Evaluation of magnetic field homogeneity using in-out signal cycle mapping in gradient recalled echo images of a mixed water/oil phantom as a rough indication for daily quality control
Why this work is in the frame
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Bibliographic record
Abstract
Objective: Magnetic field (B 0 ) homogeneity is important for the performance of a magnetic resonance imaging (MRI) system. Traditionally, B 0 homogeneity was measured using the spectral peak or phase-mapping methods. However, these procedures are not generally accessible to the MRI operator and are rarely performed routinely. This study proposes a novel method for measuring B 0 homogeneity that can be implemented in daily quality control (QC). Methods: When a uniformly mixed water/oil phantom was imaged using a gradient recalled echo (GRE) pulse sequence, the signal intensity dynamically changed with echo time (TE). From this, the resonant frequency was calculated with a simplex curve-fitting algorithm on a pixel-by-pixel basis. The standard deviation of resonant frequency (SD) was used as the index of B 0 homogeneity. The appropriate TE pattern and feasibility of B 0 homogeneity evaluation were examined. Results: Over seven TEs (choosing nominal in-phase, out-phase, and the midpoints of both) were required to measure stable SD in a 1.5-T scanner. As B 0 homogeneity worsened, the SD became larger at the off-center position. Although a positive correlation was observed with the width of the spectral peak obtained by the phase-difference method, the SD value was about 5 × 10 4 times greater. Therefore, SD can be used only as an index of B 0 homogeneity. Similar results were obtained using a 0.3-T scanner. A map and SD can be obtained by acquiring several GRE images of a water/oil mixed phantom within a few minutes. Conclusions: In-out signal cycle mapping can be easily implemented for daily QC in all MRI scanners.
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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 | 0.004 | 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 it