Nonholonomic catheter path reconstruction using electromagnetic tracking
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
Catheter path reconstruction is a necessary step in many clinical procedures, such as cardiovascular interventions and high-dose-rate brachytherapy. To overcome limitations of standard imaging modalities, electromagnetic tracking has been employed to reconstruct catheter paths. However, tracking errors pose a challenge in accurate path reconstructions. We address this challenge by means of a filtering technique incorporating the electromagnetic measurements with the nonholonomic motion constraints of the sensor inside a catheter. The nonholonomic motion model of the sensor within the catheter and the electromagnetic measurement data were integrated using an extended Kalman filter. The performance of our proposed approach was experimentally evaluated using the Ascension’s 3D Guidance trakStar electromagnetic tracker. Sensor measurements were recorded during insertions of an electromagnetic sensor (model 55) along ten predefined ground truth paths. Our method was implemented in MATLAB and applied to the measurement data. Our reconstruction results were compared to raw measurements as well as filtered measurements provided by the manufacturer. The mean of the root-mean-square (RMS) errors along the ten paths was 3.7 mm for the raw measurements, and 3.3 mm with manufacturer’s filters. Our approach effectively reduced the mean RMS error to 2.7 mm. Compared to other filtering methods, our approach successfully improved the path reconstruction accuracy by exploiting the sensor’s nonholonomic motion constraints in its formulation. Our approach seems promising for a variety of clinical procedures involving reconstruction of a catheter path.
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 | 0.001 | 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