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Finite Element Analysis-Based Vertebral Bone Strength Prediction Using MDCT Data: How Low Can We Go?

N. M. Rayudu, K. Subburaj, K. Mei, M. Dieckmeyer, J. Kirschke, P. Noël, Thomas Baum
Published 2020 · Medicine

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Objective: To study the impact of dose reduction in MDCT images through tube current reduction or sparse sampling on the vertebral bone strength prediction using finite element (FE) analysis for fracture risk assessment. Methods: Routine MDCT data covering lumbar vertebrae of 12 subjects (six male; six female; 74.70 ± 9.13 years old) were included in this study. Sparsely sampled and virtually reduced tube current–based MDCT images were computed using statistical iterative reconstruction (SIR) with reduced dose levels at 50, 25, and 10% of the tube current and original projections, respectively. Subject-specific static non-linear FE analyses were performed on vertebra models (L1, L2, and L3) 3-D-reconstructed from those dose-reduced MDCT images to predict bone strength. Coefficient of correlation (R2), Bland-Altman plots, and root mean square coefficient of variation (RMSCV) were calculated to find the variation in the FE-predicted strength at different dose levels, using high-intensity dose-based strength as the reference. Results: FE-predicted failure loads were not significantly affected by up to 90% dose reduction through sparse sampling (R2 = 0.93, RMSCV = 8.6% for 50%; R2 = 0.89, RMSCV = 11.90% for 75%; R2 = 0.86, RMSCV = 11.30% for 90%) and up to 50% dose reduction through tube current reduction method (R2 = 0.96, RMSCV = 12.06%). However, further reduction in dose with the tube current reduction method affected the ability to predict the failure load accurately (R2 = 0.88, RMSCV = 22.04% for 75%; R2 = 0.43, RMSCV = 54.18% for 90%). Conclusion: Results from this study suggest that a 50% radiation dose reduction through reduced tube current and a 90% radiation dose reduction through sparse sampling can be used to predict vertebral bone strength. Our findings suggest that the sparse sampling–based method performs better than the tube current–reduction method in generating images required for FE-based bone strength prediction models.
This paper references
Is multidetector CT-based bone mineral density and quantitative bone microstructure assessment at the spine still feasible using ultra-low tube current and sparse sampling?
K. Mei (2017)
Combining Ordered Subsets and Momentum for Accelerated X-Ray CT Image Reconstruction
D. Kim (2015)
Quantitative computed tomography and opportunistic bone density screening by dual use of computed tomography scans
A. Brett (2015)
Fitzpatrick JM, Sonka M, ediors
Fessler JA. Statistical image reconstruction methods for t In (2000)
MDCT-based Finite Element Analysis of Vertebral Fracture Risk: What Dose is Needed?
D. Anitha (2018)
Design and Validation of Synchronous QCT Calibration Phantom: Practical Methodology.
Malakeh Malekzadeh (2019)
The Association of Radiographically Detected Vertebral Fractures with Back Pain and Function: A Prospective Study
M. Nevitt (1998)
Comparison of image quality from filtered back projection, statistical iterative reconstruction, and model-based iterative reconstruction algorithms in abdominal computed tomography
Yu Kuo (2016)
Effects of sparse sampling schemes on image quality in low-dose CT.
Sajid Abbas (2013)
Spatial distribution of intracortical porosity varies across age and sex.
J. Nirody (2015)
Fracture prediction, imaging and screening in osteoporosis
N. Fuggle (2019)
Relations of mechanical properties to density and CT numbers in human bone.
J. Rho (1995)
X. García-Albéniz (2014)
Changes in functional status attributable to hip fracture: a comparison of hip fracture patients to community-dwelling aged.
J. Magaziner (2003)
Opportunistic Screening for Osteoporosis Using Computed Tomography: State of the Art and Argument for Paradigm Shift
L. Lenchik (2018)
Automated quantitative morphometry of vertebral heights on spinal radiographs: comparison of a clinical workflow tool with standard 6-point morphometry
K. Engelke (2019)
Multi-detector CT imaging: impact of virtual tube current reduction and sparse sampling on detection of vertebral fractures
N. Sollmann (2019)
Low-dose and sparse sampling MDCT-based femoral bone strength prediction using finite element analysis
N. M. Rayudu (2020)
Multidetector Computed Tomography Imaging: Effect of Sparse Sampling and Iterative Reconstruction on Trabecular Bone Microstructure
M. Mookiah (2018)
Finite element models predict in vitro vertebral body compressive strength better than quantitative computed tomography.
R. Crawford (2003)
use of different densitometric measures
J Keyak (1994)
Effects of dose reduction on bone strength prediction using finite element analysis
D. Anitha (2016)
Structural analysis of cortical porosity applied to HR-pQCT data.
Willy Tjong (2014)
A Practical Methodology
L. Cao (2019)
A preliminary study
L. Qiu (2018)
a socioeconomics perspective
AA Baaj (2011)
a validation study using a computed tomography-based nonlinear finite element analysis
Imai K. Aging (2015)
A Finite Element Study on the
Effects of virtual tube current reduction and sparse sampling on MDCT-based femoral BMD measurements
N. Sollmann (2018)
Validation of a Low Dose Simulation Technique for Computed Tomography Images
D. Muenzel (2014)
Epidemiology of vertebral fractures in women.
L. J. Melton (1989)
The utility and limitations of using trabecular bone score with FRAX
P. Martineau (2018)
Statistical Image Reconstruction Methods for Transmission Tomography
J. Fessler (2000)
A Third-Generation Adaptive Statistical Iterative Reconstruction Technique: Phantom Study of Image Noise, Spatial Resolution, Lesion Detectability, and Dose Reduction Potential.
A. Euler (2018)
Trends in the treatment of lumbar spine fractures in the United States: a socioeconomics perspective: clinical article.
A. Baaj (2011)
Correlations between orthogonal mechanical properties and density of trabecular bone: use of different densitometric measures.
J. Keyak (1994)
Implications of local osteoporosis on the efficacy of anti-resorptive drug treatment: a 3-year follow-up finite element study in risedronate-treated women
D. Anitha (2013)
Effect of Statistically Iterative Image Reconstruction on Vertebral Bone Strength Prediction Using Bone Mineral Density and Finite Element Modeling: A Preliminary Study
D. Anitha (2019)
Diagnosis of osteoporosis and assessment of fracture risk
J. Kanis (2002)
Aging and disease analysis of vertebral bone strength , fracture pattern , and fracture location : a validation study using a computed tomography - based nonlinear finite element analysis
K Imai (2015)
Prediction of new clinical vertebral fractures in elderly men using finite element analysis of CT scans
Xiang Wang (2012)
X-ray-based quantitative osteoporosis imaging at the spine
M.T. Löffler (2019)
Opportunistic Use of CT Imaging for Osteoporosis Screening and Bone Density Assessment: A Qualitative Systematic Review
E. Gausden (2017)
Spine Computed Tomography Doses and Cancer Induction
P. Richards (2010)
Measuring agreement in method comparison studies
J. Bland (1999)
Improved prediction of proximal femoral fracture load using nonlinear finite element models.
J. Keyak (2001)
Informed Consent for Radiation Risk from CT Is Unjustified Based on the Current Scientific Evidence.
H. Harvey (2015)
Radiation exposure in X-ray-based imaging techniques used in osteoporosis
J. Damilakis (2010)
Effect of the intervertebral disc on vertebral bone strength prediction: a Finite-Element Study.
D. Anitha (2019)
The challenges of diagnosing osteoporosis and the limitations of currently available tools
P. Choksi (2018)
Aging and disease analysis of vertebral bone strength, fracture pattern, and fracture location : a validation study using a computed tomography-based nonlinear finite element analysis. Aging Dis
K Imai (2015)

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