DeepEMC-T₂ mapping: Deep learning–enabled T₂ mapping based on echo modulation curve modeling

Purpose: Echo modulation curve (EMC) modeling enables accurate quantification of T₂ relaxation times in multi-echo spin-echo (MESE) imaging. The standard EMC-T₂ mapping framework, however, requires sufficient echoes and cumbersome pixel-wise dictionary-matching steps. This work proposes a deep learning version of EMC-T₂ mapping, called DeepEMC-T₂ mapping, to efficiently estimate accurate T₂ maps from fewer echoes. Methods: DeepEMC-T₂ mapping was developed using a modified U-Net to estimate both T₂ and proton density (PD) maps directly from MESE images. The network implements several new features to improve the accuracy of T₂/PD estimation. A total of 67 MESE datasets acquired in axial orientation were used for network training and evaluation. An additional 57 datasets acquired in coronal orientation with different scan parameters were used to evaluate the generalizability of the framework. The performance of DeepEMC-T₂ mapping was evaluated in seven experiments. Results: Compared to the reference, DeepEMC-T₂ mapping achieved T₂ estimation errors from 1% to 11% and PD estimation errors from 0.4% to 1.5% with ten/seven/five/three echoes, which are more accurate than standard EMC-T₂ mapping. By incorporating datasets acquired with different scan parameters and orientations for joint training, DeepEMC-T₂ exhibits robust generalizability across varying imaging protocols. Increasing the echo spacing and including longer echoes improve the accuracy of parameter estimation. The new features proposed in DeepEMC-T₂ mapping all enabled more accurate T₂ estimation. Conclusions: DeepEMC-T₂ mapping enables simplified, efficient, and accurate T₂ quantification directly from MESE images without dictionary matching. Accurate T₂ estimation from fewer echoes allows for increased volumetric coverage and/or higher slice resolution without prolonging total scan times.