Transient shear wave elastometry using a portable magnetic resonance sensor
Bibliographic record
Abstract
PURPOSE: Magnetic resonance elastography (MRE) provides detailed maps of tissue stiffness, helping to diagnose various health conditions, but requires the use of expensive clinical MRI scanners. Our approach utilizes compact, cost-effective portable MR sensors that offer bulk characterization of material properties in a region of interest close to the surface (within 1-2 cm). This accessible instrument could enable routine monitoring and prevention of diseases not readily evaluated with conventional tools. METHODS: The method was tested on tissue-mimicking phantoms with varying stiffness. The gels were excited with acoustic pulses (one cycle of a sinusoidal waveform) at a fixed distance from the MR sensor. A series of delays between acoustic excitation and MR signal detection allowed time for the pulse to travel to the sensitive region. RESULTS: The "arrival time" of the shear wave, determined by the time-dependent MR signal response, was used to calculate the shear wave speed. MR measurements of shear wave speed were compared with optical sensor measurements and manufacturer-tabulated values, aligning with expected relative differences between samples. CONCLUSION: A portable MR-based transient elastometry technique for measuring tissue elasticity was developed and demonstrated on tissue-mimicking phantoms. Future improvements include using a new portable magnet to investigate depth-dependent changes in elasticity in stratified samples and integrating MR relaxation and diffusion measurements for comprehensive tissue analysis. This approach can complement conventional MRE in applications where a portable, affordable, and localized assessment of tissue stiffness is required.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".