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Proceedings Paper

Design of CT reconstruction kernel specifically for clinical lung imaging
Author(s): Dianna D. Cody; Jiang Hsieh; Gregory W. Gladish
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Paper Abstract

In this study we developed a new reconstruction kernel specifically for chest CT imaging. An experimental flat-panel CT scanner was used on large dogs to produce 'ground-truth’ reference chest CT images. These dogs were also examined using a clinical 16-slice CT scanner. We concluded from the dog images acquired on the clinical scanner that the loss of subtle lung structures was due mostly to the presence of the background noise texture when using currently available reconstruction kernels. This qualitative evaluation of the dog CT images prompted the design of a new recon kernel. This new kernel consisted of the combination of a low-pass and a high-pass kernel to produce a new reconstruction kernel, called the 'Hybrid’ kernel. The performance of this Hybrid kernel fell between the two kernels on which it was based, as expected. This Hybrid kernel was also applied to a set of 50 patient data sets; the analysis of these clinical images is underway. We are hopeful that this Hybrid kernel will produce clinical images with an acceptable tradeoff of lung detail, reliable HU, and image noise.

Paper Details

Date Published: 14 April 2005
PDF: 8 pages
Proc. SPIE 5746, Medical Imaging 2005: Physiology, Function, and Structure from Medical Images, (14 April 2005); doi: 10.1117/12.594973
Show Author Affiliations
Dianna D. Cody, Univ. of Texas M.D. Anderson Cancer Ctr. (United States)
Jiang Hsieh, GE Healthcare (United States)
Gregory W. Gladish, Univ. of Texas M.D. Anderson Cancer Ctr. (United States)


Published in SPIE Proceedings Vol. 5746:
Medical Imaging 2005: Physiology, Function, and Structure from Medical Images
Amir A. Amini; Armando Manduca, Editor(s)

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