Bedside three-dimensional acoustic angiography and perfusion imaging for early detection of delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage: A Feasibility study
Bibliographic record
Abstract
ABSTRACT Aneurysmal subarachnoid hemorrhage (aSAH) is a life-threatening condition with high rates of secondary complications such as delayed cerebral ischemia (DCI). Early detection of perfusion deficits is critical but remains challenging with conventional imaging modalities that are intermittent, non-portable, and provide limited temporal resolution. Here, we evaluate a novel investigational platform for volumetric acoustic angiography and bedside cerebral perfusion imaging using three-dimensional contrast-enhanced ultrasound (3D CEUS). In a prospective feasibility study, eleven aSAH patients underwent bilateral 3D CEUS at admission, day 5, and day 10 in the neurocritical care unit. The system enabled reconstruction of 3D microvascular volumes and time-intensity evolution (TIE) curves for cerebral blood flow (CBF) assessment. Global perfusion measurements were successful in 95% of acquisitions. Notably, TIE-derived perfusion patterns at day 5 showed strong correlation with poor neurological outcome (88.9% accuracy) suggesting the prognostic potential of the technique. This study demonstrates, for the first time, the feasibility of bedside volumetric CEUS for dynamic assessment of cerebral perfusion in patients with aSAH. The method offers high spatial and temporal resolution, real-time feedback, and correlation with clinical trajectory. These findings position three dimensional CEUS-based acoustic angiography as a promising tool for early detection of DCI and improved neuromonitoring in the intensive care unit.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".