Technical Brief

Methods for Post Hoc Quantitative Computed Tomography Bone Density Calibration: Phantom-Only and Regression

[+] Author and Article Information
Jacob M. Reeves

Department of Mechanical Engineering,
Western University Canada,
1151 Richmond Street,
London, ON N6A3K7, Canada
e-mail: jreeves3@uwo.ca

Nikolas K. Knowles

Department of Biomedical Engineering,
Western University Canada,
1151 Richmond Street,
London, ON N6A3K7, Canada
e-mail: nknowle@uwo.ca

George S. Athwal

Roth|McFarlane Hand and Upper Limb Centre,
268 Grosvenor Street,
London, ON N6A4V2, Canada
e-mail: gathwal@uwo.ca

James A. Johnson

Department of Mechanical Engineering,
Western University Canada,
1151 Richmond Street,
London, ON N6A3K7, Canada
e-mail: jajohnso@uwo.ca

Manuscript received July 20, 2017; final manuscript received April 21, 2018; published online May 24, 2018. Assoc. Editor: Joel D. Stitzel.

J Biomech Eng 140(9), 094501 (May 24, 2018) (6 pages) Paper No: BIO-17-1318; doi: 10.1115/1.4040122 History: Received July 20, 2017; Revised April 21, 2018

Quantitative computed tomography (qCT) relies on calibrated bone mineral density data. If a calibration phantom is absent from the CT scan, post hoc calibration becomes necessary. Scanning a calibration phantom after-the-fact and applying that calibration to uncalibrated scans has been used previously. Alternatively, the estimated density is known to vary with CT settings, suggesting that it may be possible to predict the calibration terms using CT settings. This study compares a novel CT setting regression method for post hoc calibration to standard and post hoc phantom-only calibrations. Five cadaveric upper limbs were scanned at 11 combinations of peak tube voltage and current (80–140 kV and 100–300 mA) with two calibration phantoms. Density calibrations were performed for the cadaver scans, and scans of the phantoms alone. Stepwise linear regression determined if the calibration equation terms were predictable using peak tube voltage and current. Peak tube voltage, but not current, was significantly correlated with regression calibration terms. Calibration equation slope was significantly related to the type of phantom (p < 0.001), calibration method (p = 0.026), and peak tube voltage (p < 0.001), but not current (p = 1.000). The calibration equation vertical intercept was significantly related to the type of phantom (p < 0.001), and peak tube voltage (p = 0.006), but not calibration method (p = 0.682), or current (p = 0.822). Accordingly, regression can correlate peak tube voltage with density calibration terms. Suggesting that, while standard qCT calibration is preferable, regression calibration may be an acceptable post hoc method when necessary.

Copyright © 2018 by ASME
Your Session has timed out. Please sign back in to continue.


Cong, A. , Buijs, J. O. D. , and Dragomir-Daescu, D. , 2011, “ In Situ Parameter Identification of Optimal Density-Elastic Modulus Relationships in Subject-Specific Finite Element Models of the Proximal Femur,” Med. Eng. Phys., 33(2), pp. 164–173. [CrossRef] [PubMed]
Keaveny, T. M. , McClung, M. R. , Wan, X. , Kopperdahl, D. L. , Mitlak, B. H. , and Krohn, K. , 2012, “ Femoral Strength in Osteoporotic Women Treated With Teriparatide or Alendronate,” Bone, 50(1), pp. 165–170. [CrossRef] [PubMed]
Keyak, J. H. , Sigurdsson, S. , Karlsdottir, G. S. , Oskarsdottir, D. , Sigmarsdottir, A. , Kornak, J. , Harris, T. B. , Sigurdsson, G. , Jonsson, B. Y. , Siggeirsdottir, K. , Eiriksdottir, G. , Gudnason, V. , and Lang, T. F. , 2013, “ Effect of Finite Element Model Loading Condition on Fracture Risk Assessment in Men and Women: The AGES-Reykjavik Study,” Bone, 57(1), pp. 18–29. [CrossRef] [PubMed]
Tawara, D. , Sakamoto, J. , Murakami, H. , Kawahara, N. , Oda, J. , and Tomita, K. , 2010, “ Mechanical Evaluation by Patient-Specific Finite Element Analyses Demonstrates Therapeutic Effects for Osteoporotic Vertebrae,” J. Mech. Behav. Biomed. Mater., 3(1), pp. 31–40. [CrossRef] [PubMed]
Kopperdahl, D. L. , Aspelund, T. , Hoffmann, P. F. , Sigurdsson, S. , Siggeirsdottir, K. , Harris, T. B. , Gudnason, V. , and Keaveny, T. M. , 2014, “ Assessment of Incident Spine and Hip Fractures in Women and Men Using Finite Element Analysis of CT Scans,” J. Bone Miner. Res., 29(3), pp. 570–580. [CrossRef] [PubMed]
Razfar, N. , Reeves, J. M. , Langohr, D. G. , Willing, R. , Athwal, G. S. , and Johnson, J. A. , 2016, “ Comparison of Proximal Humeral Bone Stresses Between Stemless, Short Stem, and Standard Stem Length: A Finite Element Analysis,” J. Shoulder Elbow Surg., 25(7), pp. 1076–1083. [CrossRef] [PubMed]
Dragomir-Daescu, D. , Op Den Buijs, J. , McEligot, S. , Dai, Y. , Entwistle, R. C. , Salas, C. , Melton, L. J. , Bennet, K. E. , Khosla, S. , and Amin, S. , 2011, “ Robust QCT/FEA Models of Proximal Femur Stiffness and Fracture Load During a Sideways Fall on the Hip,” Ann. Biomed. Eng., 39(2), pp. 742–755. [CrossRef] [PubMed]
Eberle, S. , Göttlinger, M. , and Augat, P. , 2013, “ An Investigation to Determine if a Single Validated Density-Elasticity Relationship Can Be Used for Subject Specific Finite Element Analyses of Human Long Bones,” Med. Eng. Phys., 35(7), pp. 875–883. [CrossRef] [PubMed]
Eberle, S. , Göttlinger, M. , and Augat, P. , 2013, “ Individual Density-Elasticity Relationships Improve Accuracy of Subject-Specific Finite Element Models of Human Femurs,” J. Biomech., 46(13), pp. 2152–2157. [CrossRef] [PubMed]
Haider, I. T. , Speirs, A. D. , and Frei, H. , 2013, “ Effect of Boundary Conditions, Impact Loading and Hydraulic Stiffening on Femoral Fracture Strength,” J. Biomech., 46(13), pp. 2115–2121. [CrossRef] [PubMed]
Kheirollahi, H. , and Luo, Y. , 2015, “ Assessment of Hip Fracture Risk Using Cross-Section Strain Energy Determined by QCT-Based Finite Element Modeling,” Biomed. Res. Int., p. e413839.
Campoli, G. , Bolsterlee, B. , van der Helm, F. , Weinans, H. , and Zadpoor, A. A. , 2014, “ Effects of Densitometry, Material Mapping and Load Estimation Uncertainties on the Accuracy of Patient-Specific Finite-Element Models of the Scapula,” J. R. Soc. Interface, 11(93), p. 20131146. [CrossRef] [PubMed]
Pomwenger, W. , Entacher, K. , Resch, H. , and Schuller-Götzburg, P. , 2014, “ Need for CT-Based Bone Density Modelling in Finite Element Analysis of a Shoulder Arthroplasty Revealed Through a Novel Method for Result Analysis,” Biomed. Tech., 59(5), pp. 421–430.
Taylor, W. R. , Roland, E. , Ploeg, H. , Hertig, D. , Klabunde, R. , Warner, M. D. , Hobatho, M. C. , Rakotomanana, L. , and Clift, S. E. , 2002, “ Determination of Orthotropic Bone Elastic Constants Using FEA and Modal Analysis,” J. Biomech., 35(6), pp. 767–773. [CrossRef] [PubMed]
Schileo, E. , Dall'Ara, E. , Taddei, F. , Malandrino, A. , Schotkamp, T. , Baleani, M. , and Viceconti, M. , 2008, “ An Accurate Estimation of Bone Density Improves the Accuracy of Subject-Specific Finite Element Models,” J. Biomech., 41(11), pp. 2483–2491. [CrossRef] [PubMed]
Keyak, J. H. , Lee, I. Y. , and Skinner, H. B. , 1994, “ Correlations Between Orthogonal Mechanical Properties and Density of Trabecular Bone: Use of Different Densitometric Measures,” J. Biomed. Mater. Res., 28(11), pp. 1329–1336. [CrossRef] [PubMed]
Giambini, H. , Dragomir-Daescu, D. , Huddleston, P. M. , Camp, J. J. , An, K.-N. , and Nassr, A. , 2015, “ The Effect of Quantitative Computed Tomography Acquisition Protocols on Bone Mineral Density Estimation,” ASME J. Biomech. Eng., 137(11), pp. 1–6. [CrossRef]
Faulkner, K. G. , Gluer, C. C. , Grampp, S. , and Genant, H. K. , 1993, “ Cross-Calibration of Liquid and Solid QCT Calibration Standards: Corrections to the UCSF Normative Data,” Osteoporos. Int., 3(1), pp. 36–42. [CrossRef] [PubMed]
Mindways Software, 2011, “ QCT PRO: Bone Mineral Densitometry Software CT Calibration Phantom,” Mindways Software Inc., Austin, TX, pp. 1–10.
Ashman, R. B. , 1989, “ Experimental Techniques,” Bone Mechanics, S. C. Cowin , ed., CRC Press, Boca Raton, FL, p. 76.
Bonnuci, E. , 2000, “ Basic Composition and Structure of Bone,” Mechanical Testing of Bone and the Bone-Implant Interface, Y. H. An and R. A. Draughn , eds., CRC Press, Boca Raton, FL, pp. 3–21.


Grahic Jump Location
Fig. 1

Process flow diagram, outlining the sequence of events for the phantom-only, regression and standard calibration methods

Grahic Jump Location
Fig. 2

Plots of ash density percent difference (relative to proper calibration equation terms) for both phantom-only and regression calibration methods

Grahic Jump Location
Fig. 3

Bland–Altman plots for density calibration equation slope and vertical intercept terms, comparing phantom-only and regression calibration methods



Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In