On the influence of training data quality in <i>k‐t</i> BLAST reconstruction
Bibliographic record
Abstract
This work investigated how the quality of prior information (i.e., data acquired during the training stage) influences k-t BLAST reconstruction. The impact of several factors, such as the amount of training data, the presence of spatial misregistration in the training data, and the effects of filtering, was investigated with simulations and in vivo data. It is shown that k-t BLAST outperforms sliding window reconstruction, even with very limited training data. By increasing the amount of training data, reconstruction error continues to decrease, albeit by a diminishing amount. However, an increased amount of training data also increases susceptibility to misregistration of the training data. Filtering of the training data with the goal of reducing truncation artifacts had only minor impact on reconstruction errors. Considering the balance among obtaining the most benefit from the training data, minimizing susceptibility to misregistration, and keeping data acquisition to a minimum, it is concluded that in cardiac imaging the training datasets should be limited to 10-20 profiles in k-space for a typical field of view. The training data may be acquired in a separate breathhold without much penalty, if care is taken to minimize misregistration, such as with a navigator.
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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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".