Spin‐history artifact during functional MRI: Potential for adaptive correction
Bibliographic record
Abstract
PURPOSE: Functional magnetic resonance imaging (fMRI) is limited by sensitivity to millimetre-scale head motion. Adaptive correction is a strategy to adjust the imaging plane in response to measured head motion, thereby suppressing motion artifacts. This strategy should correct for motion in all six degrees of freedom and also holds promise for through-plane motion that creates "spin-history" artifact that cannot easily be removed by postprocessing methods. Improved quantitative understanding of the MRI signal behavior associated with spin-history artifact would be useful for implementing adaptive correction robustly. METHODS: A numerical simulation was developed to predict MRI artifact signal amplitude in a single-slice for simple motions, implemented with and without adaptive correction, and compared with experiment by imaging a phantom at 3.0 T. Functional MRI was also performed of a human volunteer to illustrate adaptive correction in the presence of spin-history artifact. RESULTS: Good agreement was achieved between simulation and experimental results. Although time-averaged artifact signal amplitude was observed to correlate linearly with motion speed, artifact time-courses were nonlinearly related to motion waveforms. In addition, experimental results demonstrated effective adaptive correction of spin-history artifact when the phantom underwent complex motions. Adaptive correction during human fMRI suppressed spin-history artifacts and spurious activations associated with task-correlated motion. CONCLUSIONS: Overall, this work suggests that adaptive correction, especially when implemented with minimal lag between motion measurement and scan plane update, may help to expand the populations for which fMRI can be performed robustly.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 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 teacher head, 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".