Compressed sensing reconstruction improves sensitivity of variable density spiral fMRI
Bibliographic record
Abstract
PURPOSE: Functional MRI (fMRI) techniques that can provide excellent blood oxygen level dependent contrast, rapid whole brain imaging, and minimal spatial distortion are in demand. This study explored whether fMRI sensitivity can be improved through the use of compressed sensing (CS) reconstruction of variable density spiral fMRI. METHODS: Three different CS-reconstructed 1-shot variable density spirals were explored (corresponding to 28%, 35%, and 46% under-sampling), and compared with conventional 1-shot and 2-shot Archimedean spirals acquired using matched echo time and volume repetition time. fMRI maps were reconstructed with or without CS MRI and sensitivity was compared using identically matched voxels. RESULTS: The results demonstrated that an l1 -norm based CS reconstruction only led to an increase in functional contrast when applied to 28% under-sampled data. A whole brain t-contrast map revealed that 2-shot uniformly sampled spiral and 28% under-sampled spiral data reconstructed with CS yield equivalent sensitivity, even with matched echo time and volume repetition time CONCLUSION: VD spiral exhibits a useful operating range, in the region of 25-30% under-sampling, for which CS reconstruction can be used to increase the sensitivity of fMRI to brain activity. Using CS, VD acquisitions achieve the same sensitivity as 2-shot Archimedean acquisitions, but require only a single shot.
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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 | 0.005 |
| 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 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".