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Technical Brief

The Effect of Quantitative Computed Tomography Acquisition Protocols on Bone Mineral Density Estimation

[+] Author and Article Information
Hugo Giambini

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

Dan Dragomir-Daescu

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

Paul M. Huddleston

Biomechanics Laboratory,
Division of Orthopedic Research,
Department of Orthopedic Surgery,
Mayo Clinic,
Rochester, MN 55905
e-mail: huddleston.paul@mayo.edu

Jon J. Camp

Biomedical Imaging Resource,
Department of Radiology,
Mayo Clinic College of Medicine,
Rochester, MN 55905
e-mail: camp.jon@mayo.edu

Kai-Nan An

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

Ahmad Nassr

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

Manuscript received April 8, 2015; final manuscript received September 8, 2015; published online September 30, 2015. Assoc. Editor: Joel D. Stitzel.

J Biomech Eng 137(11), 114502 (Sep 30, 2015) (6 pages) Paper No: BIO-15-1155; doi: 10.1115/1.4031572 History: Received April 08, 2015; Revised September 08, 2015; Accepted September 09, 2015

Osteoporosis is characterized by bony material loss and decreased bone strength leading to a significant increase in fracture risk. Patient-specific quantitative computed tomography (QCT) finite element (FE) models may be used to predict fracture under physiological loading. Material properties for the FE models used to predict fracture are obtained by converting grayscale values from the CT into volumetric bone mineral density (vBMD) using calibration phantoms. If there are any variations arising from the CT acquisition protocol, vBMD estimation and material property assignment could be affected, thus, affecting fracture risk prediction. We hypothesized that material property assignments may be dependent on scanning and postprocessing settings including voltage, current, and reconstruction kernel, thus potentially having an effect in fracture risk prediction. A rabbit femur and a standard calibration phantom were imaged by QCT using different protocols. Cortical and cancellous regions were segmented, their average Hounsfield unit (HU) values obtained and converted to vBMD. Estimated vBMD for the cortical and cancellous regions were affected by voltage and kernel but not by current. Our study demonstrated that there exists a significant variation in the estimated vBMD values obtained with different scanning acquisitions. In addition, the large noise differences observed utilizing different scanning parameters could have an important negative effect on small subregions containing fewer voxels.

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Figures

Grahic Jump Location
Fig. 3

Sample regression curves used for vBMD estimation obtained from the different QCT scanning acquisitions

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Fig. 2

Segmented image showing the masked femur and a three-dimensional representation of the cancellous and cortical volumetric regions. Individual rods from the calibration phantom are also shown.

Grahic Jump Location
Fig. 1

Computed tomography images showing the calibration phantom and the femur using two different acquisition protocols. (a) B70 kernel, 0.6 mm isotropic slice thickness, 450 mAs, and 140 kVp. (b) B30 kernel, 0.6 mm isotropic slice thickness, 110 mAs, and 80 kVp.

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