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Imaging microscopic samples requires capturing multiple, sequential measurements, then using computational algorithms to reconstruct a single, high-resolution image. This process can work well when the sample is static, but if it's moving—as is common with live, biological specimens—the final image may be blurry or distorted.

Now, Berkeley researchers have developed a method to improve temporal resolution for these dynamic samples. In a study published in Nature Methods, they demonstrate a new computational imaging tool, dubbed the neural space-time model (NSTM), that uses a small, lightweight neural network to reduce motion artifacts and solve for the motion trajectories.

"The challenge with imaging dynamic samples is that the reconstruction algorithm assumes a static scene," said lead author Ruiming Cao, a Ph.D. student in bioengineering.

"NSTM extends these computational methods to dynamic scenes by modeling and reconstructing the motion at each timepoint. This reduces the artifacts caused by motion dynamics and allows us to see those super-fast-paced changes within a sample."

According to the researchers, NSTM can be integrated with existing systems without the need for additional, expensive hardware. And it's highly effective. "NSTM has been shown to provide roughly an order of magnitude improvement on the temporal resolution," said Cao.

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