An Eight-Channel MRI Head Coil Array for Enhanced MRI-Guided Transcranial Focused Ultrasound Imaging Performance
Bibliographic record
Abstract
Magnetic resonance guided focused ultrasound (MRgFUS) thermal ablation is a new method of non-invasive neurosurgery that can benefit from improved MR imaging performance. The ultrasound transducer shields the radio-frequency field of the body coil, reducing transmit and receive sensitivity as well as reducing transmit homogeneity. To improve the quality of structural imaging and the MR thermometry used to guide lesioning, we propose a dedicated 8-element receive array designed to work within the water bath of the ultrasound transducer helmet. The array consists of dipoles, consisting of flexible twin lead conductors, that pierce the water bath interface membrane, which forms a watertight seal around the patient’s head. In transmit mode, with the array detuned, no change in the transmit efficiency or homogeneity was observed, and simulation demonstrated no significant change in the 10g averaged specific absorption rate (SAR). In receive mode, the mean receive sensitivity within the brain was 5.7 times higher than that of the body coil. FUS heating is unaffected by the coil array, as demonstrated by an unaltered spread of the temperature at the FUS focal point. Finally, the precision of thermometry was improved by a factor of 2.25 in results from phantom imaging and a similar amount in in-vivo imaging.
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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) | 0.000 | 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) | 0.002 | 0.001 |
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".