Transient shear wave elastometry using a portable magnetic resonance sensor
Bibliographic record
Abstract
This repository contains all raw data files and processing scripts associated with the paper published in Magnetic Resonance in Medicine entitled “Transient Shear Wave Elastometry Using a Portable Magnetic Resonance Sensor.” 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.079 | 0.037 |
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".