Improved dynamic susceptibility contrast (DSC)‐MR perfusion estimates by motion correction
Bibliographic record
Abstract
PURPOSE: To investigate the effect of patient motion on quantitative cerebral blood flow (CBF) maps in ischemic stroke patients and to evaluate the efficacy of a motion-correction scheme. MATERIALS AND METHODS: Perfusion data from 25 ischemic stroke patients were selected for analysis. Two motion profiles were applied to a digital anthropomorphic brain phantom to estimate accuracy. CBF images were generated for motion-corrupted and motion-corrected data. To correct for motion, rigid-body registration was performed. The realignment parameters and mean CBF in regions of interest were recorded. RESULTS: All patient data with motion exhibited visibly reduced intervolume misalignment after motion correction. Improved flow delineation between different tissues and a more clearly defined ischemic lesion (IL) were achieved in the motion-corrected CBF. A significant difference occurred in the IL (P < 0.05) for patients with severe motion with an average difference between corrupted and corrected data of 4.8 mL/minute/100 g. The phantom data supported the patient results with better CBF accuracy after motion correction and high registration accuracy (<1 mm translational and <1 degrees rotational error). CONCLUSION: Motion degrades flow differentiation between adjacent tissues in CBF maps and can cause ischemic severity to be underestimated. A registration motion correction scheme improves dynamic susceptibility contrast (DSC)-MR perfusion estimates.
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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".