Atlas‐based automated positioning of outer volume suppression slices in short‐echo time 3D MR spectroscopic imaging of the human brain
Bibliographic record
Abstract
Spatial suppression of peripheral lipid-containing regions in volumetric MR spectroscopic imaging of the human brain requires placing large numbers of outer volume suppression (OVS) slices, which is time-consuming, prone to operator error and may introduce subject-dependent variability in volume coverage. We developed a novel, computationally efficient atlas-based approach for automated positioning of up to 16 OVS slices and the MR spectroscopic imaging slab. Standardized positions in Montreal Neurological Institute atlas space were established offline using a recently developed iterative optimization procedure. During the scanning session, positions in subject space were computed using affine transformation of standardized positions in Montreal Neurological Institute space. Offline analysis using magnetization prepared rapid gradient echo scans from 11 subjects demonstrated reliable OVS placement, comparable with but faster than iterative placement in subject space. This atlas-based method was further validated in 14 subjects using 3D short-echo time proton-echo-planar-spectroscopic-imaging at 3 T. Comparison of manual and automatic placement using 8 OVS slices demonstrated consistent MR spectroscopic imaging volume selection and comparable spectral quality with similar degree of lipid suppression and number of usable voxels. Automated positioning of 16 OVS slices enabled larger volume coverage, while maintaining similar spectral quality and lipid suppression. Atlas-based automatic prescription of short echo time MR spectroscopic imaging is expected to be advantageous for longitudinal and cross-sectional 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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".