Magnetic resonance imaging (MRI) always suffers from long acquisition times. Parallel imaging (PI) is one solution to reduce scan time by periodically skipping certain K-space lines and then reconstructing high-quality images from undersampled measurements. Recently, implicit neural representation (INR) has emerged as a new deep learning method that represents an object as a continuous function of spatial coordinates, and this function is normally parameterized by a multilayer perceptron (MLP). In this paper, we propose a novel MRI PI reconstruction method based on INR, which represents the reconstructed fully-sampled images as the function of voxel coordinates and prior feature vectors of undersampled images to overcome the generalization problem of INR. Specifically, we introduce a scale-embedded encoder to produce scale-independent voxel-specific features from MR images with different undersampling scales and then concatenate with coordinate vectors to recover fully-sampled MR images, thus achieving multiple scale reconstructions. The performance of the proposed method was assessed by experimenting with publicly available MRI datasets and was compared with other reconstruction methods. Our quantitative evaluation demonstrates the superiority of the proposed method over alternative reconstruction methods.

翻译：磁共振成像（MRI）通常存在采集时间较长的问题。并行成像（PI）通过周期性地跳过特定K空间线，再利用欠采样测量重建高质量图像，是缩短扫描时间的一种有效方案。近年来，隐式神经表示（INR）作为一种新型深度学习方法，能够将物体表示为空间坐标的连续函数，该函数通常由多层感知机（MLP）参数化。本文提出了一种基于INR的MRI PI重建新方法，将重建的全采样图像表示为体素坐标与欠采样图像先验特征向量的函数，以克服INR的泛化性问题。具体而言，我们引入了尺度嵌入编码器，从不同欠采样尺度的MR图像中提取尺度无关的体素特异特征，并与坐标向量拼接以恢复全采样MR图像，从而实现多尺度重建。通过公开MRI数据集实验评估了所提方法的性能，并与其它重建方法进行了比较。定量评价结果表明，所提方法优于其它重建方法。