The quantitative assessment of regional myocardial function remains an important goal in clinical cardiology. As such,
tissue Doppler imaging and speckle tracking based methods have been introduced to estimate local myocardial strain.
Recently, volumetric ultrasound has become more readily available, allowing therefore the 3D estimation of motion and
myocardial deformation. Our lab has previously presented a method based on spatio-temporal elastic registration of
ultrasound volumes to estimate myocardial motion and deformation in 3D, overcoming the spatial limitations of the
existing methods. This method was optimized on simulated data sets in previous work and is currently tested in a clinical
setting. In this manuscript, 10 healthy volunteers, 10 patient with myocardial infarction and 10 patients with arterial hypertension were included. The cardiac strain values extracted with the proposed method were compared with the ones estimated with 1D tissue Doppler imaging and 2D speckle tracking in all patient groups. Although the absolute values of the 3D strain components assessed by this new methodology were not identical to the reference methods, the relationship between the different patient groups was similar.
We present an algorithm for the segmentation of the liver portal veins from an arterial phase CT. The developed
segmentation algorithm incorporates a physiological model that states that the vasculature pattern is organized
such that the whole organ is perfused using minimal mechanical energy. This model is, amongst others, applicable
to the lungs, the liver, and the kidneys. The algorithm first locally detects probable candidate vessel segments in
the image. The subset of these segments that generates the most probable vessel tree according the image and
the physiological model is afterwards sought by a global optimization method. The algorithm has already been
applied successfully to segment heavily simplified lung vessel trees from CT images. Now the general feasibility
of this approach is evaluated by applying it to the segmentation of the liver portal veins from an arterial phase
CT scan. This is more challenging, because the intensity difference between the vessels and the parenchyma
is small. To cope with the low contrast a support vector machines approach with a robust feature vector is
used to locally detect vessels. This approach has been applied to a set of five images, for which a ground truth
segmentation is available. This algorithm is a first step towards an automatic segmentation of all of the liver
vasculature.
The goal of radiotherapy is to deliver maximal dose to the tumor and minimal dose to the surrounding tissue.
This requires accurate target definition. In sites were the tumor is difficult to see on the CT images, such as for
rectal cancer, PET-CT imaging can be used to better define the target. If the information from multiple PETCT
images with different tracers needs to be combined, a nonrigid registration is indispensable to compensate
for rectal tissue deformations. Such registration is complicated by the presence of different volumes of bowel
gas in the images to be registered. In this paper, we evaluate the performance of different nonrigid registration
approaches by looking at the overlap of manually delineated rectum contours after registration. Using a B-spline
transformation model, the results for two similarity measures, sum of squared differences and mutual information,
either calculated over the entire image or on a region of interest are compared. Finally, we also assess the effect
of the registration direction.
We show that the combination of MI with a region of interest is best able to cope with residual rectal contrast
and differences in bowel filling. We also show that for optimal performance the registration direction should be
chosen depending on the difference in bowel filling in the images to be registered.
We present a novel method for lung vessel tree segmentation. The method combines image information and a
high-level physiological model, stating that the vasculature is organized such that the whole organ is perfused
using minimal effort. The method consists of three consecutive steps. First, a limited set of possible bifurcation
locations is determined. Subsequently, individual vessel segments of varying diameters are constructed between
each two bifurcation locations. This way, a graph is constructed consisting of each bifurcation location candidate
as vertices and vessel segments as edges. Finally, the overall vessel tree is found by selecting the subset of these
segments that perfuses the whole organ, while minimizing an energy function. This energy function contains
a data term, a volume term and a bifurcation term. The data term measures how well the selected vessel
segments fit to the image data, the volume term measures the total amount of blood in the vasculature, and the
bifurcation term models the physiological fit of the diameters of the in- and outgoing vessels in each bifurcation.
The selection of the optimal subset of vessel segments into a single vessel tree is an NP-hard combinatorial
optimization problem that is solved here with an ant colony optimization approach. The bifurcation detection
as well as the segmentation method have been validated on lung CT images with manually segmented arteries
and veins.
KEYWORDS: Image registration, Ultrasonography, Data modeling, Error analysis, Motion estimation, 3D image processing, 3D modeling, 3D metrology, Motion models, Transducers
Current ultrasound methods for measuring myocardial strain are often limited to measurements in one or two
dimensions. Spatio-temporal elastic registration of 3D cardiac ultrasound data can however be used to estimate
the 3D motion and full 3D strain tensor. In this work, the spatio-temporal elastic registration method was
validated for both non-scanconverted and scanconverted images. This was done using simulated 3D pyramidal
ultrasound data sets based on a thick-walled deforming ellipsoid and an adapted convolution model. A B-spline
based frame-to-frame elastic registration method was applied to both the scanconverted and non-scanconverded
data sets and the accuracy of the resulting deformation fields was quantified. The mean accuracy of the estimated
displacement was very similar for the scanconverted and non-scanconverted data sets and thus, it was shown
that 3D elastic registration to estimate the cardiac deformation from ultrasound images can be performed on
non-scanconverted images, but that avoiding of the scanconversion step does not significantly improve the results
of the displacement estimation.
We present a new method to evaluate 4D (3D + time) cardiac ultrasound data sets by nonrigid spatio-temporal image registration. First, a frame-to-frame registration is performed that yields a dense deformation field. The deformation field is used to calculate local spatiotemporal properties of the myocardium, such as the velocity, strain and strain rate. The field is also used to propagate particular points and surfaces, representing e.g. the endo-cardial surface over the different frames. As such, the 4D path of these point is obtained, which can be used to calculate the velocity by which the wall moves and the evolution of the local surface area over time. The wall velocity is not angle-dependent as in classical Doppler imaging, since the 4D data allows calculating the true 3D motion. Similarly, all 3D myocardium strain components can be estimated. Combined they result in local surface area or volume changes which van be color-coded as a measure of local contractability. A diagnostic method that strongly benefits from this technique is cardiac motion and deformation analysis, which is an important aid to quantify the mechanical properties of the myocardium.
Temporal subtraction is a visual enhancement technique to improve the detection of pathological changes from
medical images acquired at different times. Prior to subtracting a previous image from a current image, a nonrigid
warping of the two images might be necessary. As the nonrigid warping may change the size of pathological
lesions, the subtraction image can be misleading. In this paper we present an alternative subtraction technique to
avoid this problem. Instead of subtracting the intensities of corresponding voxels, a convolution filter is applied
to both images prior to subtraction. The technique is demonstrated for computed tomography images of the
lungs. It is shown that this method results in an improved visual enhancement of changing nodules compared
with the conventional subtraction technique.
A new generic model-based segmentation scheme is presented, which
can be trained from examples akin to the Active Shape Model (ASM)
approach in order to acquire knowledge about the shape to be
segmented and about the gray-level appearance of the object in the
image. Because in the ASM approach the intensity and shape models
are typically applied alternately during optimizing as first an
optimal target location is selected for each landmark separately
based on local gray-level appearance information only to which the
shape model is fitted subsequently, the ASM may be misled in case
of wrongly selected landmark locations. Instead, the proposed
approach optimizes for shape and intensity characteristics
simultaneously. Local gray-level appearance information at the
landmark points extracted from feature images is used to
automatically detect a number of plausible candidate locations for
each landmark. The shape information is described by multiple
landmark-specific statistical models that capture local
dependencies between adjacent landmarks on the shape. The shape
and intensity models are combined in a single cost function that
is optimized non-iteratively using dynamic programming which
allows to find the optimal landmark positions using combined shape
and intensity information, without the need for initialization.
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