%0 Journal Article %1 SEYEDPOUR2021104963 %A Seyedpour, S.M. %A Nafisi, S. %A Nabati, M. %A Pierce, D.M. %A Reichenbach, J.R. %A Ricken, T. %D 2021 %J Journal of the Mechanical Behavior of Biomedical Materials %K (MRI), Biomechanics, Cartilage, Clinical Constitutive Imaging Magnetic Osteoarthritis Resonance application, model, myown %P 104963 %R https://doi.org/10.1016/j.jmbbm.2021.104963 %T Magnetic Resonance Imaging–based biomechanical simulation of cartilage: A systematic review %U https://www.sciencedirect.com/science/article/pii/S1751616121005932 %X MRI-based mathematical and computational modelling studies can contribute to a better understanding of the mechanisms governing cartilage’s mechanical performance and cartilage disease. In addition, distinct modelling of cartilage is needed to optimize artificial cartilage production. These studies have opened up the prospect of further deepening our understanding of cartilage function. Furthermore, these studies reveal the initiation of an engineering-level approach to how cartilage disease affects material properties and cartilage function. Aimed at researchers in the field of MRI-based cartilage simulation, research articles pertinent to MRI-based cartilage modelling were identified, reviewed, and summarized systematically. Various MRI applications for cartilage modelling are highlighted, and the limitations of different constitutive models used are addressed. In addition, the clinical application of simulations and studied diseases are discussed. The paper’s quality, based on the developed questionnaire, was assessed, and out of 79 reviewed papers, 34 papers were determined as high-quality. Due to the lack of the best constitutive models for various clinical conditions, researchers may consider the effect of constitutive material models on the cartilage disease simulation. In the future, research groups may incorporate various aspects of machine learning into constitutive models and MRI data extraction to further refine the study methodology. Moreover, researchers should strive for further reproducibility and rigorous model validation and verification, such as gait analysis.

@article{SEYEDPOUR2021104963, abstract = {MRI-based mathematical and computational modelling studies can contribute to a better understanding of the mechanisms governing cartilage’s mechanical performance and cartilage disease. In addition, distinct modelling of cartilage is needed to optimize artificial cartilage production. These studies have opened up the prospect of further deepening our understanding of cartilage function. Furthermore, these studies reveal the initiation of an engineering-level approach to how cartilage disease affects material properties and cartilage function. Aimed at researchers in the field of MRI-based cartilage simulation, research articles pertinent to MRI-based cartilage modelling were identified, reviewed, and summarized systematically. Various MRI applications for cartilage modelling are highlighted, and the limitations of different constitutive models used are addressed. In addition, the clinical application of simulations and studied diseases are discussed. The paper’s quality, based on the developed questionnaire, was assessed, and out of 79 reviewed papers, 34 papers were determined as high-quality. Due to the lack of the best constitutive models for various clinical conditions, researchers may consider the effect of constitutive material models on the cartilage disease simulation. In the future, research groups may incorporate various aspects of machine learning into constitutive models and MRI data extraction to further refine the study methodology. Moreover, researchers should strive for further reproducibility and rigorous model validation and verification, such as gait analysis.}, added-at = {2021-12-03T15:05:39.000+0100}, author = {Seyedpour, S.M. and Nafisi, S. and Nabati, M. and Pierce, D.M. and Reichenbach, J.R. and Ricken, T.}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2bba87f6dabbfab63d7f634095482c039/morteza}, doi = {https://doi.org/10.1016/j.jmbbm.2021.104963}, interhash = {eb2819af85861bf965bd47592252b3f6}, intrahash = {bba87f6dabbfab63d7f634095482c039}, issn = {1751-6161}, journal = {Journal of the Mechanical Behavior of Biomedical Materials}, keywords = {(MRI), Biomechanics, Cartilage, Clinical Constitutive Imaging Magnetic Osteoarthritis Resonance application, model, myown}, pages = 104963, timestamp = {2022-11-24T09:23:49.000+0100}, title = {Magnetic Resonance Imaging–based biomechanical simulation of cartilage: A systematic review}, url = {https://www.sciencedirect.com/science/article/pii/S1751616121005932}, year = 2021 }