, a 6 × 10
neighbourhood median filter was applied at each point (known in advance).
IVUS images tend to be distorted by both speckle noise and modality-specific
artefacts. To reduce speckle noise in the images, a global speckle reducing bilateral
filter (SRBF) [4] was used. This incorporates local noise statistics to provide a full
automated high-performance image despeckling and originates from classical
bilateral filtering that takes into account both space- and intensity-oriented
similarity in a specified region around the pixel. The filter combines both domain
filtering, using pixel weights that decay as the distance to the central pixel rise, and
range filtering, where pixel weights decrease when a dissimilarity-in-intensity
measure increases. SRBF has been shown to demonstrate better results in ultrasound
images, maintaining edge details, over other filters [36]. The filter is shown in the
equations below:
+
in
WstWstgst
(, ) (, )(, )
dr
+
in
WstWst
(, ) (, )
dr
(11.6)
fij
(, )
∑∑
=−+=−
snintn
=
11
∑∑
=−+=−
snintn
11
is every pixel value of the input image and the filtered pixel is
ij(, )
size of the window filter was w = 7 (an odd number for symmetry) and the span was
22
−+−
is jt() ()
−
Wst e(, )
Ws t e(, ) .
r
=
d
∥−∥
−
=
2
σ
2
d
gi j gs t(, ) (, )
2
σ
2
r
2
11-13
fij(, )
(11.7)
(11.8)
. The
1
Vascular and Intravascular Imaging Trends, Analysis, and Challenges, Volume 1
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n = 3(
=+wn2
index for range parameters.
). d is an index corresponding to domain parameters while r is an
and
2
are parameters (variances) that regulate the
σ
r
2
σ
d
weight behaviour in the domain and range fields [42]. The choice of these values
depends on the statistical features of noise; high values lead to edges loss while low
values create a weak filter that is unable to eliminate noise. In our implementation,
22
σσ==4
dr
. Finally, to increase the contrast of the IVUS images, contrast
stretching (histogram dynamic full range expansion) [32] was applied.
A number of IVUS segmentation methods have already been proposed and these
can be divided into four main strategies: (1) edge detection techniques, (2) active
contour methods, (3) statistical approaches and (4) multiscale analysis. Due to the
absence of peripheral tissues and blood flow in our phantom and the structural
homogeneity of the phantom vascular wall, a straightforward global thresholding
strategy was implemented. Otsu’s method [28] was chosen to distinguish between
vessel wall and noise components. The method assumes that the pixel set of every
image can be divided into two discrete classes: objects and background. The
classification is performed through an optimisation problem which aims to detect
an optimal threshold in the image histogram.
Finally, dilation and erosion were applied to the binary images. This was followed
by a connect-component labelling (region labelling) whereby a graph-based process
assigned a specific label to a set of pixels within a clearly bounded region. A two pass
process was applied. In the first step, initial labels were assigned to the objects and
label correspondences were recorded. In the second step, the algorithm substituted
each previously specified label for the smallest of the corresponding class. For each
region, a centre of mass was calculated and these values were used to estimate the
main ring of image energy; this allowed for the safe application of vessel-specific
geometrical constraints to further remove non-object (non-lumen) regions. First,
regions of size below a certain threshold were removed. Geometric constraints on
the size of the average human aorta were then used to remove regions that were too
far away from the geometric centroid of all the regions.
It should be noted that the appearance of the IVUS images collected from the
phantom currently does not resemble those from patients; however, in vivo data do
exhibit the same kind of speckle noise that we encountered. For that reason, while
the image processing steps presented here are not directly transferrable to in vivo
sequences, many of the steps can be used.
11.3.2.2 Landmark detection
The detection of landmarks is a key step in the proposed IVUS-based navigation
platform. In our platform, we cater for both anatomical and morphological
landmarks. Morphological landmarks are structures which describe a specific shape,
are present in all shapes of the same class, and are easily detected. Anatomical
landmarks along the aorta may be as suggested by the ACCF/AHA (American
College of Cardiology Foundation/American Heart Association) Task Force [17];
here the authors identified nine well-established anatomical landmarks to effectively
describe the anatomy of a normal aorta. These include: the aortic sinuses of
Valsalva, the sinotubular junction, the mid ascending aorta, the proximal aortic
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arch, the mid aortic arch, the proximal descending thoracic arch, the mid descending
aorta, the aorta at diaphragm and the abdominal aorta at the coeliac axis origin.
As the landmarks used in the proposed framework must be effective descriptors of
the vascular structures, some of these anatomical landmarks are unsuitable. For
example, the mid aortic arch only refers to a length of the descending aorta,
effectively a tube. For this reason, we only consider branches, being present in each
artery, as well as morphological variations in the aorta, dilations and stenoses,
representing the two main pathologies of the aorta, aneurysms and atherosclerotic
lesions.
To detect these landmarks, two ellipses were fitted to the inner wall edge and
outer boundary of the vessel in the processed IVUS frames. The ellipses were fitted
using the direct least squares fitting approach by Fitzgibbon et al [13]. The method
searches for an optimally fitted curve to a known set of scattered 2D data by
minimising the sum value of the calculated residuals of the given points to the curve.
A Cartesian quadratic representation of an ellipse was used:
2
+++++=ax bxycy dxeyf 0.
1
11211
1
(11.9)
In addition to fitting the ellipses, the main ellipse parameters were calculated: the
centre of each ellipse, semi-axis lengths and ellipse orientation. Subsequently, these
parameters form the basis of the description of vessel morphology.
The absolute lengths of the major and minor axes of the ellipses defined the area
covered by the ellipses and indicated the lumen area. Their relative lengths
(calculating the eccentricity, defined by the ratio of major to minor lengths) describe
the shape of the lumen. Finally, the absolute difference between the corresponding
axes of the inner and outer ellipses described wall thickness.
A combination of these descriptors can be used to indicate the presence of
landmarks along the aorta. It is challenging to identify certain landmarks in a single
IVUS image in isolation but examining these parameters along the vessel axial
dimension, i.e. along a series of IVUS images in a pullback sequence, it is possible to
interpret the parameter fluctuations.
An aneurysm would be characterised by an increase and then a decrease in the
inner or outer ellipse axes lengths. An atheromatic stenosis will be a decrease and
then an increase. The presence of a branch would be indicated by a localised increase
in the eccentricity of the inner ellipse.
11.3.2.3 3D IVUS–CT fusion
After detection of landmarks in the IVUS sequence, the landmarks must be matched
to the equivalent landmarks detected in 3D. For this, we used a preoperatively
obtained 3D surface mesh of the vessel. This was derived from a preoperative
rotational-CT scan of the phantom obtained using a GE Innova 4100 interventional
imaging suite. CT visible markers were attached to the phantom for registration
purposes. ITK-SNAP [48] was used to segment the vessel from the images and a 3D
surface mesh was produced.
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To define the mesh skeleton (vessel centrelines), a mesh thinning algorithm was
applied to the 3D surface mesh. Binvox [25, 27], followed by Thinvox [26, 29], was
used for this purpose. Binvox first converts the 3D surface mesh to a voxel model
while Thinvox is an iterative procedure that detects and removes boundary vessels
until a single voxel curve is left. By taking the centroid of each of the voxels, the
mesh skeleton was produced.
To find branch-points along the skeleton, all voxels along the initial voxel curve
were examined. Each voxel could have up to 26 neighbouring voxels; however, as we
are starting with a voxel curve, no voxel should have all 26 neighbours. Any voxel
with a neighbouring voxel will either (1) share a common side (four common
vertices), (2) share a common edge (two common vertices), or (3) share a single
common vertex. After examining all neighbours of each voxel, it was determined
that voxels with at least three neighbouring voxels were branch-points, i.e. were
voxel junctions, and voxels with only one neighbouring voxel were end-points.
Subsequently, after all end-points and branch-points were identified along the
vessel centreline, points along the centreline must be grouped, corresponding to each
of the junctions. K-means clustering was used, with k set to the ideal number of
junctions in the shape. After the lengths of centre points have been clustered
together, the central axis of the aorta was determined using a Dijkstra-based directed
path search algorithm. This sorted points within each junction and identified
erroneous branch- and end-points. The total final output path was the centreline
of the vessel, which was smoothed using a spline.
The final step was registration to the IVUS pullback sequence. For this, the start
and end of the IVUS image sequence were registered to the end-points. Branches
identified in the pullback were associated with the branch-points along the centreline. Images in between were linearly interpolated between these fixed points. As we
currently do not have information on the orientation of the tip of the catheter, the
IVUS images were oriented perpendicularly to the central axis.
11.3.2.4 Results
The image processing steps transforming each initial IVUS image to the final
centralised binary frame are presented in figure 11.6.
Figure 11.7 depicts the main branch detection procedure. The three peaks of the
inner ellipse eccentricity index plot along the vessel correspond to the three main
branches of the aortic arch. The detection of other lesions is based on tracking other
features of the fitted ellipses.
The calculated skeleton of the 3D CT model is presented in figure 11.8 along with
the discretised vascular volume as well as the result of the IVUS–CT fusion
procedure.
In figure 11.9 we present the results of our system in terms of navigation precision.
This diagram provides a comprehensive comparison of the catheter tip points
tracked by the EM system (red: ground truth) and the tip points calculated by our
method (green). The point cloud of the 3D model (yellow points) as well as its
smoothed central axis (blue curve) are also depicted. The absolute differences
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Figure 11.6. The processing stages transforming each raw image to the finally centralised frame.
Figure 11.7. Branch detection and localisation based on eccentricity index of the inner fitted ellipse. Left:
top—EI plot along the vascular structure; bottom—the three main branches of our aorta phantom. Right: the
three branch-corresponding binary IVUS frames before the centralisation step.
between the tip point coordinates provided by the EM system and those estimated
via our system are also given in the form of three supplementary boxplots.
The current implementation of the proposed programme is in MATLAB and can
be seen in figure 11.10.
11.4 The future of intravascular imaging for navigation
There still remain a number of challenges to be addressed in the use of intravascular
imaging for navigation and these remain the research priority areas for any potential
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Figure 11.8. From left to right: Voxelised CT-derived vessel model, central axis with end- and branch-points
and IVUS–CT data fusion.
Figure 11.9. Precision results and absolute error in space. Yellow points: 3D model volume; red points:
catheter tip tracked with EM system (ground truth); blue points: smoothed calculated central axis; green
points: catheter tip according to the proposed method.
framework to enter clinical usage. The first is down to the limitations in vessel
coverage using an imaging modality acquired at the tip of a catheter. The quality of
the acquired image-sets may be degraded by unpredictable abrupt movements of the
catheter tip (jumps) during the pullback scan due to localised bending, torsional
stiffness and lumen irregularities. These events may create extended filling deficits of
the vascular wall surface produced by the frame-sequence 3D repositioning.
Furthermore, the relative position of the catheter and the wall at a cross section
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Figure 11.10. The graphical user interface for the proposed framework.
may cause an incomplete capture of the wall inner circumference in cases of large
distances between the lumen and the frame centres; this is, in particular, possible in
the aorta. This deficit may also extend several centimetres along the vessel, requiring
rescanning. Scans conducted by using an automatic pullback device are more prone
to these kinds of errors, while the manual steering of the catheter allows for better
results. This way, the operator adapts the catheter configuration to the geometrical
shape of the vascular structure by applying appropriate torque in a real-time model.
However, multiple deficit regions may be present in the endovascular images due to
imaging noise and artefacts and these may cause a loss of potentially valuable
information that interferes with the different processing steps. As a solution we
propose the implementation of a mechanism that detects the position and extent of
the deficit and suggests a corrective rescanning of the corresponding vascular
segment. This mechanism could be based on detecting discontinuities of the wall
representation in a longitudinal mode, or tracking gaps in the wall region in polar
coordinates.
For the use of a static 3D scan, such as a preoperative or intra-operative 3D model,
co-registration is required. For example, in our framework, the detection of useful
anchoring points that would serve as a reference pattern for this task is necessary on
both IVUS and CT datasets, and consequently they must be matched. However, the
lack of information on the exact diameter of the CT-derived model at every discrete
cross section hinders the use of any point corresponding to a stenosis or an aneurysm
limiting our landmark set to that containing only the branch- and end-points of the
3D model axis. One way to expand the usable landmark set could be the application
of appropriate mesh-skeletonisation algorithms that would give feedback on the
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number of cycles (cropped voxels) needed to produce an ideal one-voxel central line.
Furthermore, the use of a more sophisticated method of IVUS image segmentation
could further enlarge the set of landmarks via embedding tissue characterisation
features and, thus, allowing for the discretisation among different lesions of the same
type and the creation of a plaque map. These improvements would increase
navigation precision and get us one step further towards the fully automated detection
of all landmarks. Our proposed system currently relies on a friendly graphical user
interface which allows for the manual detection and classification of each landmark
based on the fluctuations of several fitted-ellipses parameters along an IVUS image
stack. The development of novel training algorithm strategies, which may also
encompass statistical modelling data as well as anatomical and epidemiological
information, should further facilitate this process.
The current commercial electromagnetic tracking systems provide excellent
feedback on the position as well as the spatial orientation of the catheter tip (5 or
6 DOF sensors), as utilised in our first proposed system. However, the difficulties in
incorporating EM sensing during arterial procedures are clear. While our second
proposed framework provides independence from EM trackers, the system can only
partially compensate for the loss of the 3D orientation of the catheter tip.
Development of a strategy relying solely on IVUS image data that would resolve
this limitation and lead to better precision results is thus an important research
direction. The preliminary technique assumes a perfectly cylindrical model for the
vessel under investigation, with a constant wall thickness. By using measured wall
thickness, aided by an extension to in vivo data, it may be possible to determine the
true angular orientation of the IVUS scanning plane. This would also provide
clinicians with information about the current state of the catheter in vivo, without
resorting to the use of fluoroscopy for confirmation.
The proposed methods also cannot depict the changes to the entire vessel
structure caused by patient motion or the introduction of stiff endovascular devices
during a procedure. With the first proposed method, it is possible to perform an
extra IVUS pullback after deformation to reconstruct the new shape; however, the
second method is only able to identify the location of an endovascular imaging
device relative to a known map of the vasculature. The use of biomechanical
modelling has a role here in the simulation of the shape of the vessels after
deformation, in particular, with the use of limited intravascular imaging data to
act as constraints. The current limitation in its use is, as reviewed earlier in this
chapter, the computation time required.
In summary, while the benefits of using a safer and established intravascular
imaging modality for navigation purposes are clear, there are still a number of
challenging problems that require research to bring its application to clinical use.
11.5 Conclusion
We have presented here two intravascular navigation frameworks, both reliant on
IVUS imaging. The first requires EM tracking for the reconstruction of the
vasculature in situ, a technique able to compensate for any motion between the
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preoperative scan and the endovascular procedure. The second is based on image
processing of intra-operative IVUS data and the use of a preoperative model,
without the need for any external tracking equipment. This system aims to facilitate
endovascular procedures and especially in providing the operator with the opportunity to tag a specific point of interest along a vessel and re-visit it in less time and
with better precision.
While there remain issues to be addressed for the proposed frameworks to be
accepted clinically, the use of endovascular imaging methods for navigation is a
promising approach that limits the use of x-ray fluoroscopy and the accompanying
contrast agents required to delineate the arteries.
Acknowledgements
Partial support of this research was provided by the FP7-EU Project ‘Smart
Catheterization (SCATh)’.
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