Image interpolation allows accurate quantitative bone morphometry in registered micro-computed tomography scans

Time-lapsed in vivo micro-computed tomography is a powerful tool to analyse longitudinal changes in the bone micro-architecture. Registration can overcome problems associated with spatial misalignment between scans; however, it requires image interpolation which might affect the outcome of a subsequent bone morphometric analysis. The impact of the interpolation error itself, though, has not been quantified to date. Therefore, the purpose of this ex vivo study was to elaborate the effect of different interpolator schemes [nearest neighbour, tri-linear and B-spline (BSP)] on bone morphometric indices. None of the interpolator schemes led to significant differences between interpolated and non-interpolated images, with the lowest interpolation error found for BSPs (1.4%). Furthermore, depending on the interpolator, the processing order of registration, Gaussian filtration and binarisation played a role. Independent from the interpolator, the present findings suggest that the evaluation of bone morphometry should be done with images registered using greyscale information.

[1]  Max A. Viergever,et al.  Quantitative Comparison of Sinc-Approximating Kernels for Medical Image Interpolation , 1999, MICCAI.

[2]  Ralph Müller,et al.  Guidelines for assessment of bone microstructure in rodents using micro–computed tomography , 2010, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[3]  M. Unser,et al.  Interpolation revisited [medical images application] , 2000, IEEE Transactions on Medical Imaging.

[4]  M. Jergas,et al.  Accurate assessment of precision errors: How to measure the reproducibility of bone densitometry techniques , 2005, Osteoporosis International.

[5]  Michael Unser,et al.  A pyramid approach to subpixel registration based on intensity , 1998, IEEE Trans. Image Process..

[6]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[7]  Gary G. Koch,et al.  Intraclass Correlation Coefficient , 2011, International Encyclopedia of Statistical Science.

[8]  Thomas M. Link,et al.  Three-dimensional image registration of MR proximal femur images for the analysis of trabecular bone parameters. , 2008 .

[9]  P. Rüegsegger,et al.  Direct Three‐Dimensional Morphometric Analysis of Human Cancellous Bone: Microstructural Data from Spine, Femur, Iliac Crest, and Calcaneus , 1999, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[10]  TOR Hildebrand,et al.  Quantification of Bone Microarchitecture with the Structure Model Index. , 1997, Computer methods in biomechanics and biomedical engineering.

[11]  Ralph Müller,et al.  Mouse tail vertebrae adapt to cyclic mechanical loading by increasing bone formation rate and decreasing bone resorption rate as shown by time-lapsed in vivo imaging of dynamic bone morphometry. , 2011, Bone.

[12]  D L Hill,et al.  Automated three-dimensional registration of magnetic resonance and positron emission tomography brain images by multiresolution optimization of voxel similarity measures. , 1997, Medical physics.

[13]  Ralph Müller,et al.  Monitoring individual morphological changes over time in ovariectomized rats by in vivo micro-computed tomography. , 2006, Bone.

[14]  Steven K Boyd,et al.  Improved reproducibility of high-resolution peripheral quantitative computed tomography for measurement of bone quality. , 2008, Medical engineering & physics.

[15]  Ralph Müller,et al.  Automated compartmental analysis for high-throughput skeletal phenotyping in femora of genetic mouse models. , 2007, Bone.

[16]  Ralph Müller,et al.  Evaluation of Three-dimensional Image Registration Methodologies for In Vivo Micro-computed Tomography , 2006, Annals of Biomedical Engineering.

[17]  P. Rüegsegger,et al.  A new method for the model‐independent assessment of thickness in three‐dimensional images , 1997 .

[18]  R. Huiskes,et al.  Effects of PTH treatment on tibial bone of ovariectomized rats assessed by in vivo micro-CT , 2009, Osteoporosis International.

[19]  Steven K Boyd,et al.  Reproducibility of bone micro-architecture measurements in rodents by in vivo micro-computed tomography is maximized with three-dimensional image registration. , 2010, Bone.

[20]  H J Gundersen,et al.  Estimation of structural anisotropy based on volume orientation. A new concept , 1990, Journal of microscopy.

[21]  M. Unser,et al.  Interpolation Revisited , 2000, IEEE Trans. Medical Imaging.

[22]  Andres Laib,et al.  Noninvasive monitoring of changes in structural cancellous bone parameters with a novel prototype micro-CT , 2009, Journal of Bone and Mineral Metabolism.

[24]  G. H. van Lenthe,et al.  Non-invasive bone competence analysis by high-resolution pQCT: an in vitro reproducibility study on structural and mechanical properties at the human radius. , 2009, Bone.

[25]  Paul Suetens,et al.  Comparative evaluation of multiresolution optimization strategies for multimodality image registration by maximization of mutual information , 1999, Medical Image Anal..

[26]  Ralph Müller,et al.  In vivo micro-computed tomography allows direct three-dimensional quantification of both bone formation and bone resorption parameters using time-lapsed imaging. , 2011, Bone.

[27]  H Weinans,et al.  Detecting and tracking local changes in the tibiae of individual rats: a novel method to analyse longitudinal in vivo micro-CT data. , 2004, Bone.

[28]  A Odgaard,et al.  Three-dimensional methods for quantification of cancellous bone architecture. , 1997, Bone.

[29]  R. Müller,et al.  Compartmental Bone Morphometry in the Mouse Femur: Reproducibility and Resolution Dependence of Microtomographic Measurements , 2005, Calcified Tissue International.