Velocity encoding with the slice select refocusing gradient for faster imaging and reduced chemical shift‐induced phase errors
Bibliographic record
Abstract
PURPOSE: To investigate a novel phase-contrast MRI velocity-encoding technique for faster imaging and reduced chemical shift-induced phase errors. METHODS: Velocity encoding with the slice select refocusing gradient achieves the target gradient moment by time shifting the refocusing gradient, which enables the use of the minimum in-phase echo time (TE) for faster imaging and reduced chemical shift-induced phase errors. Net forward flow was compared in 10 healthy subjects (N = 10) within the ascending aorta (aAo), main pulmonary artery (PA), and right/left pulmonary arteries (RPA/LPA) using conventional flow compensated and flow encoded (401 Hz/px and TE = 3.08 ms) and slice select refocused gradient velocity encoding (814 Hz/px and TE = 2.46 ms) at 3 T. RESULTS: Improved net forward flow agreement was measured across all vessels for slice select refocused gradient compared to flow compensated and flow encoded: aAo vs. PA (1.7% ± 1.9% vs. 5.8% ± 2.8%, P = 0.002), aAo vs. RPA + LPA (2.1% ± 1.7% vs. 6.0% ± 4.3%, P = 0.03), and PA vs. RPA + LPA (2.9% ± 2.1% vs. 6.1% ± 6.3%, P = 0.04), while increasing temporal resolution (35%) and signal-to-noise ratio (33%). CONCLUSION: Slice select refocused gradient phase-contrast MRI with a high receiver bandwidth and minimum in-phase TE provides more accurate and less variable flow measurements through the reduction of chemical shift-induced phase errors and a reduced TE/repetition time, which can be used to increase the temporal/spatial resolution and/or reduce breath hold durations.
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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) | 0.000 | 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".