How to See Capillaries in a Living Brain express

Original: "ULMShare: A Large-Scale In Vivo Ultrasound Localization Microscopy Dataset for Microvascular Imaging"
arXiv:2606.07851 · 2026-06-05 · CC BY 4.0 · 1 min
Scientists have assembled a vast open collection of ultrasound images of mouse brains so that anyone can learn to spot the tiniest vessels.

Photographing a delicate spiderweb trembling in the wind: that's roughly what it takes to look into the blood vessels of a living brain. Ultrasound localization microscopy (ULM) turns ordinary ultrasound into a tool capable of seeing individual capillaries, but only if you get the settings just right.

A new dataset called ULMShare is a shared sandbox for engineers and scientists. Inside are 99 recordings of microvessels in mouse brains, obtained through the skull bones. There were 61 mice of different ages and weights, using two types of transducers, different scanning planes, and even different anesthesia methods. All together, it takes up 30 terabytes — roughly 6,000 high-quality movies.

The finest vessel they managed to see is 50 times thinner than a human hair.

Usually, each lab records in its own way, making comparisons nearly impossible. ULMShare provides a common benchmark: you can take the same recordings and see whose algorithm reconstructs the image better. The authors have already calculated how completely vessels are filled in the images and assessed sharpness using ring correlation (a way to check how well two images of the same spot match in fine details).

The mice in the experiment weighed on average 17.7 grams — about the same as four teaspoons of sugar.

In the science fiction story "Flowers for Algernon," a mouse's brain becomes the key to unlocking the mysteries of intelligence — here, the mouse brain helps refine a technology that might one day reveal hidden vascular catastrophes in humans.

Scientists
Willebrord Snellius
Concepts
brain scan medical image segmentation medical acoustic imaging noninvasive brain monitoring live cell imaging maximum likelihood tumor segmentation ultrasound neural network memory architecture mri laser ultrasound imaging neural recording technology