Error introduced by common reorientation algorithms in the assessment of rodent trabecular morphometry using micro‐computed tomography
dc.contributor.author | Newton, Michael D. | |
dc.contributor.author | Hartner, Samantha | |
dc.contributor.author | Gawronski, Karissa | |
dc.contributor.author | Maerz, Tristan | |
dc.date.accessioned | 2018-11-20T15:34:29Z | |
dc.date.available | 2019-12-02T14:55:09Z | en |
dc.date.issued | 2018-10 | |
dc.identifier.citation | Newton, Michael D.; Hartner, Samantha; Gawronski, Karissa; Maerz, Tristan (2018). "Error introduced by common reorientation algorithms in the assessment of rodent trabecular morphometry using micro‐computed tomography." Journal of Orthopaedic Research® 36(10): 2762-2770. | |
dc.identifier.issn | 0736-0266 | |
dc.identifier.issn | 1554-527X | |
dc.identifier.uri | https://hdl.handle.net/2027.42/146417 | |
dc.description.abstract | Quantitative analyses of bone using micro‐computed tomography (μCT) are routinely employed in preclinical research, and virtual image reorientation to a consistent reference frame is a common processing step. The purpose of this study was to quantify error introduced by common reorientation algorithms in μCT‐based characterization of bone. Mouse and rat tibial metaphyses underwent μCT scanning at a range of resolutions (6–30 μm). A trabecular volume‐of‐interest (VOI) was manually selected. Image stacks were analyzed without rotation, following 45° In‐Plane axial rotation, and following 45° Triplanar rotation. Interpolation was performed using Nearest‐Neighbor, Linear, and Cubic interpolations. Densitometric (bone volume fraction, tissue mineral density, bone mineral density) and morphometric variables (trabecular thickness, trabecular spacing, trabecular number, structural model index) were computed for each combination of voxel size, rotation, and interpolation. Significant reorientation error was measured in all parameters, and was exacerbated at higher voxel sizes, with relatively low error at 6 and 12 μm (max. reorientation error in BV/TV was 2.9% at 6 μm, 7.7% at 12 μm and 36.5% at 30 μm). Considering densitometric parameters, Linear and Cubic interpolations introduced significant error while Nearest‐Neighbor interpolation caused minimal error, and In‐Plane rotation caused greater error than Triplanar. Morphometric error was strongly and intricately dependent on the combination of rotation and interpolation employed. Reorientation error can be eliminated by avoiding reorientation altogether or by “de‐rotating” VOIs from reoriented images back to the original reference frame prior to analysis. When these are infeasible, reorientation error can be minimized through sufficiently high resolution scanning, careful selection of interpolation type, and consistent processing of all images. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:2762–2770, 2018. | |
dc.publisher | Wiley Periodicals, Inc. | |
dc.subject.other | error | |
dc.subject.other | trabecular morphometry | |
dc.subject.other | reorientation | |
dc.subject.other | realignment | |
dc.subject.other | rotation | |
dc.subject.other | interpolation | |
dc.title | Error introduced by common reorientation algorithms in the assessment of rodent trabecular morphometry using micro‐computed tomography | |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | |
dc.subject.hlbsecondlevel | Kinesiology and Sports | |
dc.subject.hlbtoplevel | Health Sciences | |
dc.description.peerreviewed | Peer Reviewed | |
dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/146417/1/jor24039_am.pdf | |
dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/146417/2/jor24039.pdf | |
dc.identifier.doi | 10.1002/jor.24039 | |
dc.identifier.source | Journal of Orthopaedic Research® | |
dc.identifier.citedreference | Saha PK, Wehrli FW. 2004. Measurement of trabecular bone thickness in the limited resolution regime of in vivo MRI by fuzzy distance transform. IEEE Trans Med Imaging 23: 53 – 62. | |
dc.identifier.citedreference | Christiansen BA. 2016. Effect of micro‐computed tomography voxel size and segmentation method on trabecular bone microstructure measures in mice. Bone Rep 5: 136 – 140. | |
dc.identifier.citedreference | Morey RD. 2008. Confidence intervals from normalized data: a correction to Cousineau (2005). Tutor Quant Methods Psychol 4: 61 – 64. | |
dc.identifier.citedreference | Sage D, Prodanov D, Tinevez J‐Y, et al. MIJ: making interoperability between ImageJ and Matlab possible, ImageJ User & Developer Conference, 2012. | |
dc.identifier.citedreference | Bouxsein ML, Boyd SK, Christiansen BA, et al. 2010. Guidelines for assessment of bone microstructure in rodents using micro‐computed tomography. J Bone Miner Res 25: 1468 – 1486. | |
dc.identifier.citedreference | Doube M, Kłosowski MM, Arganda‐Carreras I, et al. 2010. BoneJ: free and extensible bone image analysis in ImageJ. Bone 47: 1076 – 1079. | |
dc.identifier.citedreference | O’Brien F, Cousineau D. 2014. Representing error bars in within‐subject designs in typical software packages. Quant Methods Psychol 10: 56 – 67. | |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
Files in this item
Remediation of Harmful Language
The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.
Accessibility
If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.