Sci‐Fri PM Imaging‐09: Robust MR‐DSC Perfusion using a Patient Motion Correction Scheme
Bibliographic record
Abstract
Stroke is a leading cause of death in North America and results when blood supply to the brain is reduced, which compromises nutrient exchange to and waste product removal from brain cells. Ischemic stroke is the most common type of stroke and results when blood flow in an artery supplying blood to brain tissue is reduced due to a thrombus or embolism. Magnetic resonance (MR) dynamic susceptibility contrast (DSC) perfusion‐weighted imaging (PWI) shows great utility in assessing blood flow characteristics in brain tissue, and thus for potentially assessing tissue status in ischemic stroke. Quantitative cerebral blood flow (qCBF) maps can be generated from the 4D perfusion data and may be useful for defining perfusion thresholds to better determine tissue status. The accuracy of quantitative perfusion maps depends on the tracking of the same tissue location within the brain over the course of the image series; the consistency of this location tracking is compromised by patient motion during the scan. Patient motion blends the temporal signals of adjacent tissue locations in the brain, yielding erroneous perfusion measurements. Using a digital brain phantom and patient data, we investigated the effect of motion on qCBF maps and evaluated the efficacy of a realignment scheme for motion correction. Results show that qCBF maps are sensitive to motion and that that rigid‐body registration (realignment) is a robust scheme for correcting patient motion to improve the accuracy of qCBF maps.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".