MR elastography of the human heart: Noninvasive assessment of myocardial elasticity changes by shear wave amplitude variations
Bibliographic record
Abstract
Many cardiovascular diseases and disorders are associated with hemodynamic dysfunction. The heart's ability to contract and pump blood through the vascular system primarily depends on the elasticity of the myocardium. This article introduces a magnetic resonance elastography (MRE) technique that allows noninvasive and time-resolved measurement of changes in myocardial elasticity over the cardiac cycle. To this end, low-frequency shear vibrations of 24.3 Hz were induced in the human heart via the anterior chest wall. An electrocardiograph (ECG)-triggered, steady-state MRE sequence was used to capture shear oscillations with a frame rate of eight images per vibration cycle. The time evolution of 2D-shear wave fields was observed in two imaging planes through the short axis of the heart in six healthy volunteers. Correlation analysis revealed that wave amplitudes were modulated in synchrony to the heartbeat with up to 2.45 +/- 0.18 higher amplitudes during diastole than during systole (interindividual mean +/- SD). The reduction of wave amplitudes started at 75 +/- 9 ms prior to changes in left ventricular diameter occurring at the beginning of systole. Analysis of this wave amplitude alteration using a linear elastic constitutive model revealed a maximum change in the myocardial wall stiffness of a factor of 37.7 +/- 10.6 during the cardiac cycle.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".