Method for accurate sizing of pulmonary vessels from 3D medical images

Walter G. O'Dell

doi:10.1117/12.2082644

20 March 2015 Method for accurate sizing of pulmonary vessels from 3D medical images

Walter G. O'Dell

Proceedings Volume 9413, Medical Imaging 2015: Image Processing; 94132J (2015) https://doi.org/10.1117/12.2082644
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

Detailed characterization of vascular anatomy, in particular the quantification of changes in the distribution of vessel sizes and of vascular pruning, is essential for the diagnosis and management of a variety of pulmonary vascular diseases and for the care of cancer survivors who have received radiation to the thorax. Clinical estimates of vessel radii are typically based on setting a pixel intensity threshold and counting how many “On” pixels are present across the vessel cross-section. A more objective approach introduced recently involves fitting the image with a library of spherical Gaussian filters and utilizing the size of the best matching filter as the estimate of vessel diameter. However, both these approaches have significant accuracy limitations including mis-match between a Gaussian intensity distribution and that of real vessels. Here we introduce and demonstrate a novel approach for accurate vessel sizing using 3D appearance models of a tubular structure along a curvilinear trajectory in 3D space. The vessel branch trajectories are represented with cubic Hermite splines and the tubular branch surfaces represented as a finite element surface mesh. An iterative parameter adjustment scheme is employed to optimally match the appearance models to a patient’s chest X-ray computed tomography (CT) scan to generate estimates for branch radii and trajectories with subpixel resolution. The method is demonstrated on pulmonary vasculature in an adult human CT scan, and on 2D simulated test cases.

Citation Download Citation

Walter G. O'Dell "Method for accurate sizing of pulmonary vessels from 3D medical images", Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 94132J (20 March 2015); https://doi.org/10.1117/12.2082644

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