用双因子分解提升磁共振图像超分辨率，无需高分辨标签
Learning Two-factor Representation for Magnetic Resonance Image Super-resolution
- 将强度信号分解为可学习基与系数因子，实现低分辨率图像的连续体素表示
- 在BraTS 2019和MSSEG 2016上达到当前最优性能，大倍数上采样效果突出
- 适合医学影像超分辨率任务，尤其适用于无高分辨监督的场景
磁共振成像（MRI）需在分辨率、信噪比和扫描时间之间权衡，高分辨率采集困难。因此，基于低分辨率图像的超分辨率成为可行方案。然而，现有方法在从低分辨率图像中准确学习连续体素表示方面存在挑战，或依赖高分辨率图像进行监督。为此，本文提出一种基于双因子表示的MR图像超分辨率新方法。具体地，将强度信号分解为可学习基与系数因子的线性组合，实现从低分辨率MRI图像高效构建连续体素表示；同时引入基于坐标的编码方式，捕捉稀疏体素间的结构关系，促进未观测区域的平滑补全。在BraTS 2019与MSSEG 2016数据集上的实验表明，该方法性能达到当前最优水平，视觉保真度高且鲁棒性强，尤其在大倍数上采样下表现优异。
原文摘要 · Abstract (English)
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.
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