ArticleMagnetic resonance in medicine2024
Simultaneous multi-transient linear-combination modeling of MRS data improves uncertainty estimation.
Article in Magnetic resonance in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- A data-driven algorithm to determineMagnetic resonance in medicine · 2026Article
- Reproducibility of Metabolic Mapping Using 2D Multi-Slice Short-TE and GABA-Edited Spin-Echo MRSI at 3T in a Single Protocol.NMR in biomedicine · 2026Article
- Age dependency of neurometabolite TMagnetic resonance in medicine · 2025Article
- Metabolite TMagnetic resonance in medicine · 2025Article
- Model-based frequency-and-phase correction ofMagnetic resonance in medicine · 2024Article
- Simultaneous multi-transient linear-combination modeling of MRS data improves uncertainty estimation.Magnetic resonance in medicine · 2024Article
- Metabolite TbioRxiv : the preprint server for biology · 2024Article
- Model-based frequency-and-phase correction ofbioRxiv : the preprint server for biology · 2024Article
- Simultaneous multi-transient linear-combination modeling of MRS data improves uncertainty estimation.bioRxiv : the preprint server for biology · 2024Article
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Abstract
purposeThe interest in applying and modeling dynamic MRS has recently grown. Two-dimensional modeling yields advantages for the precision of metabolite estimation in interrelated MRS data. However, it is unknown whether including all transients simultaneously in a 2D model without averaging (presuming a stable signal) performs similarly to one-dimensional (1D) modeling of the averaged spectrum. Therefore, we systematically investigated the accuracy, precision, and uncertainty estimation of both described model approaches.
methodsMonte Carlo simulations of synthetic MRS data were used to compare the accuracy and uncertainty estimation of simultaneous 2D multitransient linear-combination modeling (LCM) with 1D-LCM of the average. A total of 2,500 data sets per condition with different noise representations of a 64-transient MRS experiment at six signal-to-noise levels for two separate spin systems (scyllo-inositol and gamma-aminobutyric acid) were analyzed. Additional data sets with different levels of noise correlation were also analyzed. Modeling accuracy was assessed by determining the relative bias of the estimated amplitudes against the ground truth, and modeling precision was determined by SDs and Cramér-Rao lower bounds (CRLBs).
resultsAmplitude estimates for 1D- and 2D-LCM agreed well and showed a similar level of bias compared with the ground truth. Estimated CRLBs agreed well between both models and with ground-truth CRLBs. For correlated noise, the estimated CRLBs increased with the correlation strength for the 1D-LCM but remained stable for the 2D-LCM.
conclusionOur results indicate that the model performance of 2D multitransient LCM is similar to averaged 1D-LCM. This validation on a simplified scenario serves as a necessary basis for further applications of 2D modeling.
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