Quantitative proton short‐echo‐time LASER spectroscopy of normal human white matter and hippocampus at 4 Tesla incorporating macromolecule subtraction
Bibliographic record
Abstract
Accurate quantification of in vivo short-echo-time (TE) (1)H spectra must account for contributions from both mobile metabolites and less mobile macromolecules, which can fluctuate in disease. The purpose of this study was to develop an approach for the acquisition and processing of macromolecule information to optimize metabolite quantification accuracy and precision. Human parietal white matter (8-cm(3) voxel) and posterior hippocampus (1.7-cm(3) voxel) metabolite levels were quantified, following manomolecule subtraction, from short-echo-time spectra (TE = 46 ms) acquired at 4.0 Tesla with localization by adiabatic selective refocusing (LASER). Nineteen metabolites were fit using a time domain Levenberg-Marquardt minimization that incorporated prior knowledge of metabolite lineshapes. The macromolecule contribution to the spectrum was reduced by 87% (P < 0.05) when the acquisition of single averages of the full spectrum and macromolecule spectrum were interleaved to reduce subtraction errors due to motion. Subtracting the Hankel Lanczos singular value decomposition (HLSVD) fit of the macromolecule spectrum, which contained no random noise, did not alter quantified metabolite levels but did not increase metabolite quantification precision. Several metabolites had higher concentrations in the posterior hippocampus compared to parietal white matter, which emphasizes the need to carefully control for partial volume contamination in hippocampal spectroscopy studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".