Articular cartilage provides mechanical load dissipation and lubrication between joints, and additionally provides protects from abrasion. At present, there are no treatments to cure or attenuate the degradation of cartilage. Early detection and the ability to monitor the progression of osteoarthritis is important for developing effective therapies. However, few reliable imaging biomarkers exist to detect cartilage disease before advanced degeneration in the tissue. One specialized MRI technique, termed displacements under applied loading by MRI (dualMRI), was developed to measure displacements and strain in musculoskeletal tissues, hydrogels and engineered constructs. However, deformation information does not directly describe spatial distributions of tissue properties (e.g. stiffness), which is critical to the understanding of disease progression. To achieve the stiffness measurement, we developed and validated an inverse modeling workflow that combined dualMRI, to directly measure intratissue deformation, with topology optimization in the application of heterogeneous (layered) materials representative of the complex gradient architecture of articular cartilage. We successfully reconstructed bi-layer stiffness from ideal displacements calculated from forward simulation as well as from experimental data measured from dualMRI. To monitor the progression of osteoarthritis, we measured and analyzed biomechanical changes of sheep stifle cartilage after meniscectomy. We found that 2nd principal strain and max shear strain in the femur contact region are sensitive to cartilage degeneration at different stages and compared to more conventional methods like quantitative MRI. To investigate the biomechanical changes in articular cartilage with defect and repair, we implanted decellularized cartilage implant into sheep cartilage defect and evaluate the repair results using quantitative MRI and dualMRI. We found that implants placed in joints demonstrated lower strains compared to joints with untreated defects.