DeepEMC-T2 Mapping: Deep Learning-Enabled Echo Modulation Curve T2 Mapping

Motivation: Echo Modulation Curve T2 Mapping (EMC-T2) mapping can generate highly accurate, precise, and reproducible T2 quantification. However, the standard EMC-T2 framework requires ~10 echoes and a cumbersome post-processing step for pixel-wise dictionary matching. Goal(s): This work proposes a deep learning version of EMC-T2 mapping, called DeepEMC-T2, to enable efficient and accurate estimation of T2 maps from fewer echoes without requiring a dictionary. Approach: DeepEMC-T2 was developed using a spatiotemporal convolutional neural network, which estimates both T2 and PD maps directly from multi-echo spin-echo images. Results: DeepEMC-T2 enables efficient and accurate T2 mapping and requires only smaller number of echoes compared to standard EMC-T2. Impact: Standard EMC-T2 enables accurate T2 quantification but previously required a complicated post-processing step that made clinical translation challenging. DeepEMC-T2 enables efficient and accurate T2 quantification with fewer echoes. This could facilitate more widespread translation of this technique into clinical practice.