Towards distinguishing intra-canal and paraspinal cavitation activity during focused ultrasound exposures in the spine
Bibliographic record
Abstract
Abstract Objective. Although less established than transcranial focused ultrasound (FUS), transvertebral FUS is being developed to treat spinal cord pathologies. Transvertebral sonication of the spinal cord for microbubble-mediated drug delivery generates cavitation at the target in the spinal canal, and outside the spinal canal due to reflection off the posterior surface of the spinal column. In these two regions, circulating microbubbles are excited by local foci to generate acoustic emissions that are used to monitor FUS treatments. When trying to localize acoustic emissions generated from cavitation in the spinal cord, prefocal cavitation emissions emanating from paraspinal regions can dwarf signals originating in the canal and compromising monitoring capabilities. This paper evaluates alternative reconstruction algorithms to delay-sum-and-integrate (DAS) in-silico and ex-vivo to more reliably map intra-spinal canal sources in the face of interference. Approach. A proof-of-concept 400/800 kHz (transmit/receive) spine-specific array prototype was used to generate intracanal cavitation through intact human vertebrae and passively monitor the corresponding acoustic emissions. Delay-multiply-sum-and-integrate (DMAS) beamforming was compared to DAS in two different implementations, full array (DMAS) and half-array multiplicative compounding (DMASMu), in the modeled cavitation scenarios where paraspinal cavitation is present. Main results. Both DMAS and DMASMu improved image quality by reducing peak sidelobes and increasing image signal-to-noise ratio. Aberration corrections further improved image quality metrics and, when applied selectively to voxels co-registered to the canal, assisted localization when prefocal sources were present in-silico. When localizing canal sources in the presence of paraspinal cavitation, a switch to DMAS/DMASMu offered a more consistent localization rate in-silico and ex-vivo, though ex-vivo phase and amplitude corrections failed to replicate in-silico findings. Significance. DMAS or DMASMu reconstruction with multiple dynamic ranges and sub-image integration timings can provide more reliable mapping of cavitation in the canal in the presence of interference from paraspinal cavitation.
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 | 0.000 | 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".