ALTERNATING SSFP PERMITS RAPID, BANDING-ARTIFACT-FREE BALANCED SSFP FMRI
Bibliographic record
Abstract
Blood oxygenation level dependent (BOLD) functional magnetic resonance imaging\n(fMRI) is the dominant tool used for mapping human brain function because it is\nnon-invasive, does not use ionizing radiation, and offers relatively high spatial and\ntemporal resolution compared to other neuroimaging techniques. Unfortunately, conventional\nfMRI techniques cannot map brain function in the inferior temporal cortex\n(ITC) and orbitofrontal cortex (OFC). These brain regions experience severe magnetic\nfield distortions due to magnetic susceptibility mismatch with the neighboring\nair-filled ear-canals (ITC) or sinus cavities (OFC), causing loss of the fMRI signal.\nFunctional imaging capability is important for gaining a better understanding of these\nbrain regions and the diseases that commonly affect them (Alzheimer’s disease and\nepilepsy (ITC), Parkinson’s disease and schizophrenia (OFC)).\nBalanced steady state free precession (balanced SSFP) is a relatively new fMRI\ntechnique that can measure function in all brain regions. Rather than diffuse signal\nloss, balanced SSFP images exhibit signal loss in spatially periodic, narrow bands.\nBanding artifacts cannot be eliminated in a single scan, but the phase of the banding\nartifacts can be controlled by the experimenter, permitting the combination of two\nantiphase balanced SSFP images to produce a single image free of banding artifacts.\nUnfortunately, image-corrupting transient signal oscillations limit the rate at which\nthe banding artifact phase can be modified, such that the banding-artifact-free image\nacquisition rate is prohibitively slow for most clinical and neuroscience applications.\nThis work describes the development of a modified balanced SSFP fMRI technique,\nalternating SSFP, which permits rapid, banding-artifact-free balanced SSFP fMRI.\nTheoretical modeling was used to find a rapid transition between antiphase balanced\nSSFP images with minimal transient signal oscillations. Monte Carlo simulations\nwere used to optimize alternating SSFP acquisition parameters for BOLD sensitivity,\nwith comparison to established balanced SSFP acquisitions. Rat fMRI was used to\nconfirm these predictions. Finally, the ability of alternating SSFP to provide rapid,\nbanding-artifact-free balanced SSFP fMRI in humans at 4 T was demonstrated.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".