Assessing Intervertebral Disk Tissue Mechanics Using Dual‐Actuator Multifrequency Magnetic Resonance Elastography: Case Reports
Bibliographic record
Abstract
Background: Degenerative disk disease (DDD) is a progressive condition that occurs when the intervertebral discs (IVDs), which act as shock absorbers between the vertebrae, degenerate or wear out. Due to this degeneration process, the mechanical properties of the IVD, providing flexibility between adjacent vertebrae, can change. Thus, assessing these mechanical properties may improve diagnosis and treatment guidance for DDD. In this article, we tested in vivo multifrequency magnetic resonance elastography (MMRE) of the human IVD in identifying progressively DDD in three asymptomatic male volunteers aged 32, 50, and 60 years. Methods: MMRE of the lumbar spine was acquired using a dual‐actuator setup and operated at four frequencies from 60 to 90 Hz. MMRE data were postprocessed using multifrequency wave‐number recovery (k‐MDEV) inversion algorithm. The resulting shear wave speed (SWS) values were used as a surrogate parameter of tissue stiffness and then compared to Pfirrmann grading (Pf) of disc degeneration (1–5) performed by an experienced MRI spine researcher. Results: Morphological Pf demonstrated an inverse relationship between increasing IVD stiffness and progressive IVD degeneration by a Spearman’s rank correlation coefficient of ρ = −0.792, p < 0.001. Conclusion: MMRE allows measurement of in vivo mechanical properties of IVDs and may provide additional information in disc degeneration beyond standard morphological changes. Prior to the clinical use of this technique, future studies should be conducted to evaluate the reproducibility and repeatability of spinal MMRE in the spine, and particularly its potential confounders.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| 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".