Open Access
23 December 2017 Optical spectroscopic characterization of human meniscus biomechanical properties
Juho Ala-Myllymäki, Elvis K. Danso, Juuso T. J. Honkanen, Rami K. Korhonen, Juha Töyräs, Isaac O. Afara
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Abstract
This study investigates the capacity of optical spectroscopy in the visible (VIS) and near-infrared (NIR) spectral ranges for estimating the biomechanical properties of human meniscus. Seventy-two samples obtained from the anterior, central, and posterior locations of the medial and lateral menisci of 12 human cadaver joints were used. The samples were subjected to mechanical indentation, then traditional biomechanical parameters (equilibrium and dynamic moduli) were calculated. In addition, strain-dependent fibril network modulus and permeability strain-dependency coefficient were determined via finite-element modeling. Subsequently, absorption spectra were acquired from each location in the VIS (400 to 750 nm) and NIR (750 to 1100 nm) spectral ranges. Partial least squares regression, combined with spectral preprocessing and transformation, was then used to investigate the relationship between the biomechanical properties and spectral response. The NIR spectral region was observed to be optimal for model development ( 83.0%≤R2≤90.8%). The percentage error of the models are: Eeq (7.1%), Edyn (9.6%), Eϵ (8.4%), and Mk (8.9%). Thus, we conclude that optical spectroscopy in the NIR range is a potential method for rapid and nondestructive evaluation of human meniscus functional integrity and health in real time during arthroscopic surgery.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE) 1083-3668/2017/$25.00 © 2017 SPIE
Juho Ala-Myllymäki, Elvis K. Danso, Juuso T. J. Honkanen, Rami K. Korhonen, Juha Töyräs, and Isaac O. Afara "Optical spectroscopic characterization of human meniscus biomechanical properties," Journal of Biomedical Optics 22(12), 125008 (23 December 2017). https://doi.org/10.1117/1.JBO.22.12.125008
Received: 8 August 2017; Accepted: 27 November 2017; Published: 23 December 2017
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Cited by 7 scholarly publications.
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KEYWORDS
Near infrared

Spectroscopy

Optical spectroscopy

Collagen

Tissues

Statistical modeling

Data modeling

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