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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".