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Research Papers

Quantitative Computed Tomography Protocols Affect Material Mapping and Quantitative Computed Tomography-Based Finite-Element Analysis Predicted Stiffness

[+] Author and Article Information
Hugo Giambini

Biomaterials and Tissue Engineering Laboratory,
Department of Orthopedic Surgery,
Mayo Clinic College of Medicine,
Rochester, MN 55905
e-mail: giambini.hugo@mayo.edu

Dan Dragomir-Daescu

Division of Engineering,
Mayo Clinic College of Medicine,
Rochester, MN 55905
e-mail: dragomirdaescu.dan@mayo.edu

Ahmad Nassr

Division of Orthopedic Research,
Department of Orthopedic Surgery,
Mayo Clinic,
Rochester, MN 55905
e-mail: nassr.ahmad@mayo.edu

Michael J. Yaszemski

Biomaterials and Tissue Engineering Laboratory,
Department of Orthopedic Surgery,
Mayo Clinic College of Medicine,
Rochester, MN 55905
e-mail: yaszemski.michael@mayo.edu

Chunfeng Zhao

Biomechanics Laboratory,
Division of Orthopedic Research,
Mayo Clinic,
Rochester, MN 55905
e-mail: zhao.chunfeng@mayo.edu

1Corresponding author.

Manuscript received February 23, 2016; final manuscript received July 6, 2016; published online July 29, 2016. Assoc. Editor: Joel D. Stitzel.

J Biomech Eng 138(9), 091003 (Jul 29, 2016) (7 pages) Paper No: BIO-16-1069; doi: 10.1115/1.4034172 History: Received February 23, 2016; Revised July 06, 2016

Quantitative computed tomography-based finite-element analysis (QCT/FEA) has become increasingly popular in an attempt to understand and possibly reduce vertebral fracture risk. It is known that scanning acquisition settings affect Hounsfield units (HU) of the CT voxels. Material properties assignments in QCT/FEA, relating HU to Young's modulus, are performed by applying empirical equations. The purpose of this study was to evaluate the effect of QCT scanning protocols on predicted stiffness values from finite-element models. One fresh frozen cadaveric torso and a QCT calibration phantom were scanned six times varying voltage and current and reconstructed to obtain a total of 12 sets of images. Five vertebrae from the torso were experimentally tested to obtain stiffness values. QCT/FEA models of the five vertebrae were developed for the 12 image data resulting in a total of 60 models. Predicted stiffness was compared to the experimental values. The highest percent difference in stiffness was approximately 480% (80 kVp, 110 mAs, U70), while the lowest outcome was ∼1% (80 kVp, 110 mAs, U30). There was a clear distinction between reconstruction kernels in predicted outcomes, whereas voltage did not present a clear influence on results. The potential of QCT/FEA as an improvement to conventional fracture risk prediction tools is well established. However, it is important to establish research protocols that can lead to results that can be translated to the clinical setting.

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References

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Figures

Grahic Jump Location
Fig. 1

QCT/FEA process. Assignment of material properties (step 4) to the finite elements of the models is based on Hounsfield unit values from the CT images and estimated bone mineral densities obtained using a calibration phantom.

Grahic Jump Location
Fig. 2

Axial view of a vertebral body using high resolution scans (140 kVp, 200 mAs) reconstructed with (a) sharp (U70) and (b) smooth (U30) kernels. The smooth reconstruction kernel shows a lower image quality and contrast compared to the sharp kernel. (c–d) Same finite-element mesh was imported into the different CT image data for material properties assignment.

Grahic Jump Location
Fig. 3

Estimated vBMD versus Hounsfield unit values. Calibration curves obtained from the calibration phantoms for varying scanning and image reconstruction algorithms. Hounsfield unit values are extrapolated to 3000 [HU] to represent cortical values. Solid and dotted lines represent sharp (U70) and soft (U30) kernels, respectively.

Grahic Jump Location
Fig. 4

Predicted and experimental measured stiffness. Stiffness difference for all models versus predicted stiffness based on (a) voltage parameters and (b) reconstruction kernels. Percent different for all models and vertebral level based on (c) voltage parameters and (d) reconstruction kernels.

Grahic Jump Location
Fig. 5

Number of elements per material bin for two representative vertebrae. The thoracic vertebra shows a smaller number of total elements compared to the lumbar vertebra as described by the relative frequency at each bin. Element density and Young's modulus for each scan will vary according to the different values of Hounsfield units acquired at each bin and the different calibration equations corresponding to each combination of scan parameters (Table 2).

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