Automatic repositioning of single voxels in longitudinal <sup>1</sup>H MRS studies
Bibliographic record
Abstract
An automatic procedure, allowing the prospective registration of brain MRI images and the acquisition of nearly identical brain volumes (coverage and orientation) in longitudinal exams, is presented. This procedure, based on a fast registration algorithm and a tailored pulse sequence, is used to reposition single voxels for 1H MRS data acquired in vivo. The impact of the repositioning method on the extent of voxel overlap and on the reproducibility of metabolite concentration measurements is studied. A statistically significant increase in voxel overlap and generally decreased short-term measurement variability (decreased coefficients of variation and increased reproducibility coefficients) are observed. Differences in the long-term variances of metabolite concentrations and concentration ratios measured using the eye and automatic repositioning scheme, however, do not reach statistical significance. The improved workflow associated with the use of the automatic repositioning process, which obviates the need for skilled operator intervention for voxel repositioning, suggests that approaches similar to the one presented here may be a standard element in tomorrow's longitudinal MRI and MRS exams.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".