Design, Evaluation, and Implementation of a Novel Magnetic Resonance Imaging-Compatible Physiologic Loading Simulator for Ex-Vivo Joints
Bibliographic record
Abstract
Quantitative magnetic resonance imaging (qMRI), in combination with mechanical testing, offers potential to investigate how loading (e.g., from daily physical exercise) is related to joint and tissue function. However, current testing devices compatible with magnetic resonance imaging (MRI) are often limited to uniaxial compression, often applying low loads, or loading individual tissues (instead of multiple), while more complex simulators do not facilitate MRI. Hence, in this work, we designed, built and tested (N = 1) an MRI-compatible multi-axial load-control system, which enables scanning cadaveric joints (healthy or pathologic) loaded to physiologically relevant levels. Testing involved estimating and validating physiologic loading conditions before implementing them experimentally on cadaver knees to simulate and image gait loading (stance and swing). The resulting design consisted of a portable loading device featuring pneumatic actuators to reach a combined loading scenario, including axial compression (≤2.5 kN), shear (≤1 kN), bending (≤30 N·m) and muscle tension. Initial laboratory testing was carried out; specifically, the device was instrumented with force and pressure sensors to evaluate loading and contact response repeatability in one cadaver knee specimen. This loading system was able to simulate healthy or pathologic gait with reasonable repeatability (e.g., 1.23-2.91% coefficient of variation for axial compression), comparable to current state-of-the-art simulators, leading to generally consistent contact responses. Contact measurements demonstrated a tibiofemoral to patellofemoral load transfer with knee flexion and large contact pressures concentrated over small sites between the femoral cartilage and menisci, agreeing with experimental studies and numerical simulations in the literature.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".