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 reconstruction method based on INR, which represents the fully-sampled images as the function of pixel 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 pixel-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）一直面临采集时间过长的问题。MRI重建是一种通过跳过部分相位编码线，然后从欠采样测量中恢复高质量图像来缩短扫描时间的解决方案。近年来，隐式神经表示（INR）作为一种新的深度学习方法崭露头角，它将物体表示为空间坐标的连续函数，通常通过多层感知器（MLP）进行参数化。在本文中，我们提出了一种基于INR的新型MRI重建方法，将全采样图像表示为像素坐标与欠采样图像先验特征向量的函数，以克服INR的泛化问题。具体而言，我们引入了一个尺度嵌入编码器，从不同欠采样尺度的MR图像中生成尺度无关的像素特定特征，然后将其与坐标向量拼接，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数据集上进行实验并与其他重建方法比较，评估了所提出方法的性能。我们的定量评估表明，该方法优于其他重建方法。