Keywords: Analysis/Processing, Spectroscopy
Motivation: The precise quantification of metabolites on MR spectroscopy using deep learning-based methods faces challenges regarding the low quantification accuracy of low-concentration metabolites.
Goal(s): A new deep learning approach is proposed to accurately quantify low-concentration metabolites even in the absence of a comprehensive experimental training dataset, validated for performance across multi-vendor datasets.
Approach: In this work, a method called DMnet is proposed to achieve precise quantification of low-concentration metabolites using a differentiable least squares layer network.
Results: The results show that compared to state-of-the-art quantification methods, the proposed method exhibits lower MAPE for metabolite concentrations in simulated data and lower residuals in experimental data.
Impact: The proposed method utilizes a differentiable least squares network layer to achieve precise quantification of low-concentration metabolites, providing more accurate quantification metrics for disease diagnosis.
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