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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)
10.1097/01.brs.0000260979.98101.9c
Quantitative Computed Tomography-Based Predictions of Vertebral Strength in Anterior Bending
J. Buckley (2007)
10.1016/J.SPINEE.2003.07.003
Distribution of anterior cortical shear strain after a thoracic wedge compression fracture.
M. Kayanja (2004)
10.1016/8756-3282(85)90399-0
Quantitative computed tomography for prediction of vertebral fracture risk.
C. Cann (1985)
10.1016/J.ACRA.2006.01.005
Epidemiology of vertebral fractures: implications for vertebral augmentation.
L. J. Melton (2006)
10.1016/S0021-9290(03)00071-X
Trabecular bone modulus-density relationships depend on anatomic site.
E. Morgan (2003)
10.1097/00004728-199901000-00027
Assessment of vertebral bone mineral density using volumetric quantitative CT.
T. Lang (1999)
10.1007/s00198-008-0750-8
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)
10.1016/0021-9290(94)90056-6
Predicting the compressive mechanical behavior of bone.
T. Keller (1994)
10.1002/jbmr.1539
Prediction of new clinical vertebral fractures in elderly men using finite element analysis of CT scans
Xiang Wang (2012)
10.1148/radiology.166.2.3275985
Quantitative CT for determination of bone mineral density: a review.
C. Cann (1988)
10.1007/s10439-006-9239-9
Vertebral Osteoporosis and Trabecular Bone Quality
P. M. Donnell (2006)
10.1007/s10439-010-0196-y
Robust QCT/FEA Models of Proximal Femur Stiffness and Fracture Load During a Sideways Fall on the Hip
D. Dragomir-Daescu (2010)
10.1097/BRS.0b013e3181a55636
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)
10.1016/S0736-0266(01)00185-1
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)
10.1097/00007632-200107150-00010
Osteoporosis Changes the Amount of Vertebral Trabecular Bone at Risk of Fracture but Not the Vertebral Load Distribution
J. Homminga (2001)
10.1097/01.brs.0000225993.57349.df
Nonlinear Finite Element Model Predicts Vertebral Bone Strength and Fracture Site
K. Imai (2006)
10.1016/j.bone.2014.04.007
Analysis of strength and failure pattern of human proximal femur using quantitative computed tomography (QCT)-based finite element method.
Majid Mirzaei (2014)
10.1016/J.BONE.2003.12.001
The osteoporotic vertebral structure is well adapted to the loads of daily life, but not to infrequent "error" loads.
J. Homminga (2004)
10.2214/ajr.139.3.443
The unreliability of CT numbers as absolute values.
C. Levi (1982)
10.1016/j.jbiomech.2014.09.016
Quantitative computed tomography-based finite element analysis predictions of femoral strength and stiffness depend on computed tomography settings.
D. Dragomir-Daescu (2015)
10.1148/radiology.162.1.3786773
Quantitative CT applications: comparison of current scanners.
C. Cann (1987)
10.1016/0720-048X(91)90047-Y
Risk of vertebral insufficiency fractures in relation to compressive strength predicted by quantitative computed tomography.
M. Biggemann (1991)
10.1002/jor.1100090315
Estimation of mechanical properties of cortical bone by computed tomography.
S. Snyder (1991)
10.1016/J.JBIOMECH.2006.08.003
Prediction of strength and strain of the proximal femur by a CT-based finite element method.
M. Bessho (2007)
10.3938/JKPS.58.334
Measurement of Image Quality in CT Images Reconstructed with Different Kernels
Keun Jo Jang (2011)
10.1016/j.jbiomech.2009.04.042
On prediction of the strength levels and failure patterns of human vertebrae using quantitative computed tomography (QCT)-based finite element method.
Majid Mirzaei (2009)
10.1007/s00198-013-2452-0
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)



This paper is referenced by
10.1089/ten.TEC.2016.0078
Noninvasive Failure Load Prediction of Vertebrae with Simulated Lytic Defects and Biomaterial Augmentation.
H. Giambini (2016)
10.1002/adhm.201801353
Architectural Design of 3D Printed Scaffolds Controls the Volume and Functionality of Newly Formed Bone
A. Entezari (2019)
10.1186/s40634-016-0072-2
Quantitative Computed Tomography (QCT) derived Bone Mineral Density (BMD) in finite element studies: a review of the literature
N. Knowles (2016)
10.1002/jor.23890
Effect of different CT scanners and settings on femoral failure loads calculated by finite element models
Florieke Eggermont (2018)
10.1016/j.msec.2019.110406
Effects of immediately static loading on osteointegration and osteogenesis around 3D-printed porous implant: A histological and biomechanical study.
T S Yu (2020)
10.1007/s10439-020-02595-w
Density-Dependent Material and Failure Criteria Equations Highly Affect the Accuracy and Precision of QCT/FEA-Based Predictions of Osteoporotic Vertebral Fracture Properties
M. Prado (2020)
10.1016/j.jmir.2018.10.002
Design and Validation of Synchronous QCT Calibration Phantom: Practical Methodology.
Malakeh Malekzadeh (2019)
10.1016/j.jos.2018.04.005
Biomechanical analysis of supra-acetabular insufficiency fracture using finite element analysis.
Hidetatsu Tanaka (2018)
10.1371/journal.pone.0220564
Calibration with or without phantom for fracture risk prediction in cancer patients with femoral bone metastases using CT-based finite element models
F. Eggermont (2019)
10.1016/j.bone.2018.01.013
Practical considerations for obtaining high quality quantitative computed tomography data of the skeletal system.
Karen L. Troy (2018)
10.1080/10255842.2020.1754808
Effect of metastatic lesion size and location on the load-bearing capacity of vertebrae using an optimized ash density-modulus equation
S. Saldarriaga (2020)
10.1007/978-3-319-75583-0_3
Physical Simulators and Replicators in Endovascular Neurosurgery Training
Chander Sadasivan (2018)
10.15761/docr.1000254
Potential for estimation of Young's modulus based on computed tomography numbers in bone: A validation study using a nano-indentation test on murine maxilla
Hideaki Inagawa (2018)
10.1016/j.jocd.2017.09.001
The Influence of Reconstruction Kernel on Bone Mineral and Strength Estimates Using Quantitative Computed Tomography and Finite Element Analysis.
Andrew S Michalski (2019)
10.1053/J.SEMSS.2017.09.008
What is the future of patient-specific vertebral fracture prediction?
Hugo Giambini (2017)
10.1016/j.jbiomech.2019.01.049
Scanner influence on the mechanical response of QCT-based finite element analysis of long bones.
Y. Katz (2019)
10.1186/s41747-020-00180-3
Effect of CT imaging on the accuracy of the finite element modelling in bone
E. Benca (2020)
10.1115/1.4040122
Methods for Post Hoc Quantitative Computed Tomography Bone Density Calibration: Phantom-Only and Regression.
Jacob M. Reeves (2018)
10.1115/1.4034172
Quantitative Computed Tomography Protocols Affect Material Mapping and Quantitative Computed Tomography-Based Finite-Element Analysis Predicted Stiffness.
H. Giambini (2016)
10.1007/978-3-030-12939-2_6
An Analysis by Synthesis Approach for Automatic Vertebral Shape Identification in Clinical QCT
Stefan Reinhold (2018)
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