Ultrafast three-dimensional microbubble imaging <i>in vivo</i> predicts tissue damage volume distributions during nonthermal brain ablation
Bibliographic record
Abstract
Transcranial magnetic resonance imaging (MRI)-guided focused ultrasound (FUS) thermal ablation is under clinical investigation for non-invasive neurosurgery, though its use is restricted to central brain targets due primarily to skull heating effects. The combination of FUS and contrast agent microbubbles greatly reduces the ultrasound exposure levels needed to ablate brain tissue and may help facilitate the use of transcranial FUS ablation throughout the brain. However, sources of variability exist during microbubble-mediated FUS procedures that necessitate the continued development of systems and methods for online treatment monitoring and control, to ensure that excessive and/or off-target bioeffects are not induced from the exposures. Methods: Megahertz-rate three-dimensional (3D) microbubble imaging in vivo was performed during nonthermal ablation in rabbit brain using a clinical-scale prototype transmit/receive hemispherical phased array system. Results: In-vivo volumetric acoustic imaging over microsecond timescales uncovered spatiotemporal microbubble dynamics hidden by conventional whole-burst temporal averaging. Sonication-aggregate ultrafast 3D source field intensity data were predictive of microbubble-mediated tissue damage volume distributions measured post-treatment using MRI and confirmed via histopathology. Temporal under-sampling of acoustic emissions, which is common practice in the field, was found to impede performance and highlighted the importance of capturing adequate data for treatment monitoring and control purposes.
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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) | 0.000 | 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".