A buyer's guide to electromagnetic tracking systems for clinical applications
Bibliographic record
Abstract
When choosing an Electromagnetic Tracking System (EMTS) for image-guided procedures, it is desirable for the system to be usable for different procedures and environments. Several factors influence this choice. To date, the only factors that have been studied extensively, are the accuracy and the susceptibility of electromagnetic tracking systems to distortions caused by ferromagnetic materials. In this paper we provide a holistic overview of the factors that should be taken into account when choosing an EMTS. These factors include: the system's refresh rate, the number of sensors that need to be tracked, the size of the navigated region, system interaction with the environment, can the sensors be embedded into the tools and provide the desired transformation data, and tracking accuracy and robustness. We evaluate the Aurora EMTS (Northern Digital Inc., Waterloo, Ontario, Canada) and the 3D Guidance EMTS with the flat-panel and the short-range field generators (Ascension Technology Corp., Burlington, Vermont, USA) in three clinical environments. We show that these systems are applicable to specific procedures or in specific environments, but that, no single system is currently optimal for all environments and procedures we evaluated.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.262 | 0.277 |
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".