Asymmetric spin‐echo (ASE) spiral improves BOLD fMRI in inhomogeneous regions
Bibliographic record
Abstract
Functional MRI (fMRI) is of limited use in areas such as the orbitofrontal and inferior temporal lobes due to the presence of local susceptibility-induced field gradients (SFGs), which result in severe image artifacts. Several techniques have been developed to reduce these artifacts, the most common being the dual-echo spiral sequences (spiral-in/out and spiral-in/in). In this study, a new multiple spiral acquisition technique was developed, in which the later spiral acquisitions are acquired asymmetrically with the peak of a spin-echo causing increased R(2)-weighting but matched R(2)'-weighting. This sequence, called asymmetric spin-echo (ASE) spiral, has demonstrated significant improvements in minimizing the signal loss and increasing the image quality as well as optimal blood-oxygen-level-dependent (BOLD)-weighting. The ASE spiral is compared to conventional spiral-out using both signal-to-noise ratio (SNR) and whole brain fMRI activation volumes from a breath-hold task acquired at 4 Tesla. The ASE dual spiral has exhibited SNR increases of up to 300% in areas where strong SFGs are present. As a result, the ASE spiral is highly efficient for recovering lost activation in areas of SFGs, as demonstrated by a 16% increase in the total number of activated voxels over the whole brain. Post spin-echo ASE spiral images have decreasing SNR due to R(2) signal losses, however the increase in R(2)-weighting leads to a higher percentage of signal changes producing ASE spiral images with equivalent contrast-to-noise ratio (CNR) for each echo. The use of this sequence allows for recovery of BOLD activation in areas of SFG without sacrificing the CNR over the whole brain.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".