The quantification of changes in the trabecular bone structure induced by musculoskeletal diseases like osteoarthritis, osteoporosis, rheumatoid arthritis, and others by means of a texture analysis is a valuable tool which is expected to improve the diagnosis and monitoring of a disease. The reaction of texture parameters on different alterations in the architecture of the fine trabecular network and inherent imaging factors such as spatial resolution or image noise has to be understood in detail to ensure an accurate and reliable determination of the current bone state. Therefore, a digital model for the quantitative analysis of cancellous bone structures was developed. Five parameters were used for texture analysis: entropy, global and local inhomogeneity, local anisotropy, and variogram slope. Various generic structural changes of cancellous bone were simulated for different spatial resolutions. Additionally, the dependence of the texture parameters on tissue mineralization and noise was investigated. The present work explains changes in texture parameter outcomes based on structural changes originating from structure modifications and reveals that a texture analysis could provide useful information for a trabecular bone analysis even at resolutions below the dimensions of single trabeculae. 1. Introduction Quantitative computed tomography (QCT) is an advanced method to measure bone mineral density (BMD) in vivo at various skeletal sites [1]. However, to date the in vivo quantitative analysis of the trabecular bone network remains challenging. For peripheral locations such as the distal radius or tibia dedicated high resolution peripheral QCT (HR-pQCT) equipment with an isotropic spatial resolution of about 130？ m exists [2], but long scan times result in frequent motion artifacts and disturb the analysis of trabecular bone structure [3, 4]. Analysis of the trabecular network imaged with in vivo techniques, predominantly not only with CT but also with MRI or X-ray films, has received a fair amount of attention in the past. In the majority of reported studies, binarization methods were used to separate bone from soft tissue prior to the calculation of histomorphometric parameters [5–7]. However, the spatial resolution of almost all in vivo imaging modalities exceeds the diameter of single trabeculae of about 100？ m to 200？ m [8–10]. Therefore, binarization techniques were avoided in the present work. Instead, texture parameters directly calculated from the gray value distribution of datasets were used. The texture analysis of trabecular bone is not a
References
[1]
K. Engelke, C. Libanati, T. Fuerst, P. Zysset, and H. K. Genant, “Advanced CT based in vivo methods for the assessment of bone density, structure, and strength,” Current Osteoporosis Reports, vol. 11, no. 3, pp. 246–255, 2013.
[2]
A. J. Burghardt, T. M. Link, and S. Majumdar, “High-resolution computed tomography for clinical imaging of bone microarchitecture,” Clinical Orthopaedics and Related Research, vol. 469, no. 8, pp. 2179–2193, 2011.
[3]
J. Pialat, A. J. Burghardt, M. Sode, T. M. Link, and S. Majumdar, “Visual grading of motion induced image degradation in high resolution peripheral computed tomography: impact of image quality on measures of bone density and micro-architecture,” Bone, vol. 50, no. 1, pp. 111–118, 2012.
[4]
M. Sode, A. J. Burghardt, J.-B. Pialat, T. M. Link, and S. Majumdar, “Quantitative characterization of subject motion in HR-pQCT images of the distal radius and tibia,” Bone, vol. 48, no. 6, pp. 1291–1297, 2011.
[5]
C. Showalter, B. D. Clymer, B. Richmond, and K. Powell, “Three-dimensional texture analysis of cancellous bone cores evaluated at clinical CT resolutions,” Osteoporosis International, vol. 17, no. 2, pp. 259–266, 2006.
[6]
C. M. Phan, E. A. MacKlin, M. A. Bredella et al., “Trabecular structure analysis using C-arm CT: comparison with MDCT and flat-panel volume CT,” Skeletal Radiology, vol. 40, no. 8, pp. 1065–1072, 2011.
[7]
S. Hansen, J.-E. B. Jensen, L. Rasmussen, E. M. Hauge, and K. Brixen, “Effects on bone geometry, density, and microarchitecture in the distal radius but not the tibia in women with primary hyperparathyroidism: a case-control study using HR-pQCT,” Journal of Bone and Mineral Research, vol. 25, no. 9, pp. 1941–1947, 2010.
[8]
R. B. Martin, D. B. Burr, and N. A. Sharkey, Skeletal Tissue Mechanics, Springer, New York, NY, USA, 1998.
[9]
M. J. Turunen, V. Prantner, J. S. Jurvelin, H. Kroger, and H. Isaksson, “Composition and microarchitecture of human trabecular bone changes with age and differs between anatomical locations,” Bone, vol. 54, no. 1, pp. 118–125, 2013.
[10]
T. Hildebrand, A. Laib, R. Müller, J. Dequeker, and P. Rüegsegger, “Direct three-dimensional morphometric analysis of human cancellous bone: microstructural data from spine, femur, iliac crest, and calcaneus,” Journal of Bone and Mineral Research, vol. 14, no. 7, pp. 1167–1174, 1999.
[11]
T. Woloszynski, P. Podsiadlo, G. W. Stachowiak, M. Kurzynski, L. S. Lohmander, and M. Englund, “Prediction of progression of radiographic knee osteoarthritis using tibial trabecular bone texture,” Arthritis and Rheumatism, vol. 64, no. 3, pp. 688–695, 2012.
[12]
T. Baum, J. Carballido-Gamio, M. B. Huber et al., “Automated 3D trabecular bone structure analysis of the proximal femur-prediction of biomechanical strength by CT and DXA,” Osteoporosis International, vol. 21, no. 9, pp. 1553–1564, 2010.
[13]
E. A. Messent, J. C. Buckland-Wright, and G. M. Blake, “Fractal analysis of trabecular bone in knee osteoarthritis (OA) is a more sensitive marker of disease status than bone mineral density (BMD),” Calcified Tissue International, vol. 76, no. 6, pp. 419–425, 2005.
[14]
W. Tjong, G. J. Kazakia, A. J. Burghardt, and S. Majumdar, “The effect of voxel size on high-resolution peripheral computed tomography measurements of trabecular and cortical bone microstructure,” Medical Physics, vol. 39, no. 4, pp. 1893–1903.
[15]
N. Kim, J.-G. Lee, Y. Song, H. J. Kim, J. S. Yeom, and G. Cho, “Evaluation of MRI resolution affecting trabecular bone parameters: determination of acceptable resolution,” Magnetic Resonance in Medicine, vol. 67, no. 1, pp. 218–225, 2012.
[16]
M. Sode, A. J. Burghardt, R. A. Nissenson, and S. Majumdar, “Resolution dependence of the non-metric trabecular structure indices,” Bone, vol. 42, no. 4, pp. 728–736, 2008.
[17]
J. B. Pialat, N. Vilayphiou, S. Boutroy et al., “Local topological analysis at the distal radius by HR-pQCT: application to in vivo bone microarchitecture and fracture assessment in the OFELY study,” Bone, vol. 51, no. 3, pp. 362–368, 2012.
[18]
R. Rajamanohara, J. Robinson, J. Rymer, R. Patel, I. Fogelman, and G. M. Blake, “The effect of weight and weight change on the long-term precision of spine and hip DXA measurements,” Osteoporosis International, vol. 22, no. 5, pp. 1503–1512, 2011.
[19]
L. Forsén, G. K. R. Berntsen, H. E. Meyer, G. S. Tell, and V. F？nneb？, “Differences in precision in bone mineral density measured by SXA and DXA: the NOREPOS study,” European Journal of Epidemiology, vol. 23, no. 9, pp. 615–624, 2008.
[20]
P. K. Commean, J. A. Kennedy, K. A. Bahow et al., “Volumetric quantitative computed tomography measurement precision for volumes and densities of tarsal and metatarsal bones,” Journal of Clinical Densitometry, vol. 14, no. 3, pp. 313–320, 2011.
[21]
P. Zerfass, T. Lowitz, O. Museyko et al., “An integrated segmentation and analysis approach for QCT of the knee to determine subchondral bone mineral density and texture,” IEEE Transactions on Biomedical Engineering, vol. 59, no. 9, pp. 2449–2458, 2012.
[22]
K. Engelke, C. Libanati, Y. Liu et al., “Quantitative computed tomography (QCT) of the forearm using general purpose spiral whole-body CT scanners: accuracy, precision and comparison with dual-energy X-ray absorptiometry (DXA),” Bone, vol. 45, no. 1, pp. 110–118, 2009.
[23]
W. A. Kalender, Computed Tomography, Fundamentals, System Technology, Image Quality, Applications, Publicis, Erlangen, Germany, 3rd edition, 2011.
[24]
I. Singh, “The architecture of cancellous bone,” Journal of Anatomy, vol. 127, part 2, pp. 305–310, 1978.
[25]
V. Patel, A. S. Issever, A. Burghardt, A. Laib, M. Ries, and S. Majumdar, “MicroCT evaluation of normal and osteoarthritic bone structure in human knee specimens,” Journal of Orthopaedic Research, vol. 21, no. 1, pp. 6–13, 2003.
[26]
M. Ding, A. Odgaard, F. Linde, and I. Hvid, “Age-related variations in the microstructure of human tibial cancellous bone,” Journal of Orthopaedic Research, vol. 20, no. 3, pp. 615–621, 2002.
[27]
W. S. Rasband, “ImageJ, U. S. National Institutes of Health, Bethesda, Maryland, USA,” 1997–2012, http://imagej.nih.gov/ij/.
[28]
P. McDonnell, P. E. McHugh, and D. O'Mahoney, “Vertebral osteoporosis and trabecular bone quality,” Annals of Biomedical Engineering, vol. 35, no. 2, pp. 170–189, 2007.
[29]
C. Chappard, F. Peyrin, A. Bonnassie et al., “Subchondral bone micro-architectural alterations in osteoarthritis: a synchrotron micro-computed tomography study,” Osteoarthritis and Cartilage, vol. 14, no. 3, pp. 215–223, 2006.
[30]
J. Wolff, The Law of Bone Remodeling, Springer, Berlin, Germany, 1986.
[31]
S. Ihara, Information Theory for Continuous Systems, World Scientific, 1993.
[32]
J. J. Gough, J. T. Kent, P. O'Higgins, and L. T. Ellison, “Variogram methods for the analysis of bony trabecular shadows in plain radiographs,” International Journal of Bio-Medical Computing, vol. 35, no. 2, pp. 141–153, 1994.
[33]
L. G. E. Cox, B. Van Rietbergen, C. C. van Donkelaar, and K. Ito, “Bone structural changes in osteoarthritis as a result of mechanoregulated bone adaptation: a modeling approach,” Osteoarthritis and Cartilage, vol. 19, no. 6, pp. 676–682, 2011.
[34]
K. Kuroki, C. R. Cook, and J. L. Cook, “Subchondral bone changes in three different canine models of osteoarthritis,” Osteoarthritis and Cartilage, vol. 19, no. 9, pp. 1142–1149, 2011.
[35]
Z.-M. Zhang, Z.-C. Li, L.-S. Jiang, S.-D. Jiang, and L.-Y. Dai, “Micro-CT and mechanical evaluation of subchondral trabecular bone structure between postmenopausal women with osteoarthritis and osteoporosis,” Osteoporosis International, vol. 21, no. 8, pp. 1383–1390, 2010.
[36]
T. Y. Zhu, J. F. Griffith, L. Qin et al., “Structure and strength of the distal radius in female patients with rheumatoid arthritis: a case-control study,” Journal of Bone and Mineral Research, vol. 28, no. 4, pp. 794–806, 2012.
[37]
P. Roschger, E. P. Paschalis, P. Fratzl, and K. Klaushofer, “Bone mineralization density distribution in health and disease,” Bone, vol. 42, no. 3, pp. 456–466, 2008.
[38]
K. Nawrot-Wawrzyniak, F. Varga, A. Nader et al., “Effects of tumor-induced osteomalacia on the bone mineralization process,” Calcified Tissue International, vol. 84, no. 4, pp. 313–323, 2009.
