Endoscopic Coregistered Ultrasound Imaging and Precision Histotripsy: Initial <i>In Vivo</i> Evaluation
Bibliographic record
Abstract
Objective . Initial performance evaluation of a system for simultaneous high-resolution ultrasound imaging and focused mechanical submillimeter histotripsy ablation in rat brains. Impact Statement . This study used a novel combination of high-resolution imaging and histotripsy in an endoscopic form. This would provide neurosurgeons with unprecedented accuracy in targeting and executing nonthermal ablations in minimally invasive surgeries. Introduction . Histotripsy is a safe and effective nonthermal focused ablation technique. However, neurosurgical applications, such as brain tumor ablation, are difficult due to the presence of the skull. Current devices are too large to use in the minimally invasive approaches surgeons prefer. We have developed a combined imaging and histotripsy endoscope to provide neurosurgeons with a new tool for this application. Methods . The histotripsy component had a 10 mm diameter, operating at 6.3 MHz. Affixed within a cutout hole in its center was a 30 MHz ultrasound imaging array. This coregistered pair was used to ablate brain tissue of anesthetized rats while imaging. Histological sections were examined, and qualitative descriptions of ablations and basic shape descriptive statistics were generated. Results . Complete ablations with submillimeter area were produced in seconds, including with a moving device. Ablation progress could be monitored in real time using power Doppler imaging, and B-mode was effective for monitoring post-ablation bleeding. Collateral damage was minimal, with a 100 μ m maximum distance of cellular damage from the ablation margin. Conclusion . The results demonstrate a promising hardware suite to enable precision ablations in endoscopic procedures or fundamental preclinical research in histotripsy, neuroscience, and cancer.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".