Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial Intelligence
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.
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