Passive mapping of acoustic sources within the human skull cavity with a hemispherical sparse array using computed tomography-based aberration corrections
Bibliographic record
Abstract
Traditionally, the use of ultrasound (US) in the brain has been limited by the skull bone, which presents unique challenges for both transcranial therapy and imaging due to its attenuating and aberrating effects, which become more prevalent at higher US frequencies. On transmit, these skull-induced aberrations can be overcome through the use of large-aperture phased array transducers with appropriate driving frequencies, combined with computed tomography (CT)-based bone morphology and numerical models to derive element driving signals which minimize the distortions. Recently, we have demonstrated in silico that an analogous approach can be performed during beamforming on receive, to allow for passive acoustic imaging over a large region within the skull cavity [Jones et al., Phys. Med. Biol. 58, 4981–5005 (2013)]. We will present preliminary results obtained from applying this technique experimentally with a hemispherical (30 cm diam.) sparse receiver array (128 piezo-ceramic elements, 2.5 mm diam., and 612 kHz center frequency) to image acoustic sources through an ex vivo human skullcap. Images produced using non-invasive CT-based skull corrections will be compared with those obtained through an invasive hydrophone-based correction approach, and with images formed without skull-specific corrections. This technique has promising applications in both cavitation-mediated transcranial focused ultrasound therapies, by providing a method for treatment monitoring and control, as well as in ultrasound angiographic imaging of the brain.
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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 | 0.001 |
| 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) | 0.001 | 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 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".