@misc{linka_machine_learningaugmented_2021, author={Linka, K., Thüring, J., Rieppo, L., Aydin, R., Cyron, C., Kuhl, C., Merhof, D., Truhn, D., Nebelung, S.}, title={Machine learning-augmented and microspectroscopy-informed multiparametric MRI for the non-invasive prediction of articular cartilage composition}, year={2021}, howpublished = {journal article}, doi = {https://doi.org/10.1016/j.joca.2020.12.022}, abstract = {Once trained for the clinical setting, advanced machine learning techniques, in particular ANNs, may be used to non-invasively determine compositional features of cartilage based on quantitative MRI parameters with potential implications for the diagnosis of (early) degeneration and for the monitoring of therapeutic outcomes.}, note = {Online available at: \url{https://doi.org/10.1016/j.joca.2020.12.022} (DOI). Linka, K.; Thüring, J.; Rieppo, L.; Aydin, R.; Cyron, C.; Kuhl, C.; Merhof, D.; Truhn, D.; Nebelung, S.: Machine learning-augmented and microspectroscopy-informed multiparametric MRI for the non-invasive prediction of articular cartilage composition. Osteoarthritis and Cartilage. 2021. vol. 29, no. 4, 592-602. DOI: 10.1016/j.joca.2020.12.022}}