Reliability of quantitative magnetic susceptibility imaging metrics for cerebral cortex and major subcortical structures
Bibliographic record
Abstract
Abstract Background and purpose Susceptibility estimates derived from quantitative susceptibility mapping (QSM) images for the cerebral cortex and major subcortical structures are variably reported in brain magnetic resonance imaging (MRI) studies, as average of all (), absolute (), or positive‐ () and negative‐only () susceptibility values using a region of interest (ROI) approach. This pilot study presents a reliability analysis of currently used ROI‐QSM metrics and an alternative ROI‐based approach to obtain voxel‐weighted ROI‐QSM metrics ( and ). Methods Ten healthy subjects underwent repeated (test‐retest) 3‐dimensional multi‐echo gradient‐echo (3DMEGE) 3 Tesla MRI measurements. Complex‐valued 3DMEGE images were acquired and reconstructed with slice thicknesses of 1 and 2 mm (3DMEGE1, 3DMEGE2) along with 3DT1‐weighted isometric (voxel 1 mm 3 ) images for independent registration and ROI segmentation. Agreement, consistency, and reproducibility of ROI‐QSM metrics were assessed through Bland‐Altman analysis, intraclass correlation coefficient, and interscan and intersubject coefficient of variation (CoV). Results All ROI‐QSM metrics exhibited good to excellent consistency and test‐retest agreement with no proportional bias. Interscan CoV was higher for in comparison to the other metrics where it was below 15%, in both 3DMEGE1 and 3DMEGE2 datasets. Intersubject CoV for and exceeded 50% in all ROIs. Conclusions Among the evaluated ROI‐QSM metrics, and estimates were less reliable, whereas separating positive and negative values (using ) improved the reproducibility within, and the comparability between, subjects, even when reducing the slice thickness. These preliminary findings may offer valuable insights toward standardizing ROI‐QSM metrics across different patient cohorts and imaging settings in future clinical MRI studies.
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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 | 0.000 | 0.001 |
| 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".