In the context of cardiac applications, the primary goal of coronary vessel analysis often consists in supporting the diagnosis
of vessel wall anomalies, such as coronary plaque and stenosis. Therefore, a fast and robust segmentation of the coronary
tree is a very important but challenging task.
We propose a new approach for coronary artery segmentation. Our method is based on an earlier proposed progressive
region growing. A new growth front monitoring technique controls the segmentation and corrects local leakage by retrospective
detection and removal of leakage artifacts. While progressively reducing the region growing threshold for the
whole image, the growing process is locally analyzed using criteria based on the assumption of tubular, gradually narrowing
vessels. If a voxel volume limit or a certain shape constraint is exceeded, the growing process is interrupted. Voxels
affected by a failed segmentation are detected and deleted from the result. To avoid further processing at these positions, a
large neighborhood is blocked for growing.
Compared to a global region growing without local correction, our new local growth control and the adapted correction
can deal with contrast decrease even in very small coronary arteries. Furthermore, our algorithm can efficiently handle
noise artifacts and partial volume effects near the myocardium. The enhanced segmentation of more distal vessel parts was
tested on 150 CT datasets. Furthermore, a comparison between the pure progressive region growing and our new approach
was conducted.
The diagnosis support in the field of coronary artery disease (CAD) is very complex due to the numerous
symptoms and performed studies leading to the final diagnosis. CTA and MRI are on their way to replace invasive
catheter angiography. Thus, there is a need for sophisticated software tools that present the different analysis
results, and correlate the anatomical and dynamic image information. We introduce a new software assistant
for the combined result visualization of CTA and MR images, in which a dedicated concept for the structured
presentation of original data, segmentation results, and individual findings is realized. Therefore, we define a
comprehensive class hierarchy and assign suitable interaction functions. User guidance is coupled as closely as
possible with available data, supporting a straightforward workflow design. The analysis results are extracted
from two previously developed software assistants, providing coronary artery analysis and measurements, function
analysis as well as late enhancement data investigation. As an extension we introduce a finding concept directly
relating suspicious positions to the underlying data. An affine registration of CT and MR data in combination
with the AHA 17-segment model enables the coupling of local findings to positions in all data sets. Furthermore,
sophisticated visualization in 2D and 3D and interactive bull's eye plots facilitate a correlation of coronary
stenoses and physiology. The software has been evaluated on 20 patient data sets.
The analysis of myocardial tissue with contrast-enhanced MR yields multiple parameters, which can be used to classify
the examined tissue. Perfusion images are often distorted by motion, while late enhancement images are acquired with a
different size and resolution. Therefore, it is common to reduce the analysis to a visual inspection, or to the examination
of parameters related to the 17-segment-model proposed by the American Heart Association (AHA). As this
simplification comes along with a considerable loss of information, our purpose is to provide methods for a more
accurate analysis regarding topological and functional tissue features. In order to achieve this, we implemented
registration methods for the motion correction of the perfusion sequence and the matching of the late enhancement
information onto the perfusion image and vice versa. For the motion corrected perfusion sequence, vector images
containing the voxel enhancement curves' semi-quantitative parameters are derived. The resulting vector images are
combined with the late enhancement information and form the basis for the tissue examination. For the exploration of
data we propose different modes: the inspection of the enhancement curves and parameter distribution in areas
automatically segmented using the late enhancement information, the inspection of regions segmented in parameter
space by user defined threshold intervals and the topological comparison of regions segmented with different settings.
Results showed a more accurate detection of distorted regions in comparison to the AHA-model-based evaluation.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.