The effects of magnetic field distortion on the accuracy of passive device localization frames in MR imaging
Bibliographic record
Abstract
PURPOSE: The interventional magnetic resonance (MR) imaging environment presents many challenges for the accurate localization of interventional devices. In particular, geometric distortion of the static magnetic field may be both appreciable and unpredictable. This paper aims to quantify the sensitivity of localization error of various passive device localization frames to static magnetic field distortion in MR. METHODS: Three localization frames were considered based on having distinctly different methods of encoding position and orientation in MR images. For each frame, the effects of static field distortion were modeled, allowing rotational and translational errors to be computed as functions of the level of distortion, which was modeled using a first order approximation. Validation of the model was performed by imaging the localization frames in a 3T clinical MR scanner, and simulating the effects of static field distortion by varying the scanner's center frequency and gradient shim values. RESULTS: Plots of the rotational and translational components of error in localization frame position and orientation estimates are provided for ranges of uniform static field distortions of 1-100 μT and static field distortion gradients of 0.01-1 mT/m in all three directions. The theoretical estimates are in good agreement with the results obtained by imaging. CONCLUSIONS: The error in position and orientation estimation of passive localization frames in MR can be sensitive to static magnetic field distortions. The level of sensitivity, the type of error (i.e., rotational or translational), and the direction of error are dependent on the frame's design and the method used to image it. If 2D gradient echo imaging is employed, frames with position and orientation estimate sensitivity to slice-select error (such as the z-frame) should be avoided, since this source of error is not easily correctable. Accurate frame position and orientation estimates that are insensitive to static field distortion can be achieved using 2D gradient echo imaging if: (a) the method of determining position and orientation only uses in-plane measurements of marker positions, (b) the in-plane marker positions in images are not sensitive to slice-select error, and (c) methods of correcting in-plane error in the frequency-encoded direction are employed.
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 | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".