A robotic MR-guided high-intensity focused ultrasound platform for intraventricular hemorrhage: assessment of clot lysis efficacy in a brain phantom
Bibliographic record
Abstract
OBJECTIVE: Intraventricular hemorrhage (IVH) is a neurovascular complication due to premature birth that results in blood clots forming within the ventricles. Magnetic resonance-guided high-intensity focused ultrasound (MRgHIFU) has been investigated as a noninvasive treatment to lyse clots. The authors designed and constructed a robotic MRgHIFU platform to treat the neonatal brain that facilitates ergonomic patient positioning. The clot lysis efficacy of the platform is quantified using a brain phantom and clinical MRI system. METHODS: A thermosensitive brain-mimicking phantom with ventricular cavities was developed to test the clot lysis efficacy of the robotic MRgHIFU platform. Whole porcine blood was clotted within the phantom's cavities. Using the MRgHIFU platform and a boiling histotripsy treatment procedure (500 W, 10-msec pulse duration, 1.0% duty cycle, and 40-second duration), the clots were lysed inside the phantom. The contents of the cavities were vacuum filtered, and the remaining mass of the solid clot particles was used to quantify the percentage of clot lysis. The interior of the phantom's cavities was inspected for any collateral damage during treatment. RESULTS: A total of 9 phantoms were sonicated, yielding an average (± SD) clot lysis of 97.0% ± 2.57%. Treatment resulted in substantial clot lysis within the brain-mimicking phantoms that were apparent on postsonication T2-weighted MR images. No apparent collateral damage was observed within the phantom after treatment. The results from the study showed the MRgHIFU platform was successful at lysing more than 90% of a blood clot at a statistically significant level. CONCLUSIONS: The robotic MRgHIFU platform was shown to lyse a large percentage of a blood clot with no observable collateral damage. These results demonstrate the platform's ability to induce clot lysis when targeting through simulated brain matter and show promise toward the final application in neonatal patients.
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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".