Methods Inf Med 2012; 51(05): 398-405
DOI: 10.3414/ME11-02-0034
Focus Theme – Original Articles
Schattauer GmbH

Dual-energy CT-based Assessment of the Trabecular Bone in Vertebrae

S. Wesarg
1   Cognitive Computing and Medical Imaging, Fraunhofer IGD, Darmstadt, Germany
,
M. Kirschner
2   Interactive Graphics Systems Group, Technische Universität Darmstadt, Germany
,
M. Becker
2   Interactive Graphics Systems Group, Technische Universität Darmstadt, Germany
,
M. Erdt
1   Cognitive Computing and Medical Imaging, Fraunhofer IGD, Darmstadt, Germany
,
K. Kafchitsas
3   Clinic and Policlinic for Orthopedics and Orthopedic Surgery, Universitätsmedizin Mainz, Germany
,
M. F. Khan
4   Institute for Diagnostic and Interventional Radiology, Goethe University, Frankfurt/M., Germany
› Author Affiliations
Further Information

Publication History

received:14 October 2011

accepted:24 May 2012

Publication Date:
20 January 2018 (online)

Summary

Background: Osteoporosis can cause severe fractures of bone structures. One important indicator for pathology is a lowered bone mineral density (BMD) – conventionally assessed by dual-energy X-ray absorptiometry (DXA). Dual-energy CT (DECT) – being an alternative that is increasingly used in the clinics – allows the computation of the spatial BMD distribution.

Objectives: Using DECT, the trabecular bone of vertebrae is examined. Several analysis methods for revealing the bone density distribution as well as appropriate visualization methods for detecting regions of lowered BMD are needed for computer-assisted diagnosis (CAD) of osteoporosis. The hypothesis that DECT is better suited than DXA for the computation of local BMD is investigated.

Methods: Building on a model of the interaction of X-rays with bone tissue, novel methods for assessing the spatial structure of the trabecular bone are presented. CAD of DECT image data is facilitated by segmenting the regions of interest interactively and with an Active Shape Model, respectively. The barycentric space of fractional volumes is introduced as a novel means for analyzing bone constitution. For 29 cadaver specimens, DECT as well as DXA has been examined. BMD values derived from both modalities are compared to local force measurements. In addition, clinical data from two patients who underwent DECT scanning for a different reason is analyzed retrospectively.

Results: A novel automated delineation method for vertebrae has been successfully applied to DECT data sets. It is shown that localized BMD measurements based on DECT show a stronger linear correlation (R2 = 0.8242, linear regression) to local force measurements than density values derived from DXA (R2 = 0.4815).

Conclusions: DECT based BMD assessment is a method to extend the usage of increasingly acquired DECT image data. The developed DECT based analysis methods in conjunction with the visualization provide more detailed information for both, the radiologist and the orthopedist, compared to standard DXA based analysis.

 
  • References

  • 1 WHO. WHO Scientific Group on the Assessment of Osteoporosis at Primary Health Care Level. Summary Meeting Report. Brussels, Belgium: 2007
  • 2 Hertzman JO. What evidence is there for the prevention and screening of osteoporosis?. HEN report. 2006
  • 3 Blake GM, Fogelman I. The clinical role of dual energy x&hypen;ray absorptiometry. 2009: 406-414.
  • 4 Engelke K, Adams JE, Armbrecht G, Augat P, Bogado CE, Bouxsein ML. et al. Clinical Use of Quantitative Computed Tomography and Peripheral Quantitative Computed Tomography in the Management of Osteoporosis in Adults. The 2007 ISCD Official Positions. 2008: 123-162.
  • 5 Adams JE. Quantitative computed tomography. 2009: 415-424.
  • 6 Flohr TG, McCollough CH, Bruder H, Petersilka M, Gruber K, Süß C. et al. First performance evaluation of a dual-source CT (DSCT) system. 2006: 256-268.
  • 7 Kalender WA, Perman WH, Vetter JR, Klotz E. Evaluation of a prototype dual-energy computed tomographic apparatus. I. Phantom studies. Medical Physics. 1986: 334-339.
  • 8 Vetter JR, Perman WH, Kalender WA, Mazess RB, Holden JE. Evaluation of a prototype dual-energy computed tomographic apparatus. II. Determination of vertebral bone mineral content. 1986: 340-343.
  • 9 Nickoloff EL, Feldman F, Atherton JV. Bone mineral assessment: new dual energy CT approach. 1988: 223-228.
  • 10 van Kuijk C, Grashuis JL, Steenbeek JC, Schütte HE, Trouerbach WT. Evaluation of postprocessing dual-energy methods in quantitative computed tomography. Part 2. Practical aspects. 1990: 882-889.
  • 11 Wesarg S, Erdt M, Kafchitsas K, Khan MF. CAD of Osteoporosis in Vertebrae Using Dual-energy CT. In. Dillon Tea. editor Proc. of 23rd IEEE CBMS. IEEE Computer Society; 2010: 358-363.
  • 12 Erdt M, Kirschner M, Wesarg S. Simultaneous Segmentation and Correspondence Establishment for Statistical Shape Models. In. Magnenat-Thalmann N. Modelling the Physiological Human: Second 3D Physiological Human Workshop. Berlin, Heidelberg: Springer; 2009: 25-35.
  • 13 Lorensen WE, Cline HE. Marching Cubes: A high resolution 3D surface construction algorithm. 1987: 163-169.
  • 14 Cootes TF, Taylor CJ, Cooper DH, Graham J. Active Shape Models - Their training and application. 1995: 38-59.
  • 15 Becker M, Kirschner M, Furhmann S, Wesarg S. Automatic Construction of Statistical Shape Models for Vertebrae. In Medical Image Computing and Computer Assisted Intervention. Toronto: Springer; 2011: 500-507.
  • 16 Erickson J, Whittlesey K. Greedy optimal homotopy and homology generators. In: 16th ACM-SIAM Symposium on Discrete Algorithms. Vancouver. 2005: 1038-1046.
  • 17 Tutte WT. How to draw a graph. 1963: 743-768.
  • 18 Degener P, Meseth J, Klein R. An adaptable surface parameterization method. In: 12th International Meshing Roundtable. Santa Fe. 2003: 201-213.
  • 19 Fuhrmann S, Ackermann J, Kalbe T, Goesele M. Direct Resampling for isotropic surface remeshing. In Vision Modeling and Visualization. Siegen: Eurographics Association; 2010: 9-16.
  • 20 Yoganandan N, Pintar FA, Stemper BD, Baisden JL, Aktay R, Shender BS. et al. Trabecular bone density of male human cervical and lumbar vertebrae. Bone: 2006: 336-344.
  • 21 Kindlmann G, Weinstein D, Hart D. Strategies for direct volume rendering of diffusion tensor fields. 2000: 124-138.
  • 22 Klinder T, Ostermann J, Ehm M, Franz A, Kneser R, Lorenz C. Automated model-based vertebra detection, identification, and segmentation in CT images. 2009: 471-482.