Subject movement during multislice interleaved MR acquisitions: Prevalence and potential effect on MRI‐derived brain pathology measurements and multicenter clinical trials of therapeutics for multiple sclerosis
Bibliographic record
Abstract
PURPOSE: To show the prevalence of inter-packet motion in clinical trial magnetic resonance imaging (MRI) data and the effect of inter-packet motion on MRI-derived brain pathology measurements. MATERIALS AND METHODS: We present a method to detect and quantify inter-packet motion, apply it to 2384 MRIs to determine the prevalence of inter-packet motion in clinical trial data, and show the effect of inter-packet motion on measuring multiple sclerosis (MS) lesion volumes. RESULTS: Experiments with simulated data showed that the detection procedure was accurate at measuring the amount of movement between packets and quantifying the amount of missing data. Application to clinical trial data demonstrated that a large number of MRIs had missing data due to inter-packet motion; 20% of the images had greater than 10% of the data missing and 10% of the images had greater than 15% of the data missing. These levels corresponded to thresholds where lesions were difficult to visually identify or disappeared completely. Lesion volume measurement errors ranged from 1.3 ± 0.5% to 9.9 ± 6.3%. CONCLUSION: Inter-packet motion can introduce substantial errors to MRI-derived brain pathology measurements. The prevalence of inter-packet motion is substantial in MS clinical trial data. Automated detection procedures should be implemented to increase the fidelity of MRI-derived measurements.
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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 | 0.046 | 0.164 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".