Magnetic Resonance Imaging (MRI) requires a trade-off between resolution, signal-to-noise ratio, and scan time, making high-resolution (HR) acquisition challenging. Therefore, super-resolution for MR image is a feasible solution. However, most existing methods face challenges in accurately learning a continuous volumetric representation from low-resolution image or require HR image for supervision. To solve these challenges, we propose a novel method for MR image super-resolution based on two-factor representation. Specifically, we factorize intensity signals into a linear combination of learnable basis and coefficient factors, enabling efficient continuous volumetric representation from low-resolution MR image. Besides, we introduce a coordinate-based encoding to capture structural relationships between sparse voxels, facilitating smooth completion in unobserved regions. Experiments on BraTS 2019 and MSSEG 2016 datasets demonstrate that our method achieves state-of-the-art performance, providing superior visual fidelity and robustness, particularly in large up-sampling scale MR image super-resolution.

本研究解决了磁共振成像中高分辨率获取的难题，提出了一种基于双因子表示的新方法，以高效地从低分辨率图像学习连续体积表示。同时，采用坐标编码捕捉稀疏体素之间的结构关系，有助于在未观测区域的平滑补全。实验表明，该方法在多项数据集上达到最先进的性能，视觉效果和稳健性优于现有技术。

磁共振图像超分辨率的双因子表示学习