The objective of this proposal is to predict osteoarthritis (OA) pathogenesis in vivo using a novel noninvasive MRI-based method of measuring articular cartilage biomechanics. Recent advances in magnetic resonance imaging (MRI) have been introduced with exciting potential to diagnose and predict the progression of OA, the most common degenerative joint disease. MRI methods have sought to discover early changes in OA, when emerging disease-modifying interventions (e.g. cell implantation) may be most effective. OA pathophysiology often involves joint injury (e.g. ligament rupture) and a degenerative cascade of increased expression of inflammatory cytokines and enzymes. Moreover, the breakdown and loss of major macromolecules such as aggrecan and type II collagen leads to altered strains and material properties (e.g. moduli) within the tissue, suggesting MRI of cartilage biomechanics may be sensitive to degeneration. Unfortunately, noninvasive diagnosis of OA remains poor, especially in early disease stages, and several challenges remain, including the need for sensitive and specific imaging biomarkers that predict OA outcomes, and the need to relate imaging biomarkers to tissue function and biomechanics. In our original grant (AR063712), we pioneered dualMRI (displacements under applied loading by MRI) for cartilage biomechanics to monitor joint health. We discovered that dualMRI is robust to detect strain increases following controlled enzyme digestions or mechanical trauma to excised tissues, and in an in vivo time-course meniscectomy study in sheep. Compared to quantitative MRI (qMRI, e.g. T1? mapping), shear strains better correlated with OA severity in human cartilage. We also recently performed first-in-human in vivo and intra-tissue cartilage strain measures on a clinical 3 Tesla (T) MRI system. In this renewal application, we will establish a workflow to measure strains and moduli (i.e. elastography), and validate this workflow in multiple model systems. In humans, we will also identify biomechanics-based MRI metrics and biomarkers that predict time-course cartilage function and symptomatic pain following ligament reconstruction in a subset of patients. We will pursue three related specific aims.
In Aim 1, we will establish a routine, clinical workflow for dualMRI measures of intra-tissue strain and properties. We will extend our existing dualMRI sequence to accelerate clinical measurement of strain within 15 minutes, and coupled to inverse modeling, automate measurement of in vivo elastography.
In Aim 2, we will validate dualMRI intra-tissue strain and properties against gold-standard benchmarks, confirming reproducibility for in vivo time course analyses by quantifying numerous error metrics.
In Aim 3, we will predict functional outcomes and cartilage health in patients following ligament reconstruction. We will determine the extent that MRI metrics at six months predict patient-reported outcomes and tissue health at one and two years post treatment. If successful, we will establish a routine method for functional assessment of cartilage, and support a new paradigm targeting cartilage biomechanics as specific indicators of joint damage and repair.

Public Health Relevance

Human health is greatly impacted by joint trauma and osteoarthritis. The proposed research will likely improve our ability to understand the pathogenesis of osteoarthritis and predict degenerative changes in articular cartilage following ligament injury and repair by using novel noninvasive magnetic resonance imaging techniques, inverse and statistical modeling, and defined human populations. We aim to establish a clinical workflow for a foundational imaging technique to quantify damage and repair through noninvasive strain and material property measures, and compare the technique to conventional imaging and biomarker assays.

Agency
National Institute of Health (NIH)
Institute
National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)
Type
Research Project (R01)
Project #
5R01AR063712-08
Application #
9918150
Study Section
Skeletal Biology Structure and Regeneration Study Section (SBSR)
Program Officer
Zheng, Xincheng
Project Start
2013-09-19
Project End
2024-03-31
Budget Start
2020-04-01
Budget End
2021-03-31
Support Year
8
Fiscal Year
2020
Total Cost
Indirect Cost
Name
University of Colorado at Boulder
Department
Engineering (All Types)
Type
Biomed Engr/Col Engr/Engr Sta
DUNS #
007431505
City
Boulder
State
CO
Country
United States
Zip Code
80303
Chan, Deva D; Cai, Luyao; Butz, Kent D et al. (2018) Functional MRI can detect changes in intratissue strains in a full thickness and critical sized ovine cartilage defect model. J Biomech 66:18-25
Ghosh, Soham; Cimino, James G; Scott, Adrienne K et al. (2017) In Vivo Multiscale and Spatially-Dependent Biomechanics Reveals Differential Strain Transfer Hierarchy in Skeletal Muscle. ACS Biomater Sci Eng 3:2798-2805
Worke, Logan J; Barthold, Jeanne E; Seelbinder, Benjamin et al. (2017) Densification of Type I Collagen Matrices as a Model for Cardiac Fibrosis. Adv Healthc Mater 6:
Kim, Woong; Ferguson, Virginia L; Borden, Mark et al. (2016) Application of Elastography for the Noninvasive Assessment of Biomechanics in Engineered Biomaterials and Tissues. Ann Biomed Eng 44:705-24
Novak, Tyler; Seelbinder, Benjamin; Twitchell, Celina M et al. (2016) Dissociated and Reconstituted Cartilage Microparticles in Densified Collagen Induce Local hMSC Differentiation. Adv Funct Mater 26:5427-5436
Novak, Tyler; Fites Gilliland, Kateri; Xu, Xin et al. (2016) In Vivo Cellular Infiltration and Remodeling in a Decellularized Ovine Osteochondral Allograft. Tissue Eng Part A 22:1274-1285
Novak, Tyler; Seelbinder, Benjamin; Twitchell, Celina M et al. (2016) Mechanisms and Microenvironment Investigation of Cellularized High Density Gradient Collagen Matrices via Densification. Adv Funct Mater 26:2617-2628
Xu, Xin; Li, Zhiyu; Leng, Yue et al. (2016) Knockdown of the pericellular matrix molecule perlecan lowers in situ cell and matrix stiffness in developing cartilage. Dev Biol 418:242-7
Xu, Xin; Li, Zhiyu; Cai, Luyao et al. (2016) Mapping the Nonreciprocal Micromechanics of Individual Cells and the Surrounding Matrix Within Living Tissues. Sci Rep 6:24272
Chan, Deva D; Cai, Luyao; Butz, Kent D et al. (2016) In vivo articular cartilage deformation: noninvasive quantification of intratissue strain during joint contact in the human knee. Sci Rep 6:19220

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