Fast, Accurate, and Robust T2 Mapping of Articular Cartilage by Neural Networks

For T2 mapping, the underlying mono-exponential signal decay is traditionally quantified by non-linear Least-Squares Estimation (LSE) curve fitting, which is prone to outliers and computationally expensive. This study aimed to validate a fully connected neural network (NN) to estimate T2 relaxation times and to assess its performance versus LSE fitting methods. To this end, the NN was trained and tested in silico on a synthetic dataset of 75 million signal decays. Its quantification error was comparatively evaluated against three LSE methods, i.e., traditional methods without any modification, with an offset, and one with noise correction. Following in-situ acquisition of T2 maps in seven human cadaveric knee joint specimens at high and low signal-to-noise ratios, the NN and LSE methods were used to estimate the T2 relaxation times of the manually segmented patellofemoral cartilage. In-silico modeling at low signal-to-noise ratio indicated significantly lower quantification error for the NN (by medians of 6-33%) than for the LSE methods (p < 0.001). These results were confirmed by the in-situ measurements (medians of 10-35%). T2 quantification by the NN took only 4 s, which was faster than the LSE methods (28-43 s). In conclusion, NNs provide fast, accurate, and robust quantification of T2 relaxation times.

Automated summary

This study validated the accuracy and performance of a fully connected neural network (NN) in estimating T2 relaxation times and compared it with traditional non-linear least-squares estimation (LSE) methods. The results showed that NN exhibited lower quantification error and faster computation speed in both synthetic modeling and in-situ measurements.