Uniqueness of poroelastic and viscoelastic nonlinear inversion MR elastography at low frequencies
Bibliographic record
Abstract
Intrinsic actuation MR elastography (IA-MRE) exploits natural pulsations of the brain as a motion source to estimate mechanical property maps. The low frequency motion of IA-MRE introduces new considerations for inversion algorithms relative to traditional external actuation MRE. Specifically, inertial forces become very small, which leaves low frequency viscoelastic inversions with a non-unique scalar multiplier. Biphasic poroelastic inversions include additional fluid-solid interaction forces to balance the elastic forces, which avoids the non-uniqueness. Analyzing the convergence behavior from different starting values using 1 Hz simulated data, IA-MRE data from a gelatin phantom and in vivo brain IA-MRE data reveal that higher frequency (50 Hz) viscoelastic inversion reaches the correct, unique solution regardless of initial property estimate; whereas, low frequency viscoelastic inversion recovers relative values of shear modulus. In the presence of measurement noise, the non-unique scalar multiplier is determined by the softest material reaching the prescribed lower bound on shear modulus. Poroelastic inversion produces a unique solution at both 50 Hz and 1 Hz; however, hydraulic conductivity must be known or accurately estimated in order to recover quantitatively accurate shear modulus maps at low frequency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".