Systematic image alteration due to phase accumulation during <scp>RF</scp> pulse excitation in pure phase encode magnetic resonance imaging
Bibliographic record
Abstract
Abstract The SPI/SPRITE class of techniques in magnetic resonance imaging are pure phase encode methods that are well established for systems with short transverse signal lifetimes. Applying a broadband radio‐frequency pulse in the presence of a magnetic field gradient is unconventional in MRI but fundamental to these methods. Ordinarily, it is assumed that the excitation is instantaneous and any possible phase evolution during the RF pulse is ignored. High quality, quantitative imaging of a variety of samples over many years suggests that the off‐resonance effects of the RF pulse, with consequent phase accumulation during the pulse, are not significant. However, a reconsideration of the RF pulse behavior in related work has shown that phase accumulation during the pulse may be non‐negligible in some circumstances. The effect of phase accumulation during the RF pulse is investigated through simulation of one‐dimensional SPI experiments and is shown to manifest as a systematic scaling of the image field‐of‐view (FOV). The FOV scaling effect is also verified experimentally. One‐dimensional profiles of a cylindrical elastomer sample were acquired employing a 2.4 T horizontal bore magnet. Experiments were undertaken with variation of the experimental RF pulse duration. Under typical experimental parameters, neglecting the phase accumulation during the RF pulse is acceptable.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".