High‐order multiband encoding in the heart
Bibliographic record
Abstract
Spatial encoding with multiband selective excitation (e.g., Hadamard encoding) has been restricted to a small number of slices because the RF pulse becomes unacceptably long when more than about eight slices are encoded. In this work, techniques to shorten multiband RF pulses, and thus allow larger numbers of slices, are investigated. A method for applying the techniques while retaining the capability of adaptive slice thickness is outlined. A tradeoff between slice thickness and pulse duration is shown. Simulations and experiments with the shortened pulses confirmed that motion-induced excitation profile blurring and phase accrual were reduced. The connection between gradient hardware limitations, slice thickness, and flow sensitivity is shown. Excitation profiles for encoding 32 contiguous slices of 1-mm thickness were measured experimentally, and the artifact resulting from errors in timing of RF pulse relative to gradient was investigated. A multiband technique for imaging 32 contiguous 2-mm slices, with adaptive slice thickness, was developed and demonstrated for coronary artery imaging in healthy subjects. With the ability to image high numbers of contiguous slices, using relatively short (1-2 ms) RF pulses, multiband encoding has been advanced further toward practical application.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".