DeepFittingNet: A deep neural network-based approach for simplifying cardiac T₁ and T₂ estimation with improved robustness

Purpose: To develop and evaluate a deep neural network (DeepFittingNet) for T₁/T₂ estimation of the most commonly used cardiovascular MR mapping sequences to simplify data processing and improve robustness. Theory and Methods: DeepFittingNet is a 1D neural network composed of a recurrent neural network (RNN) and a fully connected (FCNN) neural network, in which RNN adapts to the different number of input signals from various sequences and FCNN subsequently predicts A, B, and T_x of a three-parameter model. DeepFittingNet was trained using Bloch-equation simulations of MOLLI and saturation-recovery single-shot acquisition (SASHA) T₁ mapping sequences, and T₂-prepared balanced SSFP (T₂-prep bSSFP) T₂ mapping sequence, with reference values from the curve-fitting method. Several imaging confounders were simulated to improve robustness. The trained DeepFittingNet was tested using phantom and in-vivo signals, and compared to the curve-fitting algorithm. Results: In testing, DeepFittingNet performed T₁/T₂ estimation of four sequences with improved robustness in inversion-recovery T₁ estimation. The mean bias in phantom T₁ and T₂ between the curve-fitting and DeepFittingNet was smaller than 30 and 1 ms, respectively. Excellent agreements between both methods was found in the left ventricle and septum T₁/T₂ with a mean bias <6 ms. There was no significant difference in the SD of both the left ventricle and septum T₁/T₂ between the two methods. Conclusion: DeepFittingNet trained with simulations of MOLLI, SASHA, and T₂-prep bSSFP performed T₁/T₂ estimation tasks for all these most used sequences. Compared with the curve-fitting algorithm, DeepFittingNet improved the robustness for inversion-recovery T₁ estimation and had comparable performance in terms of accuracy and precision.