In routine MRI clinical application of compressed sensing, the image quality is often not useful for diagnosis when its CS(Compressed SENSE) acceleration factor is beyond a certain level(e.g. 8 or 10). It is desirable to further accelerate MR sequences in multiple applications such as brachial plexus nerve, coronary artery, and so on. It is possible to use generative adversarial network(GAN) models to further optimize the imaging workflow by improving the image quality of data acquired with high CS factors.
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