Additional file 1 of MRI R2* and quantitative susceptibility mapping in brain tissue with extreme iron overload
Bibliographic record
Abstract
Additional file 1: Table S1. Median and inter quartile range (IQR) of magnetic susceptibility values (in ppm) assessed in whole brain and cerebrospinal fluid (CSF) as reference region, for each algorithm respectively. Table S2. Statistical results (F and p values) of investigating the effect of mapping algorithm on R2* for each brain region and group independently. Table S3. Statistical results (F and p value) for investigating the effect of mapping algorithm on magnetic susceptibility values in different brain regions, for multiple reference regions, and both patients and controls, independently. Fig. S1. Correlation between R2* and quantitative susceptibility (QSM) values for different QSM algorithms and R2* assessed using the algorithm for fast monoexponential fitting based on auto-regression on linear operations using only the first four echoes. The top row shows the correlation for QSM with whole brain reference, the middle row for QSM with cerebrospinal fluid (CSF) reference and the bottom row for QSM without reference. fansi Fast nonlinear susceptibility inversion, iLSQR Improved sparse linear equation and least-squares, medi Morphology enabled dipole inversion, merts Multiecho rapid two step, star Streaking artifact reduction, romeo Rapid opensource minimum spanning tree algorithm. Fig. S2. Correlation between R2* and quantitative susceptibility mapping (QSM) values for different QSM algorithms and R2* assessed using fitting with a linear model in logarithm space. The top row shows the correlation for QSM with whole brain reference, the middle row for QSM with cerebrospinal fluid (CSF) reference and the bottom row for QSM without reference. fansi Fast nonlinear susceptibility inversion, iLSQR Improved sparse linear equation and least-squares, medi Morphology enabled dipole inversion, merts Multiecho rapid two step, star Streaking artifact reduction, romeo Rapid opensource minimum spanning tree algorithm. Fig. S3. Correlation between R2* and quantitative susceptibility mapping (QSM) values for different QSM algorithms and R2* assessed using monoexponential R2* fitting with a nonlinear algorithm which considers only echoes above the noise level. The top row shows the correlation for QSM with whole brain reference, the middle row for QSM with cerebrospinal fluid (CSF) reference and the bottom row for QSM without reference. fansi Fast nonlinear susceptibility inversion, iLSQR Improved sparse linear equation and least-squares, medi Morphology enabled dipole inversion, merts Multiecho rapid two step, star Streaking artifact reduction, romeo Rapid opensource minimum spanning tree algorithm. Fig. S4. Correlation between R2* and quantitative susceptibility mapping (QSM) values for different QSM algorithms and R2* assessed using integrated mapping tool of the MRI system. The top row shows the correlation for QSM with whole brain reference, the middle row for QSM with cerebrospinal fluid (CSF) reference and the bottom row for QSM without reference. fansi Fast nonlinear susceptibility inversion, iLSQR Improved sparse linear equation and least-squares, medi Morphology enabled dipole inversion, merts Multiecho rapid two step, star Streaking artifact reduction, romeo Rapid opensource minimum spanning tree algorithm. Fig. S5. Correlation between R2* and quantitative susceptibility mapping (QSM) values for different QSM algorithms and R2* assessed using numerical algorithm for real-time R2* mapping. The top row shows the correlation for QSM with whole brain reference, the middle row for QSM with cerebrospinal fluid (CSF) reference and the bottom row for QSM without reference. fansi Fast nonlinear susceptibility inversion, iLSQR Improved sparse linear equation and least-squares, medi Morphology enabled dipole inversion, merts Multiecho rapid two step, star Streaking artifact reduction, romeo Rapid opensource minimum spanning tree algorithm.
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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 | 0.002 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.829 | 0.142 |
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".