Sodium imaging optimization under specific absorption rate constraint
Bibliographic record
Abstract
The concept of sodium imaging RF pulse parameter optimization for signal-to-noise ratio (SNR) under specific absorption rate (SAR) constraints is introduced. This optimization concept is unique to sodium imaging, as sodium exhibits ultrarapid T(2) relaxation in vivo, and involves minimizing echo time (TE). For 3D radial k-space acquisition, minimizing TE (and T(2) loss) requires minimizing the RF pulse length. SNR optimization also involves exploiting rapid T(1) relaxation with shortened repetition time (TR) values. However, especially at higher fields, both RF pulse length and TR are constrained by SAR, which is also dependent on the flip angle. Quantum mechanical simulations were performed for SAR equivalent sets of RF pulse length, TR, and flip angle. It was determined that an SNR advantage is associated with a spoiled steady-state approach to sodium imaging with radial acquisition even though significantly longer RF pulses (and TE) are required to implement this approach under the SAR constraint at 4.7T. This advantage, compared to RF pulse sequences implementing ultrashort echo times, 90 degrees flip angles, and longer repetition times, was confirmed in healthy volunteers (measured SNR increase of approximately 38%) and used to produce excellent quality sodium images of the human brain.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".