This project doesn't use AI to improve the image, they use it to estimate the EMI noise from the surroundings. So they're not "filling in the gaps" in the actual resulting 3D voxel volume with fantasy voxels (which I hope will never ever fly in a clinical setting).
"To tackle the EMI signals from the external environments and internal low-cost electronics during scanning, we developed a deep learning driven EMI cancellation scheme"
So it's kind of using deep learning to improve the SnR in the RF reception. Of course this could theoretically also lead to "fantasy voxels" but due to the nature of MRI decoding, I'm willing to guess that bad predictions of the EMI interference will not show up as unnoticeable alterations of realistic tissue imaging but rather as artefacts all over the volume, like you normally see in clinical MRIs that weren't taken 100% optimally.
"To tackle the EMI signals from the external environments and internal low-cost electronics during scanning, we developed a deep learning driven EMI cancellation scheme"
So it's kind of using deep learning to improve the SnR in the RF reception. Of course this could theoretically also lead to "fantasy voxels" but due to the nature of MRI decoding, I'm willing to guess that bad predictions of the EMI interference will not show up as unnoticeable alterations of realistic tissue imaging but rather as artefacts all over the volume, like you normally see in clinical MRIs that weren't taken 100% optimally.