Comprehensive Summary
The hybrid synthetic data–guided deep learning (DL) network (HSGDNet) is explored as an alternative to computationally intensive nonlinear least squares (NLS)-based methods for faster spin–lattice relaxation in the rotating frame (T1ρ) knee mapping. Utilizing 12 healthy volunteers and one with early osteoarthritis (EOA), the two methods were compared in terms of mono-exponential (ME), stretched-exponential (SE), and bi-exponential (BE) components. HSGDNet achieved error reductions of approximately 91.4 % (ME), 31.5 % (SE) and 36.0 % (BE) compared with NLS, while speeding up whole-knee T1ρ fitting by ~67.4×, 53.9× and 42.3×, respectively. Although the author states HSGDNet is a promising alternative, it is important to note that regularized nonlinear least squares (RNLS) were used as a ground truth in which the models were compared against. To minimize systematic error, future studies should use well-characterized reference objects with known T1ρ relaxation times.
Outcomes and Implications
T1ρ mapping of the knee joint using traditional NLS-based methods is time consuming and computationally intensive. By confirming greater accuracy and efficiency of HSGDNet methods over NLS ones, the clinical usage of T1ρ mapping has the ability to become more widespread. Specifically, medical providers will be able to more readily use T1ρ in accurately assessing knee joint health and diagnosing various musculoskeletal conditions. The authors state that the current HSGDNet is able to consistently outperform other models, efficiently processes data from EOA patients, and accurately model pathological conditions. Although this suggests the model is ready for clinical implementation, several improvements, such as larger and more diverse clinical datasets, are suggested to further validate HSGDNet’s usage.