Phase and amplitude correction for multi‐echo water–fat separation with bipolar acquisitions
Why this work is in the frame
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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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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 | 0.000 |
| 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 it