Learning a B <sub>0</sub> Shimming Model Using Deep Neural Networks
Bibliographic record
Abstract
Magnetic resonance (MR) is a non-invasive imaging technique used in biomedical research and clinical care. Central to this imaging modality is a strong, homogeneous, and static magnetic field for achieving enhanced data quality and shorter acquisition times. However, various factors, such as tissue susceptibility differences and hardware imperfections, can introduce field inhomogeneities. B0 shimming is essential to compensate for field variations. We propose the use of deep neural networks (DNNs) to estimate shim coils coefficients, as DNNs can model complex patterns of field perturbation, learn implicit representation of the shim fields, and are typically very fast to evaluate at inference time. This paper presents a neural network-based shim model that generates optimal shim coefficients using a simulation dataset. The model can predict near optimal coefficients for arbitrary shim volume masks, achieving a performance of R2=0.941±0.005 in ideal and non-ideal shim conditions.Clinical RelevanceThe ability to quickly predict optimized shim coefficients at the scanner can potentially reduce scan time and enhance image quality in challenging imaging scenarios.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".