University of Oulu

Mirmojarabian, S.A., Kajabi, A.W., Ketola, J.H.J., Nykänen, O., Liimatainen, T., Nieminen, M.T., Nissi, M.J. and Casula, V. (2023), Machine Learning Prediction of Collagen Fiber Orientation and Proteoglycan Content From Multiparametric Quantitative MRI in Articular Cartilage. J Magn Reson Imaging, 57: 1056-1068.

Machine learning prediction of collagen fiber orientation and proteoglycan content from multiparametric quantitative MRI in articular cartilage

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Author: Mirmojarabian, Seyed Amir1; Kajabi, Abdul Wahed2; Ketola, Juuso H. J.1;
Organizations: 1Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
2Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, US
3Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
4Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
5Medical Research Center, University of Oulu and Oulu University Hospital, Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.8 MB)
Persistent link:
Language: English
Published: John Wiley & Sons, 2023
Publish Date: 2023-05-25


Background: Machine learning models trained with multiparametric quantitative MRIs (qMRIs) have the potential to provide valuable information about the structural composition of articular cartilage.

Purpose: To study the performance and feasibility of machine learning models combined with qMRIs for noninvasive assessment of collagen fiber orientation and proteoglycan content.

Study type: Retrospective, animal model.

Animal model: An open-source single slice MRI dataset obtained from 20 samples of 10 Shetland ponies (seven with surgically induced cartilage lesions followed by treatment and three healthy controls) yielded to 1600 data points, including 10% for test and 90% for train validation.

Field strength/sequence: A 9.4 T MRI scanner/qMRI sequences: T₁, T₂, adiabatic T and T, continuous-wave T and relaxation along a fictitious field (TRAFF) maps.

Assessment: Five machine learning regression models were developed: random forest (RF), support vector regression (SVR), gradient boosting (GB), multilayer perceptron (MLP), and Gaussian process regression (GPR). A nested cross-validation was used for performance evaluation. For reference, proteoglycan content and collagen fiber orientation were determined by quantitative histology from digital densitometry (DD) and polarized light microscopy (PLM), respectively.

Statistical tests: Normality was tested using Shapiro–Wilk test, and association between predicted and measured values was evaluated using Spearman’s Rho test. A P-value of 0.05 was considered as the limit of statistical significance.

Results: Four out of the five models (RF, GB, MLP, and GPR) yielded high accuracy ( = 0.68–0.75 for PLM and 0.62–0.66 for DD), and strong significant correlations between the reference measurements and predicted cartilage matrix properties (Spearman’s Rho = 0.72–0.88 for PLM and 0.61–0.83 for DD). GPR algorithm had the highest accuracy ( = 0.75 and 0.66) and lowest prediction-error (root mean squared [RMSE] = 1.34 and 2.55) for PLM and DD, respectively.

Data conclusion: Multiparametric qMRIs in combination with regression models can determine cartilage compositional and structural features, with higher accuracy for collagen fiber orientation than proteoglycan content.

Evidence level: 2

Technical efficacy: Stage 2

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Series: Journal of magnetic resonance imaging
ISSN: 1053-1807
ISSN-E: 1522-2586
ISSN-L: 1053-1807
Volume: 57
Issue: 4
Pages: 1056 - 1068
DOI: 10.1002/jmri.28353
Type of Publication: A1 Journal article – refereed
Field of Science: 217 Medical engineering
Copyright information: © 2022 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.