Comparison of spiral imaging and SENSE‐EPI at 1.5 and 3.0 T using a controlled cerebrovascular challenge
Bibliographic record
Abstract
PURPOSE: To quantitatively compare spiral imaging and sensitivity-encoded-echo-planar-imaging (SENSE-EPI) methods for blood oxygen level-dependent (BOLD) imaging using controlled changes in the end-tidal partial pressure of CO(2) (PetCO(2)) to provide a global BOLD response. Specifically, we examined susceptibility-field-gradient effects on the BOLD sensitivity throughout the brain. MATERIALS AND METHODS: We quantified cerebrovascular reactivity (CVR) using the BOLD response to cyclic changes in PetCO(2) in five healthy volunteers at 1.5 and 3.0 T using spiral imaging and SENSE-EPI. We compared the two techniques with respect to susceptibility-induced signal dropout and CVR t-statistic. RESULTS: Compared to spiral imaging, SENSE-EPI significantly reduced the volume of signal dropout by 32 +/- 18% at 3.0 T. In regions with large susceptibility gradients, SENSE-EPI demonstrated a trend for a greater t-statistic than spiral imaging, particularly at 3.0 T. However, no statistically significant between-technique differences existed. CONCLUSION: The results at 3.0 T suggest that, compared with spiral imaging, SENSE-EPI reduces signal loss associated with susceptibility field gradients in affected regions without affecting BOLD sensitivity. This study also demonstrates a unique application of controlled PetCO(2) changes to quantitatively compare BOLD techniques, which may be useful for the design of future fMRI studies.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".