  For collecting high-quality high-resolution (HR) MR image, we propose a novel
image reconstruction network named IREM, which is trained on multiple
low-resolution (LR) MR images and achieve an arbitrary up-sampling rate for HR
image reconstruction. In this work, we suppose the desired HR image as an
implicit continuous function of the 3D image spatial coordinate and the
thick-slice LR images as several sparse discrete samplings of this function.
Then the super-resolution (SR) task is to learn the continuous volumetric
function from a limited observations using an fully-connected neural network
combined with Fourier feature positional encoding. By simply minimizing the
error between the network prediction and the acquired LR image intensity across
each imaging plane, IREM is trained to represent a continuous model of the
observed tissue anatomy. Experimental results indicate that IREM succeeds in
representing high frequency image feature, and in real scene data collection,
IREM reduces scan time and achieves high-quality high-resolution MR imaging in
terms of SNR and local image detail.
