Multi echo techniques such as JPRESS consist of both short and long echoes and provide more diversified information for spectral fitting than techniques based on a single echo. However, fitting multi echo data is more challenging because signals attenuate with increasing echo time due to T2 relaxation, and the macromolecule background also varies across the echoes. We present a novel neural network architecture that directly maps the time domain JPRESS input onto metabolite concentrations. The testing results show the model can successfully predict in vivo metabolite concentrations from multi-echo JPRESS data after being trained with quantum mechanics simulated spectral data.
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