Improving Accuracy and Repeatability of Cartilage T2 Mapping in the OAI Dataset through Extended Phase Graph Modeling

Motivation: Current methods for T2 fitting in the OAI dataset are based on exponential models, which are inherently sub-optimal as they do not account for stimulated echoes and B1 inhomogeneities. Goal(s): To study whether EPG-Model fitting methods improve accuracy and repeatability of T2 mapping in the OAI dataset compared to conventional methods. Approach: We set up three EPG modelling approaches: nonlinear-least-square, dictionary matching, and deep learning. We used simulations and data from the OAI dataset to evaluate accuracy, repeatability. Results: We found that EPG-based methods had higher accuracy and repeatability than exponential-based methods commonly used to compute T2 maps in the OAI dataset. Impact: We have demonstrated that EPG-based methods improved accuracy and repeatability of T2 mapping in the OAI dataset over the commonly used mono-exponential fitting methods. This permits more robust analysis of T2 information in the OAI dataset, especially in longitudinal analyses.