Simple compensation method for improved half‐pulse excitation profile with rephasing gradient
Bibliographic record
Abstract
PURPOSE: To improve the slice profile quality obtained by RF half-pulse excitation for 2D-UTE applications. METHODS: The overall first-order and zero-order phase errors along the slice-selection direction were obtained with the help of an optimization task to minimize the out-of-slice signal contamination from the calibration 1-dimenisonal (1D) profile data. The time-phase-error evolution was approximated from the k-space readout data, which were acquired primarily for correction of the readout trajectories during data regridding to the rectilinear grids. The correction of the slice profile was achieved by rephasing gradient pulses applied immediately after the end of excitation. The total prescan calibration typically took less than 2 minutes. RESULTS: The improved image quality using the proposed calibration method was demonstrated both on phantoms and on ankle images obtained from healthy volunteers. It was demonstrated that calibration can be performed either as a separate water phantom measurement or directly as a prescan procedure. CONCLUSION: The slice-profile distortion from the half-pulse excitation could substantially affect the overall fidelity of 2D-UTE images. The presented experiments proved that the image quality could be substantially increased by application of the proposed slice-correction method.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".