Automated removal of spurious intermediate cerebral blood flow volumes improves image quality among older patients: A clinical arterial spin labeling investigation
Bibliographic record
Abstract
PURPOSE: To evaluate the impact of rejecting intermediate cerebral blood flow (CBF) images that are adversely affected by head motion during an arterial spin labeling (ASL) acquisition. MATERIALS AND METHODS: Eighty participants were recruited, representing a wide age range (14-90 years) and heterogeneous cerebrovascular health conditions including bipolar disorder, chronic stroke, and moderate to severe white matter hyperintensities of presumed vascular origin. Pseudocontinuous ASL and T1 -weigthed anatomical images were acquired on a 3T scanner. ASL intermediate CBF images were included based on their contribution to the mean estimate, with the goal to maximize CBF detectability in gray matter (GM). Simulations were conducted to evaluate the performance of the proposed optimization procedure relative to other ASL postprocessing approaches. Clinical CBF images were also assessed visually by two experienced neuroradiologists. RESULTS: Optimized CBF images (CBFopt ) had significantly greater agreement with a synthetic ground truth CBF image and greater CBF detectability relative to the other ASL analysis methods (P < 0.05). Moreover, empirical CBFopt images showed a significantly improved signal-to-noise ratio relative to CBF images obtained from other postprocessing approaches (mean: 12.6%; range 1% to 56%; P < 0.001), and this improvement was age-dependent (P = 0.03). Differences between CBF images from different analysis procedures were not perceptible by visual inspection, while there was a moderate agreement between the ratings (κ = 0.44, P < 0.001). CONCLUSION: This study developed an automated head motion threshold-free procedure to improve the detection of CBF in GM. The improvement in CBF image quality was larger when considering older participants.
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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 | 0.003 |
| 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".