In Vivo MR-Tracking Based on Magnetic Signature Selective Excitation
Bibliographic record
Abstract
A novel magnetic resonance (MR)-tracking method specifically developed to locate the ferromagnetic core of an untethered microdevice, microrobot, or nanorobot for navigation or closed-loop control purpose is described. The tracking method relies on the application of radio-frequency (RF) excitation signals tuned to the equipotential magnetic curves generated by the magnetic signature of the object being tracked. Positive contrast projections are obtained with reference to the position of the magnetic source. A correlation function performed on only one k-space line for each of the three axes and corresponding to three projections, is necessary to obtain a 3-D location of the device. In this study, the effects of the sphere size and the RF frequency offset were investigated in order to find the best contrast noise ratio (CNR) for tracking. Resolution and precision were also investigated by proper measurement of the position of a ferromagnetic sphere by magnetic resonance imaging (MRI) acquisition and by comparing them with the real position. This method is also tested for a moving marker where the positions found by MRI projections were compared with the ones taken with a camera. In vitro and in vivo experiments show the operation of the technique in tortuous phantom and in animal models. Although the method was developed in the prospect of new interventional MR-guided endovascular operations based on miniature untethered devices, it could also be used as a passive tracking method using tools such as catheters or guide wires.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".