CTA-based Vessel Visualization Techniques

Cardiovascular and cerebrovascular diseases have become one of the diseases with highest incidence and mortality rates. Computed tomography and magnetic resonance imaging techniques make the modern imaging technology become an important diagnosis means of blood vessel diseases. In the blood vessel analysis and diagnosis system, the extraction and visualization of blood vessels play a vita role. The segmentation and extraction algorithms vary depending on imaging modality, application field and other factors. However, there is no such a general segmentation algorithm that is suitable to all medical imaging modalities. In the blood vessel surgery, visualization can locate the stenosis position which is helpful for doctors making an appropriate surgery plan.After twenty-year development, blood vessel visualization is still in its infancy and remains a great number of problems demanding prompt solution in both theory and application. Based on the existent vessel extraction and visualization techniques, this paper focuses on the research of computed tomography angiography vessel volume datasets. Computer graphics theory and image processing theory are utilized to realize more accurate visualization techniques and centerline extraction algorithms, respectively. The visualization and centerline extracted are also combined to provide more information for the diagnosis of blood vessel diseases. The main contributions and innovative ideas of this paper are summarized as follows:(1) A high-precision curved planar reformation is realized against the errors caused by simulating curves using pieces of lines in curved planar reformation at present. In the high-precision method, all control points are used to produce curves with B-spline fitting according to the sampling steps specified. Based on this, some assistant functions of curved planar reformation are introduced to enhance the performance of the curved planar reformation, such as adjustments of window widths and window levels, rotating curved planar reformation.(2) In order to make more accurate observations of the diseases in blood vessel, a centerline extraction method based on Snake model is proposed. First, a region of interest of blood vessel is determined by users in any slice of the volume data. And an initializing contour method is used to produce the initial contour for Snake model, which accelerates the convergence of the Snake model. Then, a final contour can be generated by the convergence of Snake model and a center point of the contour is obtained as the center of blood vessel in current slice. And the final contour in current slice is regarded as the initial contour of the next slice. In this way, a centerline can be obtained by connecting all the center points slice by slice.(3) Aiming at the low speed in segmentation of blood vessel volume data using traversal method, an octree structure is introduced to accelerate the blood vessel segmentation process and the computation of distance from boundary field. Then, a maximum spanning tree of vascular structure is constructed based on the distance from boundary field and the trunk of the tree is extracted which is the centerline. Finally, a graphics processing unit-based 3D texture-mapping volume rendering method is utilized to show the virtual endoscopy of blood vessel along the centerline.(4) To overcome the disadvantage in computation of the traditional distance from boundary field which loses the features of original data, an improved centerline extraction algorithm based on the distance from boundary field is proposed. In this algorithm, for each target voxel in original computed tomography angiography volume datasets, the sum of reverse of gradient and laplacian transformation value is regarded as the initial value, and the centerline extracted based on distance from boundary field is modified by center of gravity method. Compared to the traditional centerline extraction based on distance from boundary field, the centerline extracted by this algorithm is closer to the one extracted manually.(5) In order to extract more accurate centerlines for vascular structures in 3D medical volume data, a centerline extraction method based on Hessian matrix is proposed. Firstly, Hessian matrix is computed for each target voxel in the segmented data which is always binary. Then, a coarse centerline is generated according to the eigenvalues and the eigenvectors of each target voxel's Hessian matrix. Finally, scale space analysis method is used to refine each point in the coarse centerline in its cross-section plane. Thus, an accurate centerline is obtained.All achievements above have presented research schemes and experimental results in blood vessel visualization and extraction, which will provide more advanced assistant methods for blood vessel diseases diagnosis. Besides, curved planar reformation and several different centerline extraction algorithms proposed in this paper have generality and enrich the theories and applications of vascular structures visualization.