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This synthetic benchmark supports development and comparison of MRSI processing, nuisance-signal removal, and metabolite-quantification methods, while enabling method evaluation, and illustrates how characterized synthetic data can support rigorous evaluation when ground truth is difficult or impossible to obtain.
Dynamic deuterium metabolic imaging (DMI) enables time-resolved mapping of cerebral glucose metabolism in vivo, yet its intrinsically low SNR often renders voxel-wise metabolite quantification unstable-particularly at early repetitions. Low-rank denoising is widely used in MR spectroscopic imaging (MRSI)/DMI to improve robustness, but very low-SNR regimes and dynamic studies remain challenging. Self-supervised learning is promising for DMI/MRSI denoising, but its performance depends strongly on the representation domain and the noise assumptions underlying the training objective Here, we study a pragmatic denoising pipeline for dynamic DMI/MRSI that combines a mild low-rank stabilization with self-supervised learning in the spectral-temporal (f×T) domain. Exploiting metabolite-specific spectral structure and redundancy across repeated measurements, our approach relies on two mild assumptions: (i) additive, approximately zero-mean noise and (ii) approximate noise independence across repeated acquisitions. These conditions are expected to be satisfied across essentially any reconstruction and preprocessing pipeline. Our results indicate that the f×T domain is effective both with spatially correlated and approximately uncorrelated noise. We evaluate the approach in simulations and in vivo dynamic DMI data from healthy volunteers (n=6) and a brain tumor patient. The proposed pipeline improves the robustness of time-resolved metabolite estimates and increases LCModel fit stability relative to a state-of-the-art low-rank baseline (tMPPCA), with the largest gains for weak metabolites and early low-SNR repetitions. Together, this enables more reliable dynamic metabolite mapping in low-SNR regimes.
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