Effects of RF inhomogeneity at 3.0T on ramped RF excitation: Application to 3D time‐of‐flight MR angiography of the intracranial arteries
Bibliographic record
Abstract
PURPOSE: To demonstrate the effects of inherent RF inhomogeneity on ramped RF excitation at 3.0T, and to introduce a simple correction for improving visualization of distal intracranial arteries in three-dimensional time-of-flight MR angiography (3D-TOF-MRA). MATERIALS AND METHODS: At 3.0T, the effects of RF inhomogeneity arising from RF interference were demonstrated for ramped RF excitation in intracranial 3D-TOF-MRA. Computer simulations and experiments on phantoms and eight normal volunteers were performed. Four different ramp shapes were tested as a possible means of countering the reduced RF field that affects the distal intracranial arteries. RESULTS: RF destructive interference alters the ramp pulse shape, which is problematic for vessels that proceed from the center to the edge of the brain. Increasing the ramp pulse slope was shown to be an effective yet simple correction to counter the falling-off of the RF field toward the periphery of the head. With this approach, circle-of-Willis 3D-TOF-MRA studies had improved distal visibility. CONCLUSION: Ramped RF excitation is severely affected by RF interference at 3.0T, which makes the ramp profile suboptimal for distal intracranial blood vessels. A simple correction of the ramp slope can make a marked improvement.
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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 | 0.002 |
| 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".