University of Oulu

Ketola, J., Karhula, S., Finnilä, M., Korhonen, R., Herzog, W., Siltanen, S., Nieminen, M., Saarakkala, S. (2018) Iterative and discrete reconstruction in the evaluation of the rabbit model of osteoarthritis. Scientific Reports, 8 (1), 12051. doi:10.1038/s41598-018-30334-8

Iterative and discrete reconstruction in the evaluation of the rabbit model of osteoarthritis

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Author: Ketola, Juuso H.1; Karhula, Sakari S.1,2; Finnilä, Mikko A. J.1,3,4;
Organizations: 1Research Unit of Medical Imaging, Physics and Technology, University of Oulu
2Infotech Doctoral Program, University of Oulu
3Medical Research Center, University of Oulu and Oulu University Hospital
4Department of Applied Physics, University of Eastern Finland, Kuopio
5Human Performance Laboratory, Faculty of Kinesiology, University of Calgary
6McCaig Institute for Bone and Joint Health, University of Calgary
7Department of Mathematics and Statistics, University of Helsinki
8Department of Diagnostic Radiology, Oulu University Hospital
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.7 MB)
Persistent link:
Language: English
Published: Springer Nature, 2018
Publish Date: 2018-11-10


Micro-computed tomography (µCT) is a standard method for bone morphometric evaluation. However, the scan time can be long and the radiation dose during the scan may have adverse effects on test subjects, therefore both of them should be minimized. This could be achieved by applying iterative reconstruction (IR) on sparse projection data, as IR is capable of producing reconstructions of sufficient image quality with less projection data than the traditional algorithm requires. In this work, the performance of three IR algorithms was assessed for quantitative bone imaging from low-resolution data in the evaluation of the rabbit model of osteoarthritis. Subchondral bone images were reconstructed with a conjugate gradient least squares algorithm, a total variation regularization scheme, and a discrete algebraic reconstruction technique to obtain quantitative bone morphometry, and the results obtained in this manner were compared with those obtained from the reference reconstruction. Our approaches were sufficient to identify changes in bone structure in early osteoarthritis, and these changes were preserved even when minimal data were provided for the reconstruction. Thus, our results suggest that IR algorithms give reliable performance with sparse projection data, thereby recommending them for use in µCT studies where time and radiation exposure are preferably minimized.

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Series: Scientific reports
ISSN: 2045-2322
ISSN-E: 2045-2322
ISSN-L: 2045-2322
Volume: 8
Issue: 1
Article number: 12051
DOI: 10.1038/s41598-018-30334-8
Type of Publication: A1 Journal article – refereed
Field of Science: 3126 Surgery, anesthesiology, intensive care, radiology
3111 Biomedicine
Funding: J.H.K. acknowledges the support from Business Finland grant (project 1392/31/2016). R.K.K. acknowledges the support from the Academy of Finland (grant no. 286526) and the Sigrid Juselius Foundation. W.H. acknowledges the support from the Canadian Institutes of Health Research (Foundation Scheme Grant, FDN-143341), the Canada Research Chairs Program (950-230603), and the Killam Foundation. S.Sa. acknowledges the support from the Academy of Finland (grant nos. 268378 and 303786), the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013, ERC grant agreement no. 336267), and the Sigrid Juselius Foundation.
EU Grant Number: (336267) 3D-OA-HISTO - Development of 3D Histopathological Grading of Osteoarthritis
Academy of Finland Grant Number: 286526
Detailed Information: 286526 (Academy of Finland Funding decision)
268378 (Academy of Finland Funding decision)
303786 (Academy of Finland Funding decision)
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