3D Transcranial Dynamic Ultrasound Localization Microscopy in the Mouse Brain Using a Row-Column Array
Bibliographic record
Abstract
OBJECTIVE: To assess the role of brain hemodynamics in neurodegenerative diseases, current imaging technologies remain insufficient due to limitations in spatial or temporal resolution for quantitative mapping of pulsatile flow in the whole brain. This study aims to demonstrate the feasibility of 3D transcranial Dynamic Ultrasound Localization Microscopy (DULM) for spatiotemporal blood flow measurements in the brain, addressing limitations of 2D imaging for velocity estimation within the 3D complex vascularized structures. METHODS: We used a (128+128)-element, 12 MHz Row-Column Array (RCA) to perform transcranial DULM imaging in anesthetized mice (n = 7 in total). The RCA setup allows for reduced element count while maintaining a large field of view and high frame rate compared to matrix arrays. Transcranial images were acquired at a 750-Hz volume rate using an optimized microbubble concentration and a sequence of 42 tilted plane waves. Microbubbles were localized and tracked, enabling super-resolved dynamic density and velocity maps of the 3D brain vascular network. RESULTS: Pulsatile flows were observed with 3D DULM in 7 mice. The segmentation of cortical vessels indicated that pulsatility in arteries was significantly higher than in veins, consistent across all mice and aligning with findings in existing literature. CONCLUSION: This study demonstrates for the first time the feasibility and reproducibility of obtaining high spatiotemporal resolution images of mouse brain vasculature using transcranial DULM with a RCA. SIGNIFICANCE: This work highlights the potential of RCA 3D DULM for non-invasive cerebral hemodynamics studies, it might enable comprehensive vascular imaging suitable for research in early-stage neurodegenerative diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".