Longitudinal Assessment of Cerebral Blood Volume Variation in Human Neonates Using Ultrafast Power Doppler and Diverging Waves
Bibliographic record
Abstract
Longitudinal assessment of brain perfusion is a critical parameter for neurodevelopmental outcome of neonates undergoing cardiopulmonary bypass procedure. In this study, we aim to measure the variations of cerebral blood volume (CBV) in human neonates during cardiac surgery, using Ultrafast Power Doppler and freehand scanning. To be clinically relevant, this method must satisfy three criteria: being able to image a wide field of view in the brain, show significant longitudinal CBV variations, and present reproducible results. To address the first point, we performed for the first time transfontanellar Ultrafast Power Doppler using a hand-held phased-array transducer with diverging waves. This increased the field of view more than threefold compared to previous studies using linear transducers and plane waves. We were able to image vessels in the cortical areas as well as the deep grey matter and temporal lobes. Second, we measured the longitudinal variations of CBV on human neonates undergoing cardiopulmonary bypass. When compared to a pre-operative baseline acquisition, the CBV exhibited significant variation during bypass: on average, + 20±3 % in the mid-sagittal full sector ( [Formula: see text]), - 11±3 % in the cortical regions ( [Formula: see text]) and - 10±4 % in the basal ganglia ( [Formula: see text]). Third, a trained operator performing identical scans was able to reproduce CBV estimates with a variability of 4% to 7.5% depending on the regions considered. We also investigated whether vessel segmentation could further improve reproducibility, but found that it actually introduced greater variability in the results. Overall, this study demonstrates the clinical translation of ultrafast power Doppler with diverging-waves and freehand scanning.
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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 | 0.001 |
| 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".