Calculating potential error in sodium <scp>MRI</scp> with respect to the analysis of small objects
Bibliographic record
Abstract
Purpose To facilitate correct interpretation of sodium MRI measurements, calculation of error with respect to rapid signal decay is introduced and combined with that of spatially correlated noise to assess volume‐of‐interest (VOI) 23Na signal measurement inaccuracies, particularly for small objects. Methods Noise and signal decay–related error calculations were verified using twisted projection imaging and a specially designed phantom with different sized spheres of constant elevated sodium concentration. As a demonstration, lesion signal measurement variation (5 multiple sclerosis participants) was compared with that predicted from calculation. Results Both theory and phantom experiment showed that VOI signal measurement in a large 10‐mL, 314‐voxel sphere was 20% less than expected on account of point‐spread‐function smearing when the VOI was drawn to include the full sphere. Volume‐of‐interest contraction reduced this error but increased noise‐related error. Errors were even greater for smaller spheres (40–60% less than expected for a 0.35‐mL, 11‐voxel sphere). Image‐intensity VOI measurements varied and increased with multiple sclerosis lesion size in a manner similar to that predicted from theory. Correlation suggests large underestimation of 23Na signal in small lesions. Conclusions Acquisition‐specific measurement error calculation aids 23Na MRI data analysis and highlights the limitations of current low‐resolution methodologies. Magn Reson Med 79:2968–2977, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".