Abstracts from the International Society for Therapeutic Ultrasound Conference 2017
Bibliographic record
Abstract
OBJECTIVES Transcranial HIFU is now used in clinics for treating essential tremor and proposed for many other brain disorders. This promising treatment modality still faces several limitations. HIFU-induced thermal ablation in the brain requires high energy resulting eventually in undesired cavitation and potential side effects. Original strategies should be tested to increase treatment safety and efficacy. The goals of the present work were: 1-to evaluate the potential increase of the cavitation threshold using pseudo-random gated sonications and 2-to assess the heating and steering capabilities with such sonications. The performance of pseudo-random sonications was compared with conventional continuous exposures. METHODS The experiments were performed with the transcranial MR-compatible ExAblate Neuro system (InSightec). It is a 1024-element, 30 cm diameter, 15 cm focal length, transducer operating at a frequency of 660 kHz. Four methods of sonication were compared: continuous wave (CW), gated emissions with pseudo-random 2ms (p-Rand2ms) or 33 us (p-Rand33us) -period codes and 30 kHz square (Squ30kHz). The duty cycle (DC) was set to 50% for the gated sonications. The cavitation threshold was first evaluated in water. For each condition, electrical driving power was increased step by step from 20 to 500W and the acoustical noise was recorded with the integrated passive cavitation detectors. The spectral energy was averaged over the 250-500 kHz bandwidth and the sonication time (10s). Heating trials were then performed in a hydrogel tissue mimicking material (TMM, ATS Laboratories). The electrical power was set to 15, 30 or 60 W, the exposureduration to 9 or 18 s. For a fair comparison with CW, 50% DC sonications had either the electrical power or the exposure duration doubled. The temperature was measured by MRthermometry when focusing at the geometrical focus and when steering the beam off-focus by 5mm-steps.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.283 | 0.176 |
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".