Prospective motion correction using inductively coupled wireless RF coils
Bibliographic record
Abstract
PURPOSE: A novel prospective motion correction technique for brain MRI is presented that uses miniature wireless radio-frequency coils, or "wireless markers," for position tracking. METHODS: Each marker is free of traditional cable connections to the scanner. Instead, its signal is wirelessly linked to the MR receiver via inductive coupling with the head coil. Real-time tracking of rigid head motion is performed using a pair of glasses integrated with three wireless markers. A tracking pulse-sequence, combined with knowledge of the markers' unique geometrical arrangement, is used to measure their positions. Tracking data from the glasses is then used to prospectively update the orientation and position of the image-volume so that it follows the motion of the head. RESULTS: Wireless-marker position measurements were comparable to measurements using traditional wired radio-frequency tracking coils, with the standard deviation of the difference < 0.01 mm over the range of positions measured inside the head coil. Wireless-marker safety was verified with B1 maps and temperature measurements. Prospective motion correction was demonstrated in a 2D spin-echo scan while the subject performed a series of deliberate head rotations. CONCLUSION: Prospective motion correction using wireless markers enables high quality images to be acquired even during bulk motions. Wireless markers are small, avoid radio-frequency safety risks from electrical cables, are not hampered by mechanical connections to the scanner, and require minimal setup times. These advantages may help to facilitate adoption in the clinic.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".