State of Practice on Transcranial MR-Guided Focused Ultrasound: A Report from the ASNR Standards and Guidelines Committee and ACR Commission on Neuroradiology Workgroup
Bibliographic record
Abstract
Transcranial focused ultrasound (FUS) is a versatile, MR-guided, incisionless intervention with diagnostic and therapeutic applications for neurologic and psychiatric diseases. It is currently FDA-approved as a thermoablative treatment of essential tremor and Parkinson disease. However, other applications of FUS including BBB opening for diagnostic and therapeutic applications, sonodynamic therapy, histotripsy, and low-intensity focused ultrasound neuromodulation are all in clinical trials. While FUS targeting for essential tremor and Parkinson disease has classically relied on an indirect, landmark-based approach, development of novel, advanced MR imaging techniques such as DTI tractography and fast gray matter acquisition T1 inversion recovery has the potential to improve individualized targeting and thus potentially enhance treatment response, decrease treatment times, and avoid adverse effects. As the technology advances and the number of clinical applications increases, the role of the neuroradiologist on a multidisciplinary team will be essential in pairing advanced structural and functional imaging to further this image-guided procedure via a precision medicine approach. This multi-institutional report, written by an experienced team of neuroradiologists, neurosurgeons, and neurologists, summarizes current practices, the use of advanced imaging techniques for transcranial MR-guided high-intensity FUS, recommendations for clinical implementation, and emerging clinical indications.
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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 | 0.082 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".