2023-10-22 11:12:26
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Harvard scientist, Dushan Wadduwage, has developed a groundbreaking microscopy technique called De-scattering with Excitation Patterning (DEEP), which combines optics and AI algorithms to capture high-quality images of the brain. This technique allows researchers to overcome the limitations of previous microscopy methods by excising multiple points on the tissue simultaneously and using computational algorithms to reconstruct clear images. The DEEP-squared AI model has the potential to revolutionize brain imaging and could lead to further advancements in the field.
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For decades, scientists have been working to develop methods to image the depths of a living brain. Fluorescence microscopy and two-photon microscopy were significant advancements, but they came with their own limitations. Fluorescence microscopy had issues with wavelength and scattering, while two-photon microscopy was time-consuming and could only excite one point at a time.
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Wadduwage's team introduced the DEEP technique, which involves exciting multiple points on the tissue simultaneously using pre-encoded excitation patterns. These patterns, along with the detected images, are processed using a computational algorithm to reconstruct clear images. This method can produce high-quality images with just hundreds of images, compared to the hundreds of thousands needed for point-scanning microscopy.
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Wadduwage and his team further improved the DEEP technique by training an AI model, called DEEP-squared, to produce reconstructed images with even greater accuracy. They used typical point-scanning microscopy images as reference and simulated scattered input images using physics. The AI model then generated reconstructed images that closely resembled the ground truth. This advancement brings us closer to achieving clearer brain imaging with minimal images.
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Wadduwage plans to continue improving DEEP-squared by enhancing instrument design to acquire data faster. He highlights that the success of DEEP-squared is a result of cross-disciplinary cooperation among biologists, physicists, and computer scientists. This collaborative approach will continue to drive future innovations in the field of brain imaging.
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