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Reproducibility of CT-based texture feature quantification of simulated and 3D-printed trabecular bone: influence of noise and reconstruction kernel
Author(s): Nada Kamona; Qin Li; Benjamin Berman; Berkman Sahiner; Nicholas Petrick
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Paper Abstract

Computed tomography (CT) based texture feature measurements of trabecular bone may be used as imaging biomarkers for bone health assessment. This study investigated the effects of image noise and reconstruction kernels on the reproducibility of CT-based texture features of simulated bone images and their correlation to underlying physical bone microarchitecture. We used the Voronoi tessellation method to create lattices and applied morphological processing, including stochastic edge pruning, plates filling, dilating, smoothing and thresholding, to achieve trabecular bone-like structures with controllable trabecular bone parameters. The simulated structures of various trabecular bone thicknesses were passed through an imaging model: CT images were created at a pixel size of 0.24 x 0.24 mm2 by convolving the structure with a proper modulation transfer function at multiple cutoff frequency values representing different sharpness levels associated with clinical CT reconstruction kernels. Noise was added using a range of standard deviations to simulate different dose levels. We examined 39 texture features’ correlation with trabecular bone thickness and assessed their reproducibility using the Concordance Correlation Coefficient metric. Our preliminary results show the realism of our simulation model when compared to the CT scans of 3D-printed phantoms. We found that first and second-order image texture feature measurements correlated better with trabecular thickness compared to higher order features. However, the reproducibility of texture features across different reconstruction kernels and noise levels was limited. High reproducibility required the use of very sharp kernels and similar noise levels across images.

Paper Details

Date Published: 13 March 2019
PDF: 7 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109501S (13 March 2019); doi: 10.1117/12.2512571
Show Author Affiliations
Nada Kamona, The George Washington Univ. (United States)
Qin Li, U.S. Food and Drug Administration (United States)
Benjamin Berman, U.S. Food and Drug Administration (United States)
Berkman Sahiner, U.S. Food and Drug Administration (United States)
Nicholas Petrick, U.S. Food and Drug Administration (United States)


Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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