Freehand ultrasound calibration using the unscented Kalman filter
Bibliographic record
Abstract
Three-dimensional freehand ultrasound has found several clinical applications, such as image-guided surgery and radiotherapy, since the last decade. A key step of all the freehand ultrasound imaging systems is calibration. Calibration is the procedure to estimate a two to three-dimensional transformation matrix which precisely maps two-dimensional ultrasound images to the physical coordinate. This paper presents a novel freehand ultrasound calibration algorithm which is based on a sequential least squares method, known as the Unscented Kalman Filter (UKF) algorithm. This method has significant advantages over the prior approaches, where the block least squares techniques have been employed to perform the ultrasound probe calibration. One of the advantages is that it computes the calibration parameters as well as their variances sequentially by processing the sample points, collected from ultrasound images of a designed phantom, one by one. Variance evaluation can be used to generate a confidence measure for the estimated calibration matrix. It also enables us to stop the calibration procedure once the desired confidence measure is met or informs us to collect more sample points to improve the calibration accuracy. The proposed calibration method is evaluated by using a custom designed N-wire phantom. The simulation results confirm that the proposed calibration algorithm converges to the same solution as the block least squares algorithms, while having the above mentioned practical advantages.
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 | 0.001 | 0.004 |
| 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) | 0.002 | 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".