The impulse noise of TMS inside a 3 T and 9.4 T MRI
Bibliographic record
Abstract
Abstract The operation of a transcranial magnetic stimulation (TMS) coil produces high-intensity impulse sounds. In TMS, a magnetic field is generated by a short-duration pulse in the range of thousands of amperes in the coil. When placed in a strong magnetic field, such as inside an magnetic resonance imaging (MRI) bore, the interaction of the magnetic field and the current in the TMS coil can cause strong forces on the coil casing. The strengths of these forces depend on the coil orientation in the main magnetic field (B0). Part of the energy in this process is dissipated in the form of acoustic noise. To conduct concurrent TMS and functional MRI (fMRI) safely, the sound pressure levels (SPLs) generated by the TMS coil must be quantitatively characterized. Measuring the SPLs of fast and loud impulse sounds accurately in the presence of static and gradient magnetic fields is challenging. In this study, we present a method for such measurements and report the SPLs of two commercial MRI-compatible TMS systems inside a 3T MRI scanner and of a prototype multi-channel TMS (mTMS) system inside a 9.4T small-animal MRI scanner. The mTMS coil allows for changing the direction of the electric field (E-field) without physically moving the TMS coil. We measured the acoustic noise generated by the TMS coils with different E-field orientations relative to the B0 field at different stimulation intensities and locations. The measurements were compared to the sound level measured outside the MRI room. SPLs and spectrum of the click sounds changed depending on coil and induced E-field orientation compared to the B0 field. SPLs exceeding the safety limit of 140 dB(C) was measured with all the devices. Our study provide is an important step towards the safety operation of concurrent TMS-fMRI respecting the auditory limits of small animals and humans. Keywords: TMS, mTMS, fMRI, acoustic noise
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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 | 0.000 |
| 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 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".