ULMShare: New Large-Scale Dataset Released for Ultrasound Microscopy Brain Imaging Research
A team of researchers has published ULMShare, an open-access dataset of 99 whole-brain ultrasound localization microscopy (ULM) acquisitions from 61 healthy mice, totaling 30 terabytes of raw data. ULM is a technique that enables microscopic-resolution imaging of cerebral blood vessels in living subjects, but its development has been hampered by a lack of large, standardized public datasets. ULMShare aims to accelerate algorithm development—including machine-learning approaches—by providing a broad, benchmarkable resource for the research community.
Ultrasound Localization Microscopy (ULM) is an emerging imaging modality that can resolve the brain's tiny blood vessels in living animals at microscopic scales, but its multi-stage processing pipeline is highly sensitive to acquisition and reconstruction choices. Progress in the field, particularly for machine-learning-based methods, has been constrained by the scarcity of large, publicly available datasets. To address this, Rauby and colleagues introduce ULMShare, comprising 99 whole-brain transcranial ULM acquisitions from 61 healthy mice of varying ages, weights, and sexes. The dataset spans three experimental procedures, multiple injection and anesthesia protocols, two different ultrasound probes, and various imaging planes, offering substantial diversity for method testing. Each acquisition includes raw ultrasonic data, detailed metadata, an illustrative reconstruction, and microbubble trajectory files, along with quality metrics such as vascular saturation, Fourier Ring Correlation, and expert visual gradings. The full 30TB dataset is hosted on the Federated Research Data Repository, with supplementary resources on GitHub. The authors position ULMShare as a standardized benchmark to enable rigorous, reproducible evaluation and comparison of ULM algorithms going forward.
What's missing
The dataset is currently limited to healthy mice, so its generalizability to disease models or human subjects remains untested. The paper does not yet report downstream validation showing that algorithms trained or benchmarked on ULMShare improve performance on independent data. As a preprint, the work has not yet undergone formal peer review, and the arXiv DOI registration is listed as pending.
What different sources said
- arXiv physicsCenter
ULMShare: A Large-Scale In Vivo Ultrasound Localization Microscopy Dataset for Microvascular Imaging
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