Phase and amplitude correction for multi‐echo water–fat separation with bipolar acquisitions
Bibliographic record
Abstract
PURPOSE: To address phase and amplitude errors for multi-point water-fat separation with "bipolar" acquisitions, which efficiently collect all echoes with alternating read-out gradient polarities in one repetition. MATERIALS AND METHODS: With the bipolar acquisitions, eddy currents and other system nonidealities can induce inconsistent phase errors between echoes, disrupting water-fat separation. Previous studies have addressed phase correction in the read-out direction. However, the bipolar acquisitions may be subject to spatially high order phase errors as well as an amplitude modulation in the read-out direction. A method to correct for the 2D phase and amplitude errors is introduced. Low resolution reference data with reversed gradient polarities are collected. From the pair of low-resolution data collected with opposite gradient polarities, the two-dimensional phase errors are estimated and corrected. The pair of data are then combined for water-fat separation. RESULTS: We demonstrate that the proposed method can effectively remove the high order errors with phantom and in vivo experiments, including obliquely oriented scans. CONCLUSION: For bipolar multi-echo acquisitions, uniform water-fat separation can be achieved by removing high order phase errors with the proposed 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".