Design and development of a novel flexible ultra‐short echo time (FUSE) sequence
Bibliographic record
Abstract
Abstract Purpose To present the validation of a new Flexible Ultra‐Short Echo time (FUSE) pulse sequence using a short‐T2 phantom. Methods FUSE was developed to include a range of RF excitation pulses, trajectories, dimensionalities, and long‐T2 suppression techniques, enabling real‐time interchangeability of acquisition parameters. Additionally, we developed an improved 3D deblurring algorithm to correct for off‐resonance artifacts. Several experiments were conducted to validate the efficacy of FUSE, by comparing different approaches for off‐resonance artifact correction, variations in RF pulse and trajectory combinations, and long‐T2 suppression techniques. All scans were performed on a 3 T system using an in‐house short‐T2 phantom. The evaluation of results included qualitative comparisons and quantitative assessments of the SNR and contrast‐to‐noise ratio. Results Using the capabilities of FUSE, we demonstrated that we could combine a shorter readout duration with our improved deblurring algorithm to effectively reduce off‐resonance artifacts. Among the different RF and trajectory combinations, the spiral trajectory with the regular half‐inc pulse achieves the highest SNRs. The dual‐echo subtraction technique delivers better short‐T2 contrast and superior suppression of water and agar signals, whereas the off‐resonance saturation method successfully suppresses water and lipid signals simultaneously. Conclusion In this work, we have validated the use of our new FUSE sequence using a short T2 phantom, demonstrating that multiple UTE acquisitions can be achieved within a single sequence. This new sequence may be useful for acquiring improved UTE images and the development of UTE imaging protocols.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".