Automated quality control procedures for brain magnetic resonance images acquired in multi-centre clinical trials for multiple sclerosis
Bibliographic record
Abstract
Automated quality control procedures are critical for efficiently obtaining precise quantitative brain imaging-based metrics of in vivo brain pathology. This is especially important for multi-centre clinical trials of therapeutics for multiple sclerosis, in which MRI-derived brain pathology metrics may be used to quantify therapeutic efficacy. Currently, a large number of QC procedures have been developed for scanner maintenance with the idea that optimal scanner performance should produce MRIs with acceptable image quality and, thus, limit the effect of brain pathology measurement errors on quantitative analyses like therapeutic efficacy. Unfortunately these procedures may not be applicable to real subject MRI scans where non-ideal conditions like subject motion during an acquisition exist. The goal of this thesis is to provide an automated QC procedure for brain MRIs acquired in multi-centre clinical trials for multiple sclerosis where image quality is evaluated directly from the acquired MRI itself.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".