Magnetic resonance imaging (MRI) always suffered from the problem of long acquisition time. MRI reconstruction is one solution to reduce scan time by skipping certain phase-encoding lines and then restoring 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 parallel reconstruction method based on INR, which represents the fully-sampled images as the function of voxel coordinates and prior feature vectors of undersampled images for overcoming the generalization problem of INR. Specifically, we introduce a scale-embedded encoder to produce scale-independent voxel-specific features from MR images with different undersampled scales and then concatenate with coordinates vectors to recover fully-sampled MR images via an MLP, thus achieving arbitrary scale reconstruction. The performance of the proposed method was assessed by experimenting on publicly available MRI datasets and 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）长期受困于采集时间过长的问题。MRI重建是通过跳过特定相位编码线，然后从欠采样测量中恢复高质量图像来缩短扫描时间的一种解决方案。近年来，隐式神经表示（INR）作为一种新兴的深度学习方法出现，它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的新型MRI并行重建方法，该方法将全采样图像表示为体素坐标和欠采样图像先验特征向量的函数，以克服INR的泛化问题。具体而言，我们引入了一种尺度嵌入编码器，从不同欠采样尺度的MR图像中生成与尺度无关的体素特定特征，然后与坐标向量拼接，通过MLP恢复全采样MR图像，从而实现任意尺度的重建。通过在公开MRI数据集上进行实验，评估了所提方法的性能，并与其他重建方法进行了比较。我们的定量评估证明了所提方法相较于其他替代重建方法的优越性。