Area-preserving flattening maps of 3D ultrasound carotid arteries images.
ABSTRACT Quantitative measurements of the progression (or regression) of carotid plaque burden are important in monitoring patients and evaluating new treatment options. 3D ultrasound (US) has been used to monitor the progression of carotid artery plaques in symptomatic and asymptomatic patients, and different methods of measuring various ultrasound phenotypes of atherosclerosis have been developed. We have developed a quantitative metric used to analyze changes in carotid plaque morphology from 3D US. This method matched the vertices on the carotid arterial wall surface with those on the luminal surface. Vessel-wall-plus-plaque thickness (VWT) was obtained by computing the distance between each corresponding pair, which was then superimposed on the arterial wall to produce the VWT map. Since the progression of plaque thickness is important in monitoring patients who are at risk for stroke, we also computed the change of VWT by comparing the VWT maps obtained for a patient at two different time points. In this paper, we propose a technique to flatten the 3D VWT and VWT-Change maps in an area-preserving manner, in order to facilitate the visualization and interpretation of these maps.
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ABSTRACT: Purpose: Three-dimensional ultrasound (3DUS) vessel wall volume (VWV) provides a 3D measurement of carotid artery wall remodeling and atherosclerotic plaque and is sensitive to temporal changes of carotid plaque burden. Unfortunately, although 3DUS VWV provides many advantages compared to measurements of arterial wall thickening or plaque alone, it is still not widely used in research or clinical practice because of the inordinate amount of time required to train observers and to generate 3DUS VWV measurements. In this regard, semiautomated methods for segmentation of the carotid media-adventitia boundary (MAB) and the lumen-intima boundary (LIB) would greatly improve the time to train observers and for them to generate 3DUS VWV measurements with high reproducibility.Methods: The authors describe a 3D algorithm based on a modified sparse field level set method for segmenting the MAB and LIB of the common carotid artery (CCA) from 3DUS images. To the authors' knowledge, the proposed algorithm is the first direct 3D segmentation method, which has been validated for segmenting both the carotid MAB and the LIB from 3DUS images for the purpose of computing VWV. Initialization of the algorithm requires the observer to choose anchor points on each boundary on a set of transverse slices with a user-specified interslice distance (ISD), in which larger ISD requires fewer user interactions than smaller ISD. To address the challenges of the MAB and LIB segmentations from 3DUS images, the authors integrated regional- and boundary-based image statistics, expert initializations, and anatomically motivated boundary separation into the segmentation. The MAB is segmented by incorporating local region-based image information, image gradients, and the anchor points provided by the observer. Moreover, a local smoothness term is utilized to maintain the smooth surface of the MAB. The LIB is segmented by constraining its evolution using the already segmented surface of the MAB, in addition to the global region-based information and the anchor points. The algorithm-generated surfaces were sliced and evaluated with respect to manual segmentations on a slice-by-slice basis using 21 3DUS images.Results: The authors used ISD of 1, 2, 3, 4, and 10 mm for algorithm initialization to generate segmentation results. The algorithm-generated accuracy and intraobserver variability results are comparable to the previous methods, but with fewer user interactions. For example, for the ISD of 3 mm, the algorithm yielded an average Dice coefficient of 94.4% ± 2.2% and 90.6% ± 5.0% for the MAB and LIB and the coefficient of variation of 6.8% for computing the VWV of the CCA, while requiring only 1.72 min (vs 8.3 min for manual segmentation) for a 3DUS image.Conclusions: The proposed 3D semiautomated segmentation algorithm yielded high-accuracy and high-repeatability, while reducing the expert interaction required for initializing the algorithm than the previous 2D methods.Medical Physics 03/2013; 40(5). · 2.91 Impact Factor
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ABSTRACT: Imaging equipment is experiencing advances in speed, sensitivity, safety, and workflow. There is an increasing trend toward physiologic imaging and quantitation, requiring greater consistency across manufacturers and clinics. The Human Connectome Project is symbolic of the drive toward combining multimodality anatomic and functional imaging with quantitation and sophisticated atlases. Advanced visualization methods have become essential in the evaluation of large multidimensional data sets. Hybrid imaging blends advantages from multiple modalities to provide a comprehensive anatomic, functional, physiologic, and metabolic data set. Breakthrough clinical neuroimaging applications are derived from an alignment of scientific, engineering, clinical, and business conditions.Neurologic Clinics 02/2014; 32(1):1-29. · 1.34 Impact Factor
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ABSTRACT: Automatic segmentation of the carotid plaques from ultrasound images has been shown to be an important task for monitoring progression and regression of carotid atherosclerosis. Considering the complex structure and heterogeneity of plaques, a fully automatic segmentation method based on media-adventitia and lumen-intima boundary priors is proposed. This method combines image intensity with structure information in both initialization and a level-set evolution process. Algorithm accuracy was examined on the common carotid artery part of 26 3-D carotid ultrasound images (34 plaques ranging in volume from 2.5 to 456 mm(3)) by comparing the results of our algorithm with manual segmentations of two experts. Evaluation results indicated that the algorithm yielded total plaque volume (TPV) differences of -5.3 ± 12.7 and -8.5 ± 13.8 mm(3) and absolute TPV differences of 9.9 ± 9.5 and 11.8 ± 11.1 mm(3). Moreover, high correlation coefficients in generating TPV (0.993 and 0.992) between algorithm results and both sets of manual results were obtained. The automatic method provides a reliable way to segment carotid plaque in 3-D ultrasound images and can be used in clinical practice to estimate plaque measurements for management of carotid atherosclerosis.Ultrasound in medicine & biology 09/2013; · 2.46 Impact Factor