Correction of high-order phase variation effects in dynamic field monitoring
Bibliographic record
Abstract
Purpose: Field monitoring measures field perturbations, which can be accounted for during image reconstructions. In certain field monitoring environments, significant phase deviations can arise far from isocenter due to the finite extent of the gradient and/or main magnet. This can degrade the accuracy of field dynamics when field probes are placed near or outside the diameter spherical volume of the gradient coils and/or main magnet, leading to corrupted image quality. The objective of this work was to develop a correction algorithm that reduces errors from highly nonlinear phase variations at distant field probes in field dynamic fits. Methods: The algorithm is split into three components. Component one fits phase coefficients one spatial order at a time, while the second implements a weighted least squares solution based on probe distance. After initial fitting, component three calculates phase residuals and removes the phase for distant probes before re-fitting. Two healthy volunteers were scanned on a head-only 7T MRI using diffusion-weighted single-shot spiral and EPI sequences and field monitoring was performed. Images were reconstructed with and without phase coefficient correction and compared qualitatively. Results: The algorithm was able to correct corrupted field dynamics, resulting in image quality improvements. Significant artefact reduction was observed when correcting higher order fits, especially for diffusion weighted images. Stepwise fitting provided the most correction benefit, which was marginally improved when adding weighted least squares and phase residual corrections. Conclusion: The proposed algorithm can mitigate effects of phase errors in field monitoring, providing improved reliability of field dynamic characterization.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
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".