Subzone based multi-frequency magnetic resonance elastography using a Rayleigh damped material model
Bibliographic record
Abstract
MR Elastography (MRE) is a relatively novel imaging technique using conventional MRI methods to assess the mechanical properties of tissues. In time-harmonic MRE, a Rayleigh, or proportional, Damping (RD) model incorporates attenuation behavior proportionally related to both elastic and inertial forces, thus providing a more sophisticated description of the elastic energy dissipation occurring in the biological tissue. The overall damping ratio can be extracted from the combined effect of these two components, while an additional measure, called Rayleigh Composition, can be calculated by the ratio between the two components. Thus, RD elastography is capable of not only reconstructing the viscoelastic properties of the material, but also providing additional information about damping behavior and structure. A 3D subzone based reconstruction algorithm using a RD material model has been developed and optimized to reconstruct the viscoelastic properties, damping behavior and elastic energy attenuation mechanism of tissue-simulating damping phantoms across multiple frequencies. Results have shown that all three iterative reconstructed parameters are in relatively close agreement for both the tofu and gelatin materials in both phantom configurations across the frequency range. Preliminary results from in-vivo healthy brain are also presented and discussed.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".