University of Oulu

Sarin, J., Rieppo, L., Brommer, H., Afara, I., Saarakkala, S., Töyräs, J. (2017) Combination of optical coherence tomography and near infrared spectroscopy enhances determination of articular cartilage composition and structure. Scientific Reports, 7 (1), doi:10.1038/s41598-017-10973-z

Combination of optical coherence tomography and near infrared spectroscopy enhances determination of articular cartilage composition and structure

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Author: Sarin, Jaakko K.1,2; Rieppo, Lassi1,3; Brommer, Harold4;
Organizations: 1Department of Applied Physics, University of Eastern Finland, Kuopio
2Diagnostic Imaging Center, Kuopio University Hospital
3Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu
4Department of Equine Sciences, Faculty of Veterinary Medicine, Utrecht University
5Medical Research Center Oulu, Oulu University Hospital and University of Oulu
6Department of Diagnostic Radiology, Oulu University Hospital
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 2.4 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2017110750541
Language: English
Published: Springer Nature, 2017
Publish Date: 2017-11-07
Description:

Abstract

Conventional arthroscopic evaluation of articular cartilage is subjective and poorly reproducible. Therefore, implementation of quantitative diagnostic techniques, such as near infrared spectroscopy (NIRS) and optical coherence tomography (OCT), is essential. Locations (n = 44) with various cartilage conditions were selected from mature equine fetlock joints (n = 5). These locations and their surroundings were measured with NIRS and OCT (n = 530). As a reference, cartilage proteoglycan (PG) and collagen contents, and collagen network organization were determined using quantitative microscopy. Additionally, lesion severity visualized in OCT images was graded with an automatic algorithm according to International Cartilage Research Society (ICRS) scoring system. Artificial neural network with variable selection was then employed to predict cartilage composition in the superficial and deep zones from NIRS data, and the performance of two models, generalized (including all samples) and condition-specific models (based on ICRS-grades), was compared. Spectral data correlated significantly (p < 0.002) with PG and collagen contents, and collagen orientation in the superficial and deep zones. The combination of NIRS and OCT provided the most reliable outcome, with condition-specific models having lower prediction errors (9.2%) compared to generalized models (10.4%). Therefore, the results highlight the potential of combining both modalities for comprehensive evaluation of cartilage during arthroscopy.

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Series: Scientific reports
ISSN: 2045-2322
ISSN-E: 2045-2322
ISSN-L: 2045-2322
Volume: 7
Issue: 1
Article number: 10586
DOI: 10.1038/s41598-017-10973-z
OADOI: https://oadoi.org/10.1038/s41598-017-10973-z
Type of Publication: A1 Journal article – refereed
Field of Science: 3126 Surgery, anesthesiology, intensive care, radiology
Subjects:
Funding: Doctoral Programme in Science, Technology and Computing (SCITECO) of University of Eastern Finland, Kuopio University Hospital (VTR projects 5041750 and 5041744, PY210 Clinical Neurophysiology) and Orion Research Foundation sr financially supported this study. Krista Rahunen and Mari Huusko are acknowledged for technical assistance. Dr Afara acknowledges funding from Finnish Cultural Foundation (00160079).
Copyright information: © The Author(s) 2017. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
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