Researchers at the University of Minnesota (UMN) leveraged data science tools and machine learning to speed up the process of magnetic resonance imaging (MRI) reconstruction.
As part of fine-tuning the traditional image-compression process, the researchers found that the combination of data science and machine learning could overcome some of the challenges related to relying solely on deep learning, which could misinterpret MRI results due to biases in the algorithm resulting from insufficient training data.
Said UMN's Mehmet Akçakaya, "We found that if you tune the classical methods, they can perform very well. So, maybe we should go back and look at the classical methods and see if we can get better results. There is a lot of great research surrounding deep learning as well, but we're trying to look at both sides of the picture to see where we can find the best performance, theoretical guarantees and stability."
From University of Minnesota News and Events
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