Researchers have developed a curvelet-based algorithm to quickly measure and reconstruct whole-brain vasculature and brain blood flow in mice. The work could enable future research into the neurovascular mechanisms underlying conditions like Alzheimer’s disease.
The new approach deploys ultrasound technology to produce whole-brain images of animal microvasculature in just a few seconds. Instead of averaging two or three minutes of data together, the method needs only one or two seconds of data and has a good image with improved temporal resolution.
The brain blood flow measurement method relies on rotating and scaling many small, arbitrary curves to fit the local structure of microbubble imaging data. Locally, vessels are very similar to curvelets, these small curves. You can break down any arbitrarily shaped vessel into a combination of arbitrary curvelets. When you apply a curvature transform to microbubble data, you need only a tiny amount of data to represent the whole complete vessel structure. Vessels are heterogeneous, but locally, they follow a similar homogenous structure. The new approach combines the curvelet model with a sparsity-promoting algorithm.
Microbubbles are useful as ultrasound imaging contrast in clinical ultrasound of humans. The technology can be a noninvasive assessment of stroke, vascular occlusion, and neurovascular health.