Volume 38, Issue 9 p. 5003-5011
Radiation imaging physics

Long range node-strut analysis of trabecular bone microarchitecture

Tanya Schmah,

Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3G4, Canada

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Norbert Marwan,

Potsdam Institute for Climate Impact Research (PIK), D-14412 Potsdam, Germany and Interdisciplinary Center for Dynamics of Complex Systems, University of Potsdam, D-14415 Potsdam, Germany

Electronic mail: marwan@pik-potsdam.de

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Jesper Skovhus Thomsen,

Department of Connective Tissue Biology, Institute of Anatomy, University of Aarhus, DK-8000, Aarhus C, Denmark

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Peter Saparin,

Perceptive Informatics, a PAREXEL Technology Company, Am Bahnhof Westend 15, D-14059 Berlin, Germany

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First published: 11 August 2011
Citations: 4

Abstract

Purpose:

We present a new morphometric measure of trabecular bone microarchitecture, calledmean node strength (NdStr), which is part of a newly developed approach called long range node-strut analysis. Our general aim is to describe and quantify the apparent “latticelike” microarchitecture of the trabecular bone network.

Methods:

Similar in some ways to the topological node-strut analysis introduced by Garrahanet al. [J. Microsc. 142, 341–349 (1986)], our method is distinguished by an emphasis on long-range trabecular connectivity. Thus, while the topological classification of a pixel (after skeletonization) as a node, strut, or terminus, can be determined from the 3 × 3 neighborhood of that pixel, our method, which does not involve skeletonization, takes into account a much larger neighborhood. In addition, rather than giving a discrete classification of each pixel as a node, strut, or terminus, our method produces a continuous variable, node strength. The node strength is averaged over a region of interest to produce the mean node strength of the region.

Results:

We have applied our long range node-strut analysis to a set of 26 high-resolution peripheral quantitative computed tomography (pQCT) axial images of human proximal tibiae acquired 17 mm below the tibial plateau. We found that NdStr has a strong positive correlation with trabecular volumetric bone mineral density (BMD). After an exponential transformation, we obtain a Pearson's correlation coefficient ofr = 0.97. Qualitative comparison of images with similar BMD but with very different NdStr values suggests that the latter measure has successfully quantified the prevalence of the “latticelike” microarchitecture apparent in the image. Moreover, we found a strong correlation (r = 0.62) between NdStr and the conventional node-terminus ratio (Nd/Tm) of Garrahan et al. The Nd/Tm ratios were computed using traditional histomorphometry performed on bone biopsies obtained at the same location as the pQCT scans.

Conclusions:

The newly introduced morphometric measure allows a quantitative assessment of the long-range connectivity of trabecular bone. One advantage of this method is that it is based on pQCT images that can be obtained noninvasively from patients, i.e., without having to obtain a bone biopsy from the patient.