Fast and accurate calculation of myocardial T and T values using deep learning Bloch equation simulations (DeepBLESS).

PURPOSE

To propose and evaluate a deep learning model for rapid and accurate calculation of myocardial T /T values based on a previously proposed Bloch equation simulation with slice profile correction (BLESSPC) method.

METHODS

Deep learning Bloch equation simulations (DeepBLESS) models are proposed for rapid and accurate T estimation for the MOLLI T mapping sequence with balanced SSFP readouts and T /T estimation for a radial simultaneous T and T mapping (radial T -T ) sequence. The DeepBLESS models were trained separately based on simulated radial T -T and MOLLI data, respectively. The DeepBLESS T -T estimation accuracy was evaluated based on simulated data with different noise levels. The DeepBLESS model was compared with BLESSPC in simulation, phantom, and in vivo studies for the MOLLI sequence at 1.5 T and radial T -T sequence at 3 T.

RESULTS

After DeepBLESS was trained, in phantom studies, DeepBLESS and BLESSPC achieved similar accuracy and precision in T -T estimations for both MOLLI and radial T -T (P > .05). For in vivo, DeepBLESS and BLESSPC generated similar myocardial T /T values for radial T -T at 3 T (T : 1366 ± 31 ms for both methods, P > .05; T : 37.4 ms ± 0.9 ms for both methods, P > .05), and similar myocardial T values for the MOLLI sequence at 1.5 T (1044 ± 20 ms for both methods, P > .05). DeepBLESS generated a T /T map in less than 1 second.

CONCLUSION

The DeepBLESS model offers an almost instantaneous approach for estimating accurate T /T values, replacing BLESSPC for both MOLLI and radial T -T sequences, and is promising for multiparametric mapping in cardiac MRI.