Field distortion compensation for electromagnetic tracking of ultrasound probes with application in high-dose-rate prostate brachytherapy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Purpose: Electromagnetic (EM) tracking of ultrasound (US) probes has been introduced to expand US imaging capabilities and benefit challenging procedures. However, various instruments—including the US probe itself—may introduce dynamic distortions to the EM field, and compromise the EM measurements. Basic filtering methods, such as those provided by manufacturers, are usually inefficient as they do not allow for field distortion compensation. We propose to use a simultaneous localization and mapping (SLAM) algorithm to track the transrectal US (TRUS) probe while dynamically detect, map, and correct the EM field distortions. Methods: Combining the motion model of the tracked probe, the observations made by a few redundant EM sensors, and the field distortions map, the SLAM algorithm relied on an extended Kalman filter (EKF) to estimate the tracking measurements. The SLAM technique was experimentally validated in a brachytherapy suite. Tracking of a TRUS probe was performed by means of an Ascension trakSTAR tracking system and four EM sensors. In addition, an optical tracking system was employed to provide a ground truth to our data. The performance of the SLAM technique was analysed by varying pertinent parameters, such as the number of redundant measurements and the motion trajectory. Probe trajectories included longitudinal translation, rotation, and freehand motions (consisting of simultaneous longitudinal translation and rotation motions) in order to comprehensively simulate imaging scenarios. Finally, the accuracy of the SLAM estimations was compared with that of the standard filtering methods provided by the manufacturer, as well as that of a simpler sensor fusion technique. Results: SLAM efficiently reduced position tracking errors up to 46.4% during freehand motions of the TRUS probe. Moreover, higher SLAM estimation accuracies were observed as the number of redundant measurements increased. While both TRUS probe motions did not yield a clinically significant trend on position tracking accuracy, orientation measurements were considerably improved during translation of the TRUS probe. Conclusions: The SLAM technique was effective in increasing the tracking accuracy of the TRUS probe. Higher number of redundant sensors and favorable sensor configurations improved the SLAM estimations of EM measurements. In turn, SLAM can further encourage the introduction of EM tracking assistance in clinical procedures such as prostate brachytherapy.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it