One of the key technical challenges in developing an
extensible image-guided navigation system is that of interfacing with external proprietary hardware. The technical challenges arise from the constraints placed on the navigation system's hardware and software. Extending a navigation system's functionality by interfacing with an external hardware device may require modifications to internal hardware components. In some cases, it would
also require porting the complete code to a different operating system that is compatible with the manufacturer supplied application programming interface libraries and drivers. In this paper we describe our experience extending a multi-platform navigation system, implemented using the image-guided surgery toolkit IGSTK, to
support real-time acquisition of 2-D ultrasound (US) images acquired with the Terason portable US system. We describe the required hardware and software modifications imposed by the proposed extension and how the OpenIGTLink network communication protocol enabled us to minimize the changes to the system's hardware and software. The resulting navigation system retains its platform independence with the added capability for real-time image acquisition independent of the image source.
The bidomain/monodomain equations have been widely used to model electrical activity in cardiac tissue. Here
we present a sensitivity study of a crucial parameter in the bidomain model, the tissue conductivity. This
study is necessary since there is no general agreement on the actual values that should be employed, mainly
due to inconsistencies between the few sources of empirical information existent in the literature. Furthermore,
estimates of this parameter from either imaging techniques or from experiments on isolated cardiac tissue have
been inconsistent. For this study, a 3D biventricular model built from Multi-Detector Computer Tomography
was used with the most relevant electrical structures, such as myocardial fiber orientation and the Purkinje
system, were included. Specific ionic models for normal myocardium and for the Purkinje system were taken
into account. Finite Element methods were used to solve the monodomain equation for a number of different
conductivity settings. Comparative results using isochronal maps are shown in combination with statistical tests
to measure changes in the sequence of electrical activation in the myocardium, conduction velocities (CV), and
local activation times (LAT).
KEYWORDS: Single photon emission computed tomography, 3D modeling, Image segmentation, Data modeling, Statistical modeling, Monte Carlo methods, Error analysis, Statistical analysis, Heart, Gold
Over the course of the last two decades, myocardial perfusion with Single Photon Emission Computed Tomography
(SPECT) has emerged as an established and well-validated method for assessing myocardial ischemia,
viability, and function. Gated-SPECT imaging integrates traditional perfusion information along with global
left ventricular function. Despite of these advantages, inherent limitations of SPECT imaging yield a challenging
segmentation problem, since an error of only one voxel along the chamber surface may generate a huge difference
in volume calculation. In previous works we implemented a 3-D statistical model-based algorithm for Left Ventricle
(LV) segmentation of in dynamic perfusion SPECT studies. The present work evaluates the relevance of
training a different Active Shape Model (ASM) for each frame of the gated SPECT imaging acquisition in terms
of their subsequent segmentation accuracy. Models are subsequently employed to segment the LV cavity of gated
SPECT studies of a virtual population. The evaluation is accomplished by comparing point-to-surface (P2S)
and volume errors, both against a proper Gold Standard. The dataset comprised 40 voxel phantoms (NCAT,
Johns Hopkins, University of of North Carolina). Monte-Carlo simulations were generated with SIMIND (Lund
University) and reconstructed to tomographic slices with ASPIRE (University of Michigan).
In the present paper we describe the automatic construction of a statistical shape model of the whole heart built
from a training set of 100 Multi-Slice Computed Tomography (MSCT) studies of pathologic and asymptomatic
patients, including 15 (temporal) cardiac phases each. With these data sets we were able to build a compact
and representative shape model of both inter-subject and temporal variability. A practical limitation in building
statistical shape models, and in particular point distribution models (PDM), is the manual delineation of the
training set. A key advantage of the proposed method is to overcome this limitation by not requiring them.
Another one is the use of MSCT images, which thanks to their excellent anatomical depiction, have allowed
for a realistic heart representation, including the four chambers and connected vasculature. The generalization
ability of the shape model permits its deformation to unseen anatomies with an acceptable accuracy. Moreover,
its compactness allows for having a reduced set of parameters to describe the modeled population. By varying
these parameters, the statistical model can generate a set of valid examples. This is especially useful for the
generation of synthetic populations of cardiac shapes, that may correspond e.g. to healthy or diseased cases.
Finally, an illustrative example of the use of the constructed shape model for cardiac segmentation is provided.
KEYWORDS: 3D modeling, Image segmentation, 3D image processing, Ultrasonography, Transducers, Tissues, In vivo imaging, Data modeling, Point spread functions, Image resolution
In this paper a study of 3D ultrasound cardiac segmentation using Active Shape Models (ASM) is presented.
The proposed approach is based on a combination of a point distribution model constructed from a multitude of
high resolution MRI scans and the appearance model obtained from simulated 3D ultrasound images. Usually
the appearance model is learnt from a set of landmarked images. The significant level of noise, the low resolution
of 3D ultrasound images (3D US) and the frequent failure to capture the complete wall of the left ventricle (LV)
makes automatic or manual landmarking difficult. One possible solution is to use artificially simulated 3D US
images since the generated images will match exactly the shape in question. In this way, by varying simulation
parameters and generating corresponding images, it is possible to obtain a training set where the image matches
the shape exactly. In this work the simulation of ultrasound images is performed by a convolutional approach.
The evaluation of segmentation accuracy is performed on both simulated and in vivo images. The results obtained
on 567 simulated images had an average error of 1.9 mm (1.73 ± 0.05 mm for epicardium and 2 ± 0.07 mm for
endocardium, with 95% confidence) with voxel size being 1.1 × 1.1 × 0.7 mm. The error on 20 in vivo data was
3.5 mm (3.44 ± 0.4 mm for epicardium and 3.73 ± 0.4 mm for endocardium). In most images the model was
able to approximate the borders of myocardium even when the latter was indistinguishable from the surrounding
tissues.
In this paper we present a statistical model-based approach to
three-dimensional (3D) analysis of gated SPECT perfusion studies.
By means of a 3D Active Shape Model (3D-ASM) segmentation
algorithm, delineations of the endo- and epicardial borders of the
left ventricle are obtained, in all temporal phases and image
slices of the study. Prior knowledge was captured from a training
set of cardiac MRI and SPECT studies, from which geometrical
(shape) and grey-level (appearance) statistical models were built.
From the fitted shape, a truly 3D representation of the left
ventricle, a series of global and regional functional parameters
can be assessed. A myocardial center surface representation is
built on top of which scalar maps of perfusion, thickness or
motion can be depicted. Preliminary results were quite
encouraging, suggesting that statistical model-based segmentation
may serve as a robust technique for routine use.
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