We developed deep learning super-resolution MR spectroscopic imaging (MRSI) to map tumor metabolism in patients with mutant IDH glioma. A generative adversarial network (GAN) architecture comprised of a UNet neural network as the generator network and a discriminator network for adversarial training was employed to upsample MR spectroscopic imaging data with a factor of four. The preliminary results on simulated and in vivo data indicate that the proposed deep learning method is effective in enhancing the spatial resolution of metabolite maps which may better guide treatment in mutant IDH glioma patients.
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