A fully flow-compensated multiecho susceptibility-weighted imaging sequence: The effects of acceleration and background field on flow compensation
Bibliographic record
Abstract
PURPOSE: To present a fully flow-compensated multiecho gradient echo sequence that can be used for MR angiography (MRA), susceptibility weighted imaging (SWI), and quantitative susceptibility mapping (QSM) and to study the effects of flow acceleration and background field gradients on flow compensation. METHODS: The quality of flow compensation was evaluated using the data from eight volunteers. The effects of flow acceleration were studied by changing the polarities of the readout gradients in two consecutive scans. The background field was used to estimate the phase errors of flow compensation in the presence of field inhomogeneities. SWI and QSM data were generated with confounding arterial phase removed. T2 * maps were obtained from the multiecho data to estimate T2 * of arterial blood. RESULTS: Reasonable flow compensation was achieved. Nevertheless, background field gradients and acceleration-induced phase errors were found to be as large as π/2 and π/3, respectively, both in agreement with theory. T2 * was measured as 82 ± 4 ms and 74 ± 9 ms for arteries inside and outside the brain, respectively, at 3T. CONCLUSION: High-quality MRA, SWI, and QSM data can be obtained simultaneously. Masking out the arteries to remove the phase due to flow acceleration and background field gradients improves the quality of both SWI and QSM data. Magn Reson Med 76:478-489, 2016. © 2015 Wiley Periodicals, Inc.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".