Results for diffusion‐weighted imaging with a fourth‐channel gradient insert
Bibliographic record
Abstract
Diffusion-weighted imaging suffers from motion artifacts and relatively low signal quality due to the long echo times required to permit the diffusion encoding. We investigated the inclusion of a noncylindrical fourth gradient coil, dedicated entirely to diffusion encoding, into the imaging system. Standard three-axis whole body gradients were used during image acquisition, but we designed and constructed an insert coil to perform diffusion encodings. We imaged three phantoms on a 3-T system with a range of diffusion coefficients. Using the insert gradient, we were able to encode b values of greater than 1300 s/mm(2) with an echo time of just 83 ms. Images obtained using the insert gradient had higher signal to noise ratios than those obtained using the whole body gradient: at 500 s/mm(2) there was a 18% improvement in signal to noise ratio, at 1000 s/mm(2) there was a 39% improvement in signal to noise ratio, and at 1350 s/mm(2) there was a 56% improvement in signal to noise ratio. Using the insert gradient, we were capable of doing diffusion encoding at high b values by using relatively short echo times.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".