The Goldilocks Zone for 3‐T MRS Studies Using Semi‐LASER: Determining the Optimal Balance Between Repetition Time and Scan Time
Bibliographic record
Abstract
ABSTRACT 1 H‐MR spectroscopy studies often use a short TR to reduce scan time. However, this causes significant T 1 ‐weighting (T 1 w), which can alter metabolite estimates due to acquisition factors rather than biochemistry. Our goal was to determine the optimal balance between scan time and TR that minimizes T 1 w effects in semi‐LASER MRS at 3 T. Spectra were acquired in the posterior cingulate cortex of five healthy volunteers (2 male/3 female, mean age 25 ± 2 years) and analyzed using FSL‐MRS. The SNR and metabolite estimates of five metabolites were compared at TR = 2, 5, and 8 s, under conditions of “similar scan time” with varying acquisition numbers and “constant number of acquisitions” with varying scan times. T 1 relaxation times derived from metabolite estimates were compared to literature. With a 25% longer scan time, SNR TR = 5s was 34% higher and SNR TR = 2s was 29% higher than SNR TR = 8s for data with “similar scan times.” Using a TR = 5 s or longer, the SNR per minute is consistent for metabolites with T 1 s less than 2 s. Metabolite estimate trends were similar for the two different scenarios of “similar scan time” and “same number of acquisitions,” where all metabolite estimates were obtained without metabolite T 1 correction. The largest metabolite estimates were found at TR = 8 s, they were 10–15% lower at TR = 5 s and 15–30% lower at TR = 2 s. T 1 values agreed with literature values. At TR = 2 s, SNR per minute and metabolite estimates were lower due to reduced signal availability via T 1 w effects. TR = 8 s had the least amount of T 1 w effects, but results in lower SNR per minute. TR = 5 s had enough signal recovery to be robust to T 1 w effects, and yielded the largest SNR for similar scan times, with a clinically feasible scan time of 5 m 40 s. Using semi‐LASER MRS with a TR = 5 s is recommended to improve the sensitivity of MRS to changes in metabolite estimates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".