30 April 2004 In vivo assessment of trabecular bone architecture via three-dimensional tensor scale
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Abstract
Trabecular bone (TB) is a network of interconnected bony struts and plates mostly occurring near the joints of long bones and in the axial skeleton. Several bone diseases including osteoporosis are characterized by fragile bone and increased fracture risk and most fractures occur at locations rich in TB. The mechanical competence of this type of bone can only be partly explained by variations in the bone’s mass density (BMD), and there is now compelling evidence for the role of TB architecture in conferring skeletal strength. Our previous studies have demonstrated that a reduction in BMD is accompanied by a greatly magnified topological process that involves the conversion of trabeculae from plates to struts. Current in vivo technologies yield voxel sizes comparable to TB thickness resulting in inherently fuzzy representations and thus making in vivo assessment of TB architecture challenging. Most existing methods require binarization of an image into bone and non-bone regions and thus are associated with significant errors. Here, a new approach is presented for assessing TB architecture (e.g. classification of plates versus struts) using 3D tensor scale - a local morphometric index - that (1) obviates the need for binary segmentation and is applicable to grayscale bone volume fraction images, and (2) provides precise topological classification over the continuum between a perfect rod and a perfect plate. At any TB location p, tensor scale is the parametric representation of the largest ellipsoid that is centered at p and contained inside the bone region. Accuracy and reproducibility of the method under varying voxel size, and image rotation is presented and its applicability on TB images at in vivo MR resolution is demonstrated.
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Punam K. Saha, Punam K. Saha, Felix W. Wehrli, Felix W. Wehrli, } "In vivo assessment of trabecular bone architecture via three-dimensional tensor scale", Proc. SPIE 5369, Medical Imaging 2004: Physiology, Function, and Structure from Medical Images, (30 April 2004); doi: 10.1117/12.535352; https://doi.org/10.1117/12.535352
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