The effect of concomitant gradient fields on diffusion tensor imaging
Bibliographic record
Abstract
Concomitant gradient fields are transverse magnetic field components that are necessarily present to satisfy Maxwell's equations when magnetic field gradients are utilized in magnetic resonance imaging. They can have deleterious effects that are more prominent at lower static fields and/or higher gradient strengths. In diffusion tensor imaging schemes that employ large gradients that are not symmetric about a refocusing radiofrequency pulse (unlike Stejskal-Tanner, which is symmetric), concomitant fields may cause phase accrual that could corrupt the diffusion measurement. Theory predicting the error from this dephasing is described and experimentally validated for both Reese twice-refocused and split gradient single spin-echo diffusion gradient schemes. Bias in apparent diffusion coefficient values was experimentally found to worsen with distance from isocenter and with increasing duration of gradient asymmetry in both a phantom and in the brain. The amount of error from concomitant gradient fields depends on many variables, including the diffusion gradient pattern, pulse sequence timing, maximum effective gradient amplitude, static magnetic field strength, voxel size, slice distance from isocenter, and partial Fourier fraction. A prospective correction scheme that can reduce concomitant gradient errors is proposed and verified for diffusion imaging.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".