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Arch Osteoporos. 2020 Feb 22;15(1):17. doi: 10.1007/s11657-020-0708-9.

Low-dose and sparse sampling MDCT-based femoral bone strength prediction using finite element analysis.

Author information

1
Engineering Product Development (EPD) Pillar, Singapore University of Technology and Design (SUTD), 8 Somapah Road, Singapore, 487372, Singapore.
2
Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany.
3
Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany.
4
Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
5
Engineering Product Development (EPD) Pillar, Singapore University of Technology and Design (SUTD), 8 Somapah Road, Singapore, 487372, Singapore. subburaj@sutd.edu.sg.

Abstract

This study aims to evaluate the impact of dose reduction through tube current and sparse sampling on multi-detector computed tomography (MDCT)-based femoral bone strength prediction using finite element (FE) analysis. FE-predicted femoral failure load obtained from MDCT scan data was not significantly affected by 50% dose reductions through sparse sampling. Further decrease in dose through sparse sampling (25% of original projections) and virtually reduced tube current (50% and 25% of the original dose) showed significant effects on the FE-predicted failure load results.

PURPOSE:

To investigate the effect of virtually reduced tube current and sparse sampling on multi-detector computed tomography (MDCT)-based femoral bone strength prediction using finite element (FE) analysis.

METHODS:

Routine MDCT data covering the proximal femur of 21 subjects (17 males; 4 females; mean age, 71.0 ± 8.8 years) without any bone diseases aside from osteoporosis were included in this study. Fifty percent and 75% dose reductions were achieved by virtually reducing tube current and by applying a sparse sampling strategy from the raw image data. Images were then reconstructed with a statistically iterative reconstruction algorithm. FE analysis was performed on all reconstructed images and the failure load was calculated. The root mean square coefficient of variation (RMSCV) and coefficient of correlation (R2) were calculated to determine the variation in the FE-predicted failure load data for dose reductions, using original-dose MDCT scan as the standard of reference.

RESULTS:

Fifty percent dose reduction through sparse sampling showed lower RMSCV and higher correlations when compared with virtually reduced tube current method (RMSCV = 5.70%, R2 = 0.96 vs. RMSCV = 20.78%, R2 = 0.79). Seventy-five percent dose reduction achieved through both methods (RMSCV = 22.38%, R2 = 0.80 for sparse sampling; RMSCV = 24.58%, R2 = 0.73 for reduced tube current) could not predict the failure load accurately.

CONCLUSION:

Our simulations indicate that up to 50% reduction in radiation dose through sparse sampling can be used for FE-based prediction of femoral failure load. Sparse-sampled MDCT may allow fracture risk prediction and treatment monitoring in osteoporosis with less radiation exposure in the future.

KEYWORDS:

Bone strength; Finite element analysis; Multi-detector computed tomography; Osteoporosis; Proximal femur; Sparse sampling; Statistical iterative reconstruction

PMID:
32088769
DOI:
10.1007/s11657-020-0708-9

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