This research proposal will generate new non-invasive biomarkers with improved sensitivity and specificity to the biochemical and ultra-structural changes of osteoarthritis (OA) through development of quantitative magnetic resonance (MR) methods which can assess the individual water components of cartilage. Cartilage degeneration in OA is characterized by decreased proteoglycan content and disruption of the highly organized collagen fiber network. Multi-Component Driven Equilibrium Single Shot Observation of T1 and T2 (mcDESPOT) and 3D-Cones are novel techniques for bi-component T2 and T2* mapping which can measure the fractions and T2 characteristic specific to the water components bound to proteoglycan (WPG) and collagen (WCOL) within cartilage. Assessment of the individual water components of articular cartilage may provide a new sensitive and specific method to detect changes in the proteoglycan and collagen constituents of the cartilage macromolecular matrix.
In Aim 1 of the proposal, mcDESPOT and 3D-Cones will be optimized for performing bi-component T2 and T2* mapping of cartilage.
In Aim 2, bi-component T2 and T2* parameters of cartilage samples obtained from 10 human cadaveric knee joints will be measured at 3T using mcDESPOT and 3D-Cones and compared with biochemical parameters (proteoglycan, collagen, and water content), microstructural parameters (integrity of collagen fiber network measured using second harmonic generation microscopy), and biomechanical parameters (aggregate modulus, permeability, and Poison ratio). We hypothesize that the fraction of water bound to proteoglycan (FPG) will be a strong predictor of the proteoglycan content and compressive stiffness of cartilage, while the fraction of water bound to collagen (FCOL) will be a strong predictor of the degree of disruption of the collagen fiber network of cartilage.
In Aim 3 of the proposal, 35 asymptomatic volunteers and 70 patients with varying degrees of knee OA will be imaged at 3T using mcDESPOT, 3D-Cones and other currently available quantitative MR techniques. We hypothesize that bi- component T2 and T2* parameters measured using mcDESPOT and 3D-Cones will be more sensitive than other MR parameters for distinguishing between subjects with and without cartilage degeneration. Successful completion of the project will provide the OA research community with new MR biomarkers which are sensitive and specific to the proteoglycan and collagen components of articular cartilage and which can be used for both the detection and characterization of cartilage degeneration in patients with OA.

Public Health Relevance

The proposal will develop new imaging methods to conveniently and safely evaluate the various structural components of articular cartilage. The new technique will provide researchers with better tools to monitor changes occurring in cartilage during the initiation, progression, and treatment of osteoarthritis which will ultimately lead to a better understanding of the disease and development of improved treatment options.

Agency
National Institute of Health (NIH)
Institute
National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)
Type
Research Project (R01)
Project #
5R01AR068373-02
Application #
9109541
Study Section
Medical Imaging Study Section (MEDI)
Program Officer
Lester, Gayle E
Project Start
2015-07-13
Project End
2019-06-30
Budget Start
2016-07-01
Budget End
2017-06-30
Support Year
2
Fiscal Year
2016
Total Cost
Indirect Cost
Name
University of Wisconsin Madison
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
161202122
City
Madison
State
WI
Country
United States
Zip Code
53715
Nguyen, Jie C; Allen, Hailey; Liu, Fang et al. (2018) Maturation-Related Changes in T2 Relaxation Times of Cartilage and Meniscus of the Pediatric Knee Joint at 3 T. AJR Am J Roentgenol 211:1369-1375
Zhou, Zhaoye; Zhao, Gengyan; Kijowski, Richard et al. (2018) Deep convolutional neural network for segmentation of knee joint anatomy. Magn Reson Med :
Liu, Fang; Zhou, Zhaoye; Samsonov, Alexey et al. (2018) Deep Learning Approach for Evaluating Knee MR Images: Achieving High Diagnostic Performance for Cartilage Lesion Detection. Radiology 289:160-169
Liu, Fang; Jang, Hyungseok; Kijowski, Richard et al. (2018) Deep Learning MR Imaging-based Attenuation Correction for PET/MR Imaging. Radiology 286:676-684
Markhardt, B Keegan; Li, Geng; Kijowski, Richard (2018) The Clinical Significance of Osteophytes in Compartments of the Knee Joint With Normal Articular Cartilage. AJR Am J Roentgenol 210:W164-W171
Liu, Fang; Zhou, Zhaoye; Jang, Hyungseok et al. (2018) Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magn Reson Med 79:2379-2391
Kijowski, Richard; Rosas, Humberto; Williams, Adam et al. (2017) MRI characteristics of torn and untorn post-operative menisci. Skeletal Radiol 46:1353-1360
Liu, Fang; Kijowski, Richard (2017) Assessment of different fitting methods for in-vivo bi-component T2* analysis of human patellar tendon in magnetic resonance imaging. Muscles Ligaments Tendons J 7:163-172
Liu, Fang; Velikina, Julia V; Block, Walter F et al. (2017) Fast Realistic MRI Simulations Based on Generalized Multi-Pool Exchange Tissue Model. IEEE Trans Med Imaging 36:527-537
Kijowski, Richard; Wilson, John J; Liu, Fang (2017) Bicomponent ultrashort echo time T2* analysis for assessment of patients with patellar tendinopathy. J Magn Reson Imaging 46:1441-1447

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