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  Creation of angiograms via 3D reconstruction or reprojection of 2D sections

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

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

3D Reconstruction Aortic Lumen

Method of Detection Sequence of Raw Images Automated Segmentation Kmeans Clustering Algorithm Sequence of Labeled Images 3D Reconstruction (Time Permitting)

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

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

Questions?

References  S. Shiffman, G. D. Rubin, and S. Napel, Semiautomated editing of computed tomography sections for visualization of vasculature, vol. 2707, SPIE,  pers/Review%20of%20Blood%20Vessel%20Extracti on%20Techniques%20and%20Algorithms.pdf