Accelerating cardiac cine 3D imaging using <i>k‐t</i> BLAST
Bibliographic record
Abstract
By exploiting spatiotemporal correlations in cardiac acquisitions using k-t BLAST, gated cine 3D acquisitions of the heart were accelerated by a net factor of 4.3, making single breathhold acquisitions possible. Sparse sampling of k-t space along a sheared grid pattern was implemented into a cine 3D SSFP sequence. The acquisition of low-resolution training data, which was required to resolve aliasing in the k-t BLAST method, was either interleaved into the sampling process or obtained in a separate prescan to allow for shorter breathhold durations in patients with heart disease. Volumetric datasets covering the heart with 20 slices at a spatial resolution of 2 x 2 x 5 mm3 were recorded with 20 cardiac phases in a total breathhold duration of 25-27 sec, or 18 sec if partial Fourier sampling was additionally employed. The feasibility of the method was demonstrated on healthy volunteers and on patients. The comparison of endocardial area derived from single slices of the 3D dataset with values extracted from separate single-slice acquisitions showed no significant differences. By shortening the acquisition substantially, k-t BLAST may greatly facilitate volumetric imaging of the heart for evaluation of regional wall motion and the assessment of ventricular volume and ejection fraction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".