The aim of this study was to determine if a CT image deblurring algorithm can improve CT-based FE modelling accuracy at the proximal femur. Experimental data (CT scans of fourteen proximal fresh-frozen cadaveric femurs, non-destructive surface strain measurements in stance and sideways fall loading configurations on all femurs, and failure loads obtained in stance for seven specimens, in sideways fall for the other seven) were taken from a recent study (Schileo et al., 2014). An estimate of the 3D Point Spread Function for each CT scan was used within a deconvolution solver to perform the deblurring. The most proximal regions of three specimens were scanned using an HRpQCT scanner and compared to the original and deblurred CT images to quantify errors in bone contour estimates and determine correlation of intensity values within the bone contours. Subject-specific FE models of the proximal femur were generated. The accuracy of deblurred FE predictions against experimental measurements was compared to the published (non-deblurred) FE results. When compared to HRpQCT, CT deblurring led to lower mean surface distances (0.31 vs. 0.49 mm) and higher CT intensity correlations with respect to the original CT. All indicators of strain prediction accuracy were significantly improved in deblurred FE models, more markedly at the femoral neck (peak error reduced by 38%). Failure load prediction, based on a simple elastic limit model, was also improved in deblurred FE models, although differently for stance and sideways fall loading conditions. In stance, correlation was unchanged, but specimen-wise errors were reduced (mean error 10% vs. 15%). In sideways fall, correlation notably increased (R2=0.95 vs. 0.81), despite a general overestimation of failure load. In summary, the proposed CT deblurring technique yielded moderate but significant improvements in FE predictions, and may thus be considered a first step toward the improvement of CT-based FE models of the human femur.

Can CT image deblurring improve finite element predictions at the proximal femur?

Falcinelli C.
;
2016

Abstract

The aim of this study was to determine if a CT image deblurring algorithm can improve CT-based FE modelling accuracy at the proximal femur. Experimental data (CT scans of fourteen proximal fresh-frozen cadaveric femurs, non-destructive surface strain measurements in stance and sideways fall loading configurations on all femurs, and failure loads obtained in stance for seven specimens, in sideways fall for the other seven) were taken from a recent study (Schileo et al., 2014). An estimate of the 3D Point Spread Function for each CT scan was used within a deconvolution solver to perform the deblurring. The most proximal regions of three specimens were scanned using an HRpQCT scanner and compared to the original and deblurred CT images to quantify errors in bone contour estimates and determine correlation of intensity values within the bone contours. Subject-specific FE models of the proximal femur were generated. The accuracy of deblurred FE predictions against experimental measurements was compared to the published (non-deblurred) FE results. When compared to HRpQCT, CT deblurring led to lower mean surface distances (0.31 vs. 0.49 mm) and higher CT intensity correlations with respect to the original CT. All indicators of strain prediction accuracy were significantly improved in deblurred FE models, more markedly at the femoral neck (peak error reduced by 38%). Failure load prediction, based on a simple elastic limit model, was also improved in deblurred FE models, although differently for stance and sideways fall loading conditions. In stance, correlation was unchanged, but specimen-wise errors were reduced (mean error 10% vs. 15%). In sideways fall, correlation notably increased (R2=0.95 vs. 0.81), despite a general overestimation of failure load. In summary, the proposed CT deblurring technique yielded moderate but significant improvements in FE predictions, and may thus be considered a first step toward the improvement of CT-based FE models of the human femur.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11564/769916
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