Recurrent neural network-based simultaneous cardiac T1, T2, and T1 mapping

The purpose of the current study was to explore the feasibility of training a deep neural network to accelerate the process of generating T1, T2, and T1 rho maps for a recently proposed free-breathing cardiac multiparametric mapping technique, where a recurrent neural network (RNN) was utilized to exploit the temporal correlation among the multicontrast images. The RNN-based model was developed for rapid and accurate T1, T2, and T1 rho estimation. Bloch simulation was performed to simulate a dataset of more than 10 million signals and time correspondences with different noise levels for network training. The proposed RNN-based method was compared with a dictionary-matching method and a conventional mapping method to evaluate the model's effectiveness in phantom and in vivo studies at 3 T, respectively. In phantom studies, the RNN-based method and the dictionary-matching method achieved similar accuracy and precision in T1, T2, and T1 rho estimations. In in vivo studies, the estimated T1, T2, and T1 rho values obtained by the two methods achieved similar accuracy and precision for 10 healthy volunteers (T1: 1228.70 +/- 53.80 vs. 1228.34 +/- 52.91 ms, p > 0.1; T2: 40.70 +/- 2.89 vs. 41.19 +/- 2.91 ms, p > 0.1; T1 rho: 45.09 +/- 4.47 vs. 45.23 +/- 4.65 ms, p > 0.1). The RNN-based method can generate cardiac multiparameter quantitative maps simultaneously in just 2 s, achieving 60-fold acceleration compared with the dictionary-matching method. The RNN-accelerated method offers an almost instantaneous approach for reconstructing accurate T1, T2, and T1 rho maps, being much more efficient than the dictionary-matching method for the free-breathing multiparametric cardiac mapping technique, which may pave the way for inline mapping in clinical applications.