High-resolution magnetic resonance imaging (MRI) is essential in clinical diagnosis. However, its long acquisition time remains a critical issue. Parallel imaging (PI) is a common approach to reduce acquisition time by periodically skipping specific k-space lines and reconstructing images from undersampled data. This study presents a generalized implicit neural representation (INR)-based framework for MRI PI reconstruction, addressing limitations commonly encountered in conventional methods, such as subject-specific or undersampling scale-specific requirements and long reconstruction time. The proposed method overcomes these limitations by leveraging prior knowledge of voxel-specific features and integrating a novel scale-embedded encoder module. This encoder generates scale-independent voxel-specific features from undersampled images, enabling robust reconstruction across various undersampling scales without requiring retraining for each specific scale or subject. The framework's INR model treats fully sampled MR images as a continuous function of spatial coordinates and prior voxel-specific features, efficiently reconstructing high-quality MR images from undersampled data. Extensive experiments on publicly available MRI datasets demonstrate the superior performance of the proposed method in reconstructing images at multiple acceleration factors (4x, 5x, and 6x), achieving higher evaluation metrics and visual fidelity compared to state-of-the-art methods. In terms of efficiency, this INR-based approach exhibits notable advantages, including reduced floating point operations and GPU usage, allowing for accelerated processing times while maintaining high reconstruction quality. The generalized design of the model significantly reduces computational resources and time consumption, making it more suitable for real-time clinical applications.

翻译：高分辨率磁共振成像在临床诊断中至关重要，但其较长的采集时间仍是一个关键问题。并行成像是通过周期性跳过特定k空间线并从欠采样数据重建图像以减少采集时间的常用方法。本研究提出了一种基于广义隐式神经表示的磁共振并行成像重建框架，解决了传统方法中常见的局限性，如对特定受试者或特定欠采样比例的依赖以及较长的重建时间。该方法通过利用体素特异性特征的先验知识并整合新颖的尺度嵌入编码器模块来克服这些限制。该编码器从欠采样图像生成与尺度无关的体素特异性特征，从而能够在不同欠采样比例下实现稳健重建，无需针对每个特定比例或受试者重新训练。该框架的隐式神经表示模型将全采样磁共振图像视为空间坐标和先验体素特异性特征的连续函数，从而能够从欠采样数据高效重建高质量的磁共振图像。在公开可用的磁共振数据集上进行的大量实验表明，该方法在多个加速因子下重建图像均表现出优越性能，与现有先进方法相比，获得了更高的评估指标和视觉保真度。在效率方面，这种基于隐式神经表示的方法展现出显著优势，包括减少浮点运算和GPU使用量，从而在保持高重建质量的同时实现更快的处理速度。该模型的广义设计显著降低了计算资源和时间消耗，使其更适合实时临床应用。