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The Effect Of Quantitative Computed Tomography Acquisition Protocols On Bone Mineral Density Estimation.

H. Giambini, D. Dragomir-Daescu, P. Huddleston, J. Camp, K. An, A. Nassr
Published 2015 · Engineering, Medicine

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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.
This paper references
Osteoporosis: assessment by quantitative computed tomography.
H. Genant (1985)
Quantitative Computed Tomography-Based Predictions of Vertebral Strength in Anterior Bending
J. Buckley (2007)
Distribution of anterior cortical shear strain after a thoracic wedge compression fracture.
M. Kayanja (2004)
Quantitative computed tomography for prediction of vertebral fracture risk.
C. Cann (1985)
Epidemiology of vertebral fractures: implications for vertebral augmentation.
L. J. Melton (2006)
Trabecular bone modulus-density relationships depend on anatomic site.
E. Morgan (2003)
Assessment of vertebral bone mineral density using volumetric quantitative CT.
T. Lang (1999)
Assessment of vertebral fracture risk and therapeutic effects of alendronate in postmenopausal women using a quantitative computed tomography-based nonlinear finite element method
K. Imai (2008)
Predicting the compressive mechanical behavior of bone.
T. Keller (1994)
Prediction of new clinical vertebral fractures in elderly men using finite element analysis of CT scans
Xiang Wang (2012)
Quantitative CT for determination of bone mineral density: a review.
C. Cann (1988)
Vertebral Osteoporosis and Trabecular Bone Quality
P. M. Donnell (2006)
Robust QCT/FEA Models of Proximal Femur Stiffness and Fracture Load During a Sideways Fall on the Hip
D. Dragomir-Daescu (2010)
Prediction of Vertebral Strength Under Loading Conditions Occurring in Activities of Daily Living Using a Computed Tomography-Based Nonlinear Finite Element Method
T. Matsumoto (2009)
Quantitative computed tomography estimates of the mechanical properties of human vertebral trabecular bone.
D. Kopperdahl (2002)
Estimation of Young's modulus in swine cortical bone using quantitative computed tomography.
N. Kato (1998)
Osteoporosis Changes the Amount of Vertebral Trabecular Bone at Risk of Fracture but Not the Vertebral Load Distribution
J. Homminga (2001)
Nonlinear Finite Element Model Predicts Vertebral Bone Strength and Fracture Site
K. Imai (2006)
Analysis of strength and failure pattern of human proximal femur using quantitative computed tomography (QCT)-based finite element method.
Majid Mirzaei (2014)
The osteoporotic vertebral structure is well adapted to the loads of daily life, but not to infrequent "error" loads.
J. Homminga (2004)
The unreliability of CT numbers as absolute values.
C. Levi (1982)
Quantitative computed tomography-based finite element analysis predictions of femoral strength and stiffness depend on computed tomography settings.
D. Dragomir-Daescu (2015)
Quantitative CT applications: comparison of current scanners.
C. Cann (1987)
Risk of vertebral insufficiency fractures in relation to compressive strength predicted by quantitative computed tomography.
M. Biggemann (1991)
Estimation of mechanical properties of cortical bone by computed tomography.
S. Snyder (1991)
Prediction of strength and strain of the proximal femur by a CT-based finite element method.
M. Bessho (2007)
Measurement of Image Quality in CT Images Reconstructed with Different Kernels
Keun Jo Jang (2011)
On prediction of the strength levels and failure patterns of human vertebrae using quantitative computed tomography (QCT)-based finite element method.
Majid Mirzaei (2009)
The associations between QCT-based vertebral bone measurements and prevalent vertebral fractures depend on the spinal locations of both bone measurement and fracture
D. Anderson (2013)

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Quantitative Computed Tomography (QCT) derived Bone Mineral Density (BMD) in finite element studies: a review of the literature
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Practical considerations for obtaining high quality quantitative computed tomography data of the skeletal system.
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The Influence of Reconstruction Kernel on Bone Mineral and Strength Estimates Using Quantitative Computed Tomography and Finite Element Analysis.
Andrew S Michalski (2019)
What is the future of patient-specific vertebral fracture prediction?
Hugo Giambini (2017)
Scanner influence on the mechanical response of QCT-based finite element analysis of long bones.
Y. Katz (2019)
Effect of CT imaging on the accuracy of the finite element modelling in bone
E. Benca (2020)
Methods for Post Hoc Quantitative Computed Tomography Bone Density Calibration: Phantom-Only and Regression.
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Quantitative Computed Tomography Protocols Affect Material Mapping and Quantitative Computed Tomography-Based Finite-Element Analysis Predicted Stiffness.
H. Giambini (2016)
An Analysis by Synthesis Approach for Automatic Vertebral Shape Identification in Clinical QCT
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