Improving spatial signal homogeneity in MR 2D chemical shift imaging using outer volume saturation bands
Bibliographic record
Abstract
Many endeavors of improving chemical shift imaging (CSI) techniques have been made during last two decades. Good examples of two-dimensional CSI and three-dimensional CSI can be found in the literature. However, clinical CSI using available sequences is still not satisfactory. The purpose of this study was to assess the effect of outer volume saturation bands on signal homogeneity in MR 2D chemical shift imaging. The 2D CSI scans were acquired using a point resolved spectroscopy (PRESS) CSI sequence on a phantom filled with brain metabolites. A single PRESS volume of interest was prescribed graphically. The acquisition matrix was 18x18 phase encodings over a 24-cm FOV. Identical acquisitions were obtained with and without outer-volume saturation bands. After initial acquisition was obtained, four more acquisitions were repeated for both studies with and without saturation bands. Identical five groups of voxels were compared for both studies. Standard deviations of metabolite ratios were calculated in each group for both studies. Spectra obtained without outer-volume saturation bands showed signal to noise gradient with higher concentration of signal within voxels at the center of the volume of interest. Outer volume saturation bands reduced this gradient. In general, standard deviations of metabolite ratios with saturation bands were smaller than those without saturation bands. Improved spatial homogeneity of spectra in voxels of CSI with saturation bands was obtained. Outer-volume saturation bands improve spatial signal homogeneity of chemical shift imaging.
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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 | 0.002 | 0.004 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".