Partial <i>k</i>‐space reconstruction in single‐shot diffusion‐weighted echo‐planar imaging
Bibliographic record
Abstract
Partial k-space sampling is frequently used in single-shot diffusion-weighted echo-planar imaging (DW-EPI) to reduce the TE and thereby improve the SNR. However, it increases the sensitivity of the technique to bulk rotational motion, which introduces a phase gradient across the tissue that shifts the echo in k-space. If the echo is displaced into the high spatial frequencies, conventional homodyne reconstruction fails, causing intensity oscillations across the image. Zero-padding, on the other hand, compromises the image resolution and may cause truncation artifacts. We present an adaptive version of the homodyne algorithm that detects the location of the echo in k-space and adjusts the center and width of the homodyne filters accordingly. The adaptive algorithm produces artifact-free images when the echo is shifted into the high positive k-space range, and reduces to the standard homodyne algorithm in the absence of bulk motion.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".