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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".