Development of a Computational Method for Assessing Static Field Induced Torque on Medical Implants
Bibliographic record
Abstract
The objective of this thesis is the development of a computational method for finding the torque induced on an object when placed in the static magnetic field of an MR scanner. As a preliminary step, the classic EM problems of a sphere and infinitely long cylinder of linear material was modeled in commercially available simulation software. Upon verification of the parameters implemented, the second step is the simulation of simple objects with realistic material properties, stainless-steel cylinders. Physical cylinders were machined to match those in the simulations and underwent the ASTM standard method for measuring induced torque. An adjacent study that was also performed was finding the measurement uncertainty in a prototype ASTM abiding apparatus, separate from the one used for experimental verification.\nIt was found that the sphere and infinitely long cylinder models differed less than 5% from the analytical solutions. Implementing the correct material properties, magnetic susceptibility in particular, to the grades of stainless-steel used in this study was particularly challenging. However, when the experimentally measured results were used to find the necessary susceptibility values for the computational methods, it was found to be in agreement with literature values. The following computationally-found torque values agreed within 10% difference from the experimentally measured values. The induced torque increased linearly with the length of the cylinders and the square of magnetic susceptibility.\nIn the uncertainty analysis of the torque measurement apparatus described in ASTM F2213-17, it was found that the apparatus described in the ‘Pulley Method’ offered a lower instrument uncertainty than the apparatus described in the ‘Torsional Spring Method’. This study emphasized on the contribution of static friction and is important to consider should the apparatus be used in the future to verify computational results.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".