Enhancement of automated blood flow estimates (ENABLE) from arterial spin‐labeled MRI
Bibliographic record
Abstract
PURPOSE: To validate a multiparametric automated algorithm-ENhancement of Automated Blood fLow Estimates (ENABLE)-that identifies useful and poor arterial spin-labeled (ASL) difference images in multiple postlabeling delay (PLD) acquisitions and thereby improve clinical ASL. MATERIALS AND METHODS: ENABLE is a sort/check algorithm that uses a linear combination of ASL quality features. ENABLE uses simulations to determine quality weighting factors based on an unconstrained nonlinear optimization. We acquired a set of 6-PLD ASL images with 1.5T or 3.0T systems among 98 healthy elderly and adults with mild cognitive impairment or dementia. We contrasted signal-to-noise ratio (SNR) of cerebral blood flow (CBF) images obtained with ENABLE vs. conventional ASL analysis. In a subgroup, we validated our CBF estimates with single-photon emission computed tomography (SPECT) CBF images. RESULTS: ENABLE produced significantly increased SNR compared to a conventional ASL analysis (Wilcoxon signed-rank test, P < 0.0001). We also found the similarity between ASL and SPECT was greater when using ENABLE vs. conventional ASL analysis (n = 51, Wilcoxon signed-rank test, P < 0.0001) and this similarity was strongly related to ASL SNR (t = 24, P < 0.0001). CONCLUSION: These findings suggest that ENABLE improves CBF image quality from multiple PLD ASL in dementia cohorts at either 1.5T or 3.0T, achieved by multiparametric quality features that guided postprocessing of dementia ASL. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:647-655.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".