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Digital topological analysis of trabecular bone MR images and prediction of osteoporosis fractures

a topological analysis and trabecular bone technology, applied in the field of digital topological analysis, can solve the problems of inability to distinguish between trabecular and cortical bones, ignoring the role of structure as a contributor to mechanical competence, and inability to accurately define the 3d structure found in trabecular bone networks in connectivity analysis of 2d sections, etc., to achieve the effect of effective us

Inactive Publication Date: 2006-03-16
THE TRUSTEES OF THE UNIV OF PENNSYLVANIA
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

This approach effectively discriminates between patients with and without fractures, provides reproducible measurements of disease progression, and predicts fracture risk by quantifying topological parameters that correlate with bone strength, even when bone mineral density does not distinguish between groups.

Problems solved by technology

This method, however, does not distinguish between trabecular and cortical bone and ignores the role of structure as a contributor to mechanical competence.
Yet, it is well known that connectivity analysis of 2D sections does not accurately define the 3D structures found in trabecular bone networks (Odgaard et al., Bone 14:173-182 (1993).
Topologically, the network remains unaltered, but the scale properties have changed, which will result in changes to the mechanical properties.
However, classical topology in terms of the Euler number ignores the existence of plates and rods, and fails to provide information on the spatial distribution of connectivity.
Moreover, it may fail to detect the effect of osteoporotic erosion, as has been pointed out by Kinney et al., 1998.
However, it would increase as a result of perforation of plates, which increases the number of loops.
Therefore, the first Betti number can not necessarily detect osteoporotic bone erosion.
Nevertheless, none of the existing methods provide a reliable and efficient method for quantitatively characterizing the 3D architecture of cancellous bone networks, which are highly dependent on structural organization.
Nor does the prior art provide methods for assessing bone strength or for predicting fracture risk in vivo in patients suffering from osteoporosis or metabolic bone disorders.

Method used

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  • Digital topological analysis of trabecular bone MR images and prediction of osteoporosis fractures
  • Digital topological analysis of trabecular bone MR images and prediction of osteoporosis fractures
  • Digital topological analysis of trabecular bone MR images and prediction of osteoporosis fractures

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example 1

Validation and Applications of the Invention

[0121] Validation: Accuracy in Synthetic Images

[0122] Analysis of the synthetic images shows that for rod-like structures (i.e. curves) of greater than 0.9 voxel diameter the topological method has an absolute accuracy of 97%. However, at lower structural resolutions, accuracy was seen to decrease rapidly. The increase in classification errors at lower resolutions can be explained by partial volume mixing of low BVF voxels. Since a rod can intersect between one and four voxels within each slice, rods of 0.6 and 0.7 diameters result in maximum voxel BVFs ranging from 0.07 to 0.28 and 0.095 to 0.38, respectively. Recalling that the empirically determined BVF threshold for binarization was 0.25, one would expect that at low rod diameters, many of the low BVF voxels would disappear. At greater rod diameters, BVF increased sufficiently for more elements to survive after thresholding, resulting in increased classification accuracy. The reprodu...

example 2

Applications Ex Vivo to Bone Specimens: Prediction of Elastic Properties of Trabecular Bone

[0124] These properties were used to ascertain relationships with trabecular network competence of the bone specimens as represented by Young's modulus (YM) using regression analysis (JMP-IN, SAS Institute Inc., Cary, N.C.). Toward this goal Young's modulus data obtained in prior work on human wrist specimens were re-examined. The experiments are detailed in Hwang et al., 1997. In brief, YM was derived from stress-strain curves by uniaxial compression testing of cylindrical trabecular bone samples (9 mm diameter and length) drilled parallel to the long axis of the bone from the ultra-distal radius of 13 cadavers (Hwang et al., 1997).

[0125] The accuracy of topological classification is illustrated in a small region of an ex-vivo surface skeleton image, displayed as a voxel projection in FIG. 12, clearly showing the correct classification of surface and curve structures. The projection images ...

example 3

In Vivo Applications to Patients

[0129] Of particular medical significance is the application of topological analysis to patients with bone disease, which requires processing of images acquired in vivo. FIG. 14 shows three subjects of varying age and correspondingly different architectures. The images in FIG. 14(a) display both numerically and visually a pronounced rod-like architecture, unlike those in FIG. 14(c), which were predominantly plate-like. These differences in morphology can be understood considering that the images in FIG. 14(a) are from a 73 year old woman, whereas the images in FIG. 14(c) are from a 30 year old man. The images in FIG. 14(b), which display a mixed architecture of plates and rods, were from a 49 year-old woman. The topological parameters parallel the same trend seen visually, with surface-curve ratios of 5.9, 7.2, and 11.9, and erosion indices of 1.22, 1.07, and 0.72. The cross sections shown directly below each projection, show that the cylindrical cor...

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Abstract

The invention provides method, system and device for determining trabecular bone structure and strength by digital topological analysis, and offers, for the first time, a demonstration of superior associations between vertebral deformity and a number of architectural indices measured in the distal radius, thus permitting reliable and noninvasive detection and determination of the pathogenesis of osteoporosis. A preferred embodiment provides imaging in three dimension of a region of trabecular bone, after which the 3D image is converted into a skeletonized surface representation. Digital topological analysis is applied to the converted image, and each image voxel is identified and classified as a curve, a surface, or a junction; and then associated with microarchitectural indices of trabecular bone to quantitatively characterize the trabecular bone network. The invention is applicable in vivo, particularly on human subjects, or ex vivo.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS [0001] This application claims priority to U.S. Provisional Application No. 60 / 283,270 filed on Apr. 12, 2001.GOVERNMENT SUPPORT [0002] This work was supported in part by grants from the National Institutes of Health, grant numbers RO1 41443 and IT32-CA74781-02. The government may have certain rights in this invention.FIELD OF THE INVENTION [0003] This invention relates generally to the field of digital topological analysis (DTA) to derive structural parameters from trabecular bone images obtained by magnetic resonance imaging (MRI), computed tomography (CT), or other imaging technologies, and the use of these parameters to assess trabecular bone structure in patients at risk of developing fractures from osteoporosis or those suffering from metabolic bone disorders. BACKGROUND OF THE INVENTION [0004] Trabecular bone (TB) (also known as cancellous bone), which occurs in most of the axial skeleton and at locations toward the ends of the long bon...

Claims

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Application Information

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Patent Type & Authority Applications(United States)
IPC IPC(8): A61B5/05A61B5/055A61B5/103A61B6/00A61B8/00A61B17/56G06T5/00G06T7/00
CPCA61B5/055A61B5/417A61B5/7203A61B5/7264A61B5/7275G06T2207/30008G06T7/0081G06T7/602G06T2207/10088G06T2207/20044A61B6/505G06T7/11G06T7/62G16H50/20
Inventor WEHRLI, FELIX W.SAHA, PUNAM K.GOMBERG, BRYAN ROOS
Owner THE TRUSTEES OF THE UNIV OF PENNSYLVANIA
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