The speed of data acquisition in many types of imaging technologies, including MRI, depends on the number of samples taken by the machine. When the number of samples collected is small, deep neural networks can be used to remove the resulting noise and visual artifacts.
The technology works. Very well. But there is no standard theoretical framework – no complete theory – to describe why It works out.
in one paper submitted to the NeurIPS conference end of 2021, Ulugbek Kamilov, at the McKelvey School of Engineering at Washington University in St. Louis, and co-authors have outlined a path to a clear framework. Kamilov is an assistant professor in the Preston M. Green Department of Electrical & Systems Engineering and the Department of Computer Science & Engineering.
Kamilov’s results prove, with some limitations, that an accurate image can be obtained by a deep neural network from very few samples if the image is of the type that can be represented by the network.
The result is a starting point for a solid understanding of why deep learning AI is able to produce accurate images, Kamilov said. It also has the potential to help determine the most efficient sampling method and still get an accurate picture.
This research was supported by NSF awards CCF-1813910, CCF-2043134 and CCF-204629 and by the Los Alamos National Laboratory’s Laboratory Directed Research and Development program under project #20200061DR.
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