SU‐FF‐I‐59: Relative SNR Benefits of Dynamic Arterial Spin Labeling at 3T as Determined by Simulation and Comparison with Imaging at 1.5T
Bibliographic record
Abstract
Purpose: To demonstrate the benefits of brain perfusion imaging at high magnetic field MR scanner using Dynamic Flow‐sensitive alternating Inversion Recovery (FAIR) spin labeling technique as compared to standard FAIR at lower field. Method and Materials: FAIR signal behavior as a function of the inflow time (TI) at 1.5T and 3T were simulated for both dynamic and standard FAIR methods. Simulations were performed considering initial inversion of the magnetization, it's subsequent T1 recovery, in‐flow of blood into the slice and signal reduction due to repetitive RF excitations (Look‐Locker sampling) in dynamic FAIR. Experimental brain perfusion imaging of healthy volunteers (n=3) was performed on GE scanners. Results: The results showed overall signal behavior in dynamic FAIR is almost independent of readout rate. This means the temporal resolution of imaging can be increased to any desired amount as far as the scanner hardware permits. On the other hand multiple small readout flip angles will induce smaller perturbations than larger flip angles however for a given magnetic field dynamic FAIR with small flip angles lead to lower SNR as compared to standard FAIR. But given the same noise level a low flip angle dynamic FAIR would lead to higher SNR as compared to standard FAIR at 1.5T. For flip angle=20 the relative SNR of dynamic FAIR at 3T to standard FAIR at 1.5 for TI=500, 1200 would be 1.42 and 1.60 respectively. Conclusions: Significant increase in temporal resolution and SNR in FAIR arterial spin labeling technique can be achieved by performing multiple (dynamic) readouts after FAIR preparation. By implementing dynamic FAIR at higher field strength the quality of FAIR images in terms of SNR would be even much higher than standard FAIR imaging at lower field strength. This improvement can be further enhanced by repeating the dynamic FAIR more and averaging the results.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".