Sci-PM Thurs - 04: A comparative study between multi-station and moving-table methods with steady-state free precession
Bibliographic record
Abstract
Large field-of-view (FOV) imaging techniques, such as the multi-station and moving-table techniques, are necessary to image systemic diseases such as peripheral vascular disease and metastases. In the multi-station technique, the full k-space is acquired for each station, i.e., at each local FOV, and the images are combined offline. For the moving-table method, the scanner bed is continuously moved through the local FOV during a single acquisition, creating a single large FOV image. Steady-state free precession is a pulse sequence capable of rapid image data acquisition. This study compares large FOV images of healthy volunteers using both the moving-table method and the multi-station technique using an SSFP pulse sequence. In the multi-station technique, six 32 s-acquisition's are required to cover the large FOV. For the moving table method, the hybrid k-space is collected during a single 150 s-scan, creating a seamless large FOV image. Although the moving-table method is more time-efficient than the multi-station technique, image quality is sacrificed. This quality reduction is due to non-steady-state conditions caused by table motion and because the k-space data is partially sampled. By optimizing this moving-table SSFP technique and integrating it with a tissue suppression algorithm, it may be possible to perform non-contrast enhanced MR angiograms of the entire peripheral vasculature. Thus, the technique could provide a non-invasive and time-efficient method for producing seamless large FOV images for diagnosis of systemic diseases.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".