Influence of geometric and material properties on artifacts generated by interventional MRI devices: Relevance to PRF-shift thermometry
Bibliographic record
Abstract
PURPOSE: Magnetic resonance imaging (MRI) is capable of providing valuable real-time feedback during medical procedures, partly due to the excellent soft-tissue contrast available. Several technical hurdles still exist to seamless integration of medical devices with MRI due to incompatibility of most conventional devices with this imaging modality. In this study, the effect of local perturbations in the magnetic field caused by the magnetization of medical devices was examined using finite element analysis modeling. As an example, the influence of the geometric and material characteristics of a transurethral high-intensity ultrasound applicator on temperature measurements using proton resonance frequency (PRF)-shift thermometry was investigated. METHODS: The effect of local perturbations in the magnetic field, caused by the magnetization of medical device components, was examined using finite element analysis modeling. The thermometry artifact generated by a transurethral ultrasound applicator was simulated, and these results were validated against analytic models and scans of an applicator in a phantom. Several parameters were then varied to identify which most strongly impacted the level of simulated thermometry artifact, which varies as the applicator moves over the course of an ablative high-intensity ultrasound treatment. RESULTS: Key design parameters identified as having a strong influence on the magnitude of thermometry artifact included the susceptibility of materials and their volume. The location of components was also important, particularly when positioned to maximize symmetry of the device. Finally, the location of component edges and the inclination of the device relative to the magnetic field were also found to be important factors. CONCLUSIONS: Previous design strategies to minimize thermometry artifact were validated, and novel design strategies were identified that substantially reduce PRF-shift thermometry artifacts for a variety of device orientations. These new strategies are being incorporated into the next generation of applicators. The general strategy described in this study can be applied to the design of other interventional devices intended for use with MRI.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".