This approach demonstrates improved metabolite concentration assessments in deuterium metabolic imaging, suggesting advantages over traditional LCModel methods.
Motivation: Deep learning (DL) based methods have been applied for ultra-fast quantification of proton MRSI data. Combining DL-based methods and dynamic 2D-fitting for analyzing deuterium metabolic imaging (DMI) time-course data can lead to more robust and faster metabolite concentration estimations. Goal(s): Develop a DL-based pipeline for dynamic fitting of DMI data. Compare the DL approach to LCModel for performance assessment. Approach: Two different deep autoencoders (DAEs) were trained to fit in-vivo DMI datasets and were compared to LCModel. Results: Good correlation between DAE fits of (non-)denoised data and LCModel fits of denoised data was found. The DAEs outperformed LCModel fits for non-denoised data. Impact: DL-based dynamic fitting of DMI data allows ultra-fast quantification of metabolite concentration time-courses in good agreement with LCModel fits of low-rank denoised data. The proposed method is more robust against uncertainties caused by low signal-to-noise ratio (SNR) than LCModel.
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Osburg et al. (2025) studied this question.
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