Coronary artery trees (CATs) are often extracted to aid the fully automatic analysis of coronary artery disease on coronary computed tomography angiography (CCTA) images. Automatically extracted CATs often miss some arteries or include wrong extractions which require manual corrections before performing successive steps. For analyzing a large number of datasets, a manual quality check of the extraction results is time-consuming. This paper presents a method to automatically calculate quality scores for extracted CATs in terms of clinical significance of the extracted arteries and the completeness of the extracted CAT. Both right dominant (RD) and left dominant (LD) anatomical statistical models are generated and exploited in developing the quality score. To automatically determine which model should be used, a dominance type detection method is also designed. Experiments are performed on the automatically extracted and manually refined CATs from 42 datasets to evaluate the proposed quality score. In 39 (92.9%) cases, the proposed method is able to measure the quality of the manually refined CATs with higher scores than the automatically extracted CATs. In a 100-point scale system, the average scores for automatically and manually refined CATs are 82.0 (±15.8) and 88.9 (±5.4) respectively. The proposed quality score will assist the automatic processing of the CAT extractions for large cohorts which contain both RD and LD cases. To the best of our knowledge, this is the first time that a general quality score for an extracted CAT is presented.
Trans-catheter aortic valve replacement (TAVR) is an evolving technique for patients with serious aortic stenosis disease. Typically, in this application a CTA data set is obtained of the patient’s arterial system from the subclavian artery to the femoral arteries, to evaluate the quality of the vascular access route and analyze the aortic root to determine if and which prosthesis should be used. In this paper, we concentrate on the automated segmentation of the aortic root. The purpose of this study was to automatically segment the aortic root in computed tomography angiography (CTA) datasets to support TAVR procedures. The method in this study includes 4 major steps. First, the patient’s cardiac CTA image was resampled to reduce the computation time. Next, the cardiac CTA image was segmented using an atlas-based approach. The most similar atlas was selected from a total of 8 atlases based on its image similarity to the input CTA image. Third, the aortic root segmentation from the previous step was transferred to the patient’s whole-body CTA image by affine registration and refined in the fourth step using a deformable subdivision surface model fitting procedure based on image intensity. The pipeline was applied to 20 patients. The ground truth was created by an analyst who semi-automatically corrected the contours of the automatic method, where necessary. The average Dice similarity index between the segmentations of the automatic method and the ground truth was found to be 0.965±0.024. In conclusion, the current results are very promising.
KEYWORDS: X-rays, X-ray imaging, Visualization, 3D image processing, Angiography, Arteries, Information visualization, 3D acquisition, Data fusion, Image-guided intervention, 3D modeling, Image visualization, Data modeling
Coronary Artery Disease (CAD) results in the buildup of plaque below the intima layer inside the vessel wall of the coronary arteries causing narrowing of the vessel and obstructing blood flow. Percutaneous coronary intervention (PCI) is usually done to enlarge the vessel lumen and regain back normal flow of blood to the heart. During PCI, X-ray imaging is done to assist guide wire movement through the vessels to the area of stenosis. While X-ray imaging allows for good lumen visualization, information on plaque type is unavailable. Also due to the projection nature of the X-ray imaging, additional drawbacks such as foreshortening and overlap of vessels limit the efficacy of the cardiac intervention. Reconstruction of 3D vessel geometry from biplane X-ray acquisitions helps to overcome some of these projection drawbacks. However, the plaque type information remains an issue. In contrast, imaging using computed tomography angiography (CTA) can provide us with information on both lumen and plaque type and allows us to generate a complete 3D coronary vessel tree unaffected by the foreshortening and overlap problems of the X-ray imaging. In this paper, we combine x-ray biplane images with CT angiography to visualize three plaque types (dense calcium, fibrous fatty and necrotic core) on x-ray images. 3D registration using three different registration methods is done between coronary centerlines available from x-ray images and from the CTA volume along with 3D plaque information available from CTA. We compare the different registration methods and evaluate their performance based on 3D root mean squared errors. Two methods are used to project this 3D information onto 2D plane of the x-ray biplane images. Validation of our approach is performed using artificial biplane x-ray datasets.
KEYWORDS: Image segmentation, Data modeling, Arteries, Medical imaging, Magnetic resonance imaging, Image processing, Systems modeling, Image processing algorithms and systems, Blood, Navigation systems
This paper describes a novel method for segmentation and modeling of branching vessel structures in medical images using adaptive subdivision surfaces fitting. The method starts with a rough initial skeleton model of the vessel structure. A coarse triangular control mesh consisting of hexagonal rings and dedicated bifurcation elements is constructed from this skeleton. Special attention is paid to ensure a topological sound control mesh is created around the bifurcation areas. Then, a smooth tubular surface is obtained from this coarse mesh using a standard subdivision scheme. This subdivision surface is iteratively fitted to the image. During the fitting, the target update locations of the subdivision surface are obtained using a scanline search along the surface normals, finding the maximum gradient magnitude (of the imaging data). In addition to this surface fitting framework, we propose an adaptive mesh refinement scheme. In this step the coarse control mesh topology is updated based on the current segmentation result, enabling adaptation to varying vessel lumen diameters. This enhances the robustness and flexibility of the method and reduces the amount of prior knowledge needed to create the initial skeletal model. The method was applied to publicly available CTA data from the Carotid Bifurcation Algorithm Evaluation Framework resulting in an average dice index of 89.2% with the ground truth. Application of the method to the complex vascular structure of a coronary artery tree in CTA and to MRI images were performed to show the versatility and flexibility of the proposed framework.
Multi-contrast MRI is a frequently used imaging technique in preclinical brain imaging. In longitudinal cross-sectional
studies exploring and browsing through this high-throughput, heterogeneous data can become a very demanding task. The goal of this work was to build an intuitive and easy to use, dedicated visualization and side-by-side exploration tool for heterogeneous, co-registered multi-contrast, follow-up cross-sectional MRI data. The deformation field, which results from the registration step, was used to automatically link the same voxel in the displayed datasets of interest. Its determinant of the Jacobian (detJac) was used for a faster and more accurate visual assessment and comparison of brain deformation between the follow-up scans. This was combined with an efficient data management scheme. We investigated the functionality and the utility of our tool in the neuroimaging research field by means of a case study evaluation with three experienced domain scientists, using longitudinal, cross-sectional multi-contrast MRI rat brain data. Based on the performed case study evaluation we can conclude that the proposed tool improves the visual assessment of high-throughput cross-sectional, multi-contrast, follow-up data and can further assist in guiding quantitative studies.
For optimal diagnosis and treatment of lesions at coronary artery bifurcations using x-ray angiography, it is of utmost
importance to determine proper angiographic viewing angles. Due to the increasing use of CTA as a first line diagnostic
tool, 3D CTA data is more frequently available before x-ray angiographic procedures take place. Motivated by this, we
propose to use available CTA data for the determination of patient specific optimal x-ray viewing angles.
A semi-automatic iterative region growing scheme is developed for the segmentation of the coronary arterial tree. From
the segmented arterial tree, a complete hierarchical surface and centerline representation, including bifurcation points, is
automatically obtained. The optimal viewing angle for a selected bifurcation is determined as the view rendering the
least amount of foreshortening and vessel overlap.
For 83 bifurcation areas, viewing angles were automatically determined. The sensitivity of the method to patient
positioning in the x-ray system was also studied. Next, the automatically determined angels were both quantitatively and
qualitatively compared with angles determined by two experts.
The method was found not to be sensitive to the positioning of the patient in the angiographic x-ray system. In 95% of
the cases our method produced a clinically usable view (mean score of 8.4 out of 10) as compared to 98% for the experts
(mean score of 8.7). Our method produced angiographic views with significantly less foreshortening (mean difference of
10 percentage points) than the angiographic views set by the experts.
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