Importance

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Abstract

Improvements in image quality can be obtained by leveraging non-linear neural-network based feature recovery across multiple imaging modalities. Input imaging data is acquired, including first imaging data and second imaging data acquired using different imaging modalities. The first imaging data can have lower image quality and a larger FOV than the second imaging data. The first and second imaging data can be aligned and the aligned regions can be used to train a neural network to minimize the difference between the second imaging data and output data processed from the overlapping portion of the first imaging data. Once trained, the neural network can be used to generate improved image quality output from all of the first imaging data.

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First Claim