A framework for optimization‐based design of motion encoding in magnetic resonance elastography
Bibliographic record
Abstract
PURPOSE: In conventional three-dimensional magnetic resonance elastography, motion encoding gradients (MEGs) synchronized to a mechanical excitation are applied separately in each direction to encode tissue displacement generated by the corresponding waves. This requires long acquisition times that introduce errors due to patient motion and may hinder clinical deployment of magnetic resonance elastography. In this article, a framework for MEGs sequence design is proposed to reduce scanning time and increase signal-to-noise ratio. THEORY AND METHODS: The approach is based on applying MEGs in all three directions simultaneously with varying parameters, and formulation of the problem as a linear estimation of the wave properties. Multidirectional MEGs sequences are derived by setting the problem in an experimental design framework. Such designs are implemented and evaluated on simulation and phantom data. RESULTS: Estimation error of the displacement using the proposed MEGs designs is reduced up to a factor of two in comparison with a unidirectional design with a same number of acquisitions. Alternatively, for the same error, scanning time is reduced up to a factor of three using the multidirectional designs. CONCLUSION: The proposed framework generalizes acquisition of magnetic resonance elastography, and allows quantification of design performance, and optimization-based derivation of designs.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".