Standardized assessment of new electromagnetic field generators in an interventional radiology setting
Bibliographic record
Abstract
PURPOSE: Two of the main challenges associated with electromagnetic (EM) tracking in computer-assisted interventions (CAIs) are (1) the compensation of systematic distance errors arising from the influence of metal near the field generator (FG) or the tracked sensor and (2) the optimized setup of the FG to maximize tracking accuracy in the area of interest. Recently, two new FGs addressing these issues were proposed for the well-established Aurora(®) tracking system [Northern Digital, Inc. (NDI), Waterloo, Canada]: the Tabletop 50-70 FG, a planar transmitter with a built-in shield that compensates for metal distortions emanating from treatment tables, and the prototypical Compact FG 7-10, a mobile generator designed to be attached to mobile imaging devices. The purpose of this paper was to assess the accuracy and precision of these new FGs in an interventional radiology setting. METHODS: A standardized assessment protocol, which uses a precisely machined base plate to measure relative error in position and orientation, was applied to the two new FGs as well as to the well-established standard Aurora(®) Planar FG. The experiments were performed in two different settings: a reference laboratory environment and a computed tomography (CT) scanning room. In each setting, the protocol was applied to three different poses of the measurement plate within the tracking volume of the three FGs. RESULTS: The two new FGs provided higher precision and accuracy within their respective measurement volumes as well as higher robustness with respect to the CT scanner compared to the established FG. Considering all possible 5 cm distances on the grid, the error of the Planar FG was increased by a factor of 5.94 in the clinical environment (4.4 mm) in comparison to the error in the laboratory environment (0.8 mm). In contrast, the mean values for the two new FGs were all below 1 mm with an increase in the error by factors of only 2.94 (Reference: 0.3 mm; CT: 0.9 mm) and 1.04 (both: 0.5 mm) in the case of the Tabletop FG and the Compact FG, respectively. CONCLUSIONS: Due to their high accuracy and robustness, the Tabletop FG and the Compact FG could eliminate the need for compensation of EM field distortions in certain CT-guided interventions.
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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".