  Magnetic resonance imaging (MRI) usually faces lengthy acquisition times,
prompting the exploration of strategies such as parallel imaging (PI) to
alleviate this problem by periodically skipping specific K-space lines and
subsequently reconstructing high-quality images from the undersampled K-space.
Implicit neural representation (INR) has recently emerged as a promising deep
learning technique, characterizing objects as continuous functions of spatial
coordinates typically parameterized by a multilayer perceptron (MLP). In this
study, we propose a novel MRI PI reconstruction method that uses INR. Our
approach represents reconstructed fully-sampled images as functions of voxel
coordinates and prior feature vectors from undersampled images, addressing the
generalization challenges of INR. Specifically, we introduce a scale-embedded
encoder to generate scale-independent, voxel-specific features from MR images
across various undersampling scales. These features are then concatenated with
coordinate vectors to reconstruct fully-sampled MR images, facilitating
multiple-scale reconstructions. To evaluate our method's performance, we
conducted experiments using publicly available MRI datasets, comparing it with
alternative reconstruction techniques. Our quantitative assessment demonstrates
the superiority of our proposed method.
