Catalytic multiecho phase unwrapping scheme (CAMPUS) in multiecho gradient echo imaging: Removing phase wraps on a voxel‐by‐voxel basis
Bibliographic record
Abstract
Phase images reflect local field variations including susceptibility contributions and play a key role in a number of imaging applications. However, due to the limited dynamic range of phase values, phase wrapping invariably occurs at long echo times. High pass filtering and region-growing approaches have been common themes in the effort of removing phase wraps. In this article, a novel voxel-by-voxel phase unwrapping scheme taking advantage of short interecho spacing of multiecho gradient echo imaging is proposed. By removing spurious sources of phase variations and exploiting the special features of the flow-induced phase component, phase increments during adjacent echoes can be unveiled that exhibit no phase aliasing. This unaliased phase information is then used to unwrap all the phase images at all of the echoes. Data of 15 volunteers scanned at 3 and 1.5T were processed with the proposed algorithm and two other algorithms in the literature (PhUN and branch cut). It is shown that the proposed approach is fast and effective in unwrapping all the phase values even for voxels in the eyes and the skull, which the other algorithms failed to unwrap. Thus, in multiecho gradient echo imaging, the proposed algorithm has major potential in various applications involving phase processing.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".