Fat-suppressed alternating-SSFP for whole-brain fMRI using breath-hold and visual stimulus paradigms
Bibliographic record
Abstract
PURPOSE: To achieve artifact-suppressed whole-brain pass-band-balanced steady-state free precession functional MRI from a single functional magnetic resonance imaging (fMRI) scan. METHODS: A complete and practical data acquisition sequence for alt-SSFP fMRI was developed. First, multishot flyback-echo-planar imaging (EPI) and echo-time shifting were used to achieve data acquisition that was robust against eddy currents, gradient delays, and ghosting artifacts. Second, a steady-state catalyzation scheme was implemented to reduce oscillations in the transient signal when catalyzing in and out of alternate steady states. Next, a short spatial-spectral radiofrequency (RF) pulse was designed to achieve excellent fat-suppression while maintaining a repetition time <15 ms to sensitize functional activation toward smaller vessels and capillaries. Lastly, parallel imaging was used to achieve whole-brain coverage and sufficiently high temporal resolution. RESULTS: Breath-hold experiments showed excellent fat-suppression and alt-SSFP's capability to recover functional sensitivity from signal dropout regions of conventional gradient-echo and banding artifacts from conventional pass-band-balanced steady-state free precession. Applying fat-suppression resulted in improved activation maps and increased temporal SNR. Visual stimulus functional studies verify the proposed method's excellent functional sensitivity to neuronal activation. CONCLUSION: Artifact-suppressed images are demonstrated, showing a practical pass-band-balanced steady-state free precession fMRI method that permits whole-brain imaging with excellent blood oxygen level-dependent sensitivity and fat suppression.
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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) | 0.001 | 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".