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Aortic Lumen Detection

Aortic Lumen Detection. Brad Wendorff, ECE 539. Background. Extremely important diagnostic tool – eliminates need for “exploratory surgery” X-Ray Computed Tomography (CT) 3 Steps Injection of radio-opaque dye (iodine) Acquisition and 3D reconstruction of 2D images

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Aortic Lumen Detection

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  1. Aortic Lumen Detection Brad Wendorff, ECE 539

  2. Background • Extremely important diagnostic tool – eliminates need for “exploratory surgery” • X-Ray Computed Tomography (CT) • 3 Steps • Injection of radio-opaque dye (iodine) • Acquisition and 3D reconstruction of 2D images • Creation of angiograms via 3D reconstruction or reprojection of 2D sections

  3. Motivation • Physicians are often interested in specific regions • Pre-processing may be required to remove impeding or irrelevant structures • Current pre-processing methods require manual tracing of regions of interest • TIME INTENSIVE – CT scans contain hundreds of 2D images • Manual pre-processing is difficult to reproduce • Increase accuracy and efficiency by automating

  4. Design Considerations • Attenuation within blood vessels may vary thus affecting Hounsfield Unit values • Measured attenuation may be corrupted by CT artifacts • Calcium • Thrombus • Iodine enhances only vascular lumen – It does not perfuse into areas of thrombus uniformly • Semiautomated

  5. 3D Reconstruction Aortic Lumen

  6. Method of Detection

  7. K-means Clustering • Assign data points (voxels) to the cluster with the closest center • Continues to aggregate data points into each cluster until no changes occur • Implement this strategy on a series of axial slices • Extract cluster representing the aortic lumen

  8. Analysis of Results • Quality of results is based on a comparison with segmentation produced by Industry Standard program TeraReconiNtuition • Cluster diameters will be compared to manually edited segmentation in TeraRecon

  9. Questions?

  10. References S. Shiffman, G. D. Rubin, and S. Napel, Semiautomated editing of computed tomography sections for visualization of vasculature, vol. 2707, SPIE, 1996. http://www.siue.edu/~sumbaug/RetinalProjectPapers/Review%20of%20Blood%20Vessel%20Extraction%20Techniques%20and%20Algorithms.pdf

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