Quantitative susceptibility mapping at 3 T: comparison of acquisition methodologies
Bibliographic record
Abstract
Quantitative susceptibility mapping (QSM) is fast becoming a routine clinical tool in the evaluation and assessment of neurological diseases. Unfortunately, there is currently no established standard scanning protocol, and it is uncertain whether different acquisition strategies alter the derived estimates of magnetic susceptibility. Here, we compare some key deep grey matter susceptibility values in healthy adults acquired from various QSM sequences using either unipolar or bipolar readout gradients, accelerated imaging or not, and gradient-warp correction or not. Four healthy adult volunteers were scanned three times each within 4 days at 3 T. The eight different QSM combinations were acquired in different randomised order for each session, and then co-registered to an anatomical atlas. The average and standard deviations of magnetic susceptibilities in the caudate, putamen, red nucleus, internal and external globus pallidus were used in a linear mixed effects model to determine the influence of the various acquisition parameters. Gradient-warp correction was the only statistically significant fixed effect (p < 0.01), but its impact was small (~5% change) compared with the overall fixed effects. The random effects coefficients (i.e. the various tissues) were statistically significant. Based on our limited multiple observations in healthy adult volunteers, the susceptibilities in deep grey matter are statistically equivalent when QSM source data are acquired with or without accelerated imaging using either unipolar or bipolar readout gradients. There is, however, a statistically meaningful, but small, difference if gradient-warp correction is used or not. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".