Fusion of electromagnetic tracking with speckle-tracked 3D freehand ultrasound using an unscented Kalman filter
Bibliographic record
Abstract
Freehand 3D ultrasound (US) using a 2D US probe has the advantage over conventional 3D probes of being able to collect arbitrary 3D volumes at a lower cost. Conventionally, optical and electromagnetic (EM) sensors are used to keep track of the US probe position. Optical tracking provides more accuracy but requires line-of-sight which can be a problem for many applications. Conversely, EM tracking does not have any line-of-sight restrictions, but it has lower accuracy and measurement jitter, and is susceptible to metallic distortions. Ultrasound imaging has the advantage that the speckle inherent in all images contains relative position information due to the decorrelation of speckle over distance. However, tracking the position of US images using speckle information alone suffers from drifts caused by tissue inconsistencies, and overall lack of accuracy. In our work, we examine the possibility for overcoming the limitations of both EM US tracking and freehand, speckle-based US image tracking, through the fusion of these techniques. Even though positions found through speckle-based tracking have very little jitter, the overall error is large, due to drifts in position estimation. By combining the EM and speckle-based tracking information using an Unscented Kalman Filter, we are able to reduce the drift errors as well as to eliminate high-frequency jitter noise from the EM tracker positions. Such fusion produces a smooth and accurate 3D reconstruction superior to those using the EM tracker alone. In addition, we look at the effect of metallic distortions on our fusion and demonstrate improvements over the EM tracker reconstruction.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".