论文标题
使用基于功能的坐标网络，MRI中的比例尺不可屈服的超分辨率
Scale-Agnostic Super-Resolution in MRI using Feature-Based Coordinate Networks
论文作者
论文摘要
我们建议使用坐标网络解码器完成MRI超级分辨率的任务。坐标网络的连续信号表示可以使这种方法具有比例 - 敏捷性，即可以在连续的尺度范围内训练，然后以任意分辨率进行查询。由于难以对固有的嘈杂数据执行超分辨率，因此我们在多种降级策略下分析了网络行为。最后，我们将此方法与使用定量指标和Voxel中实施的放射科医生研究（我们新开发的用于基于网络的医学图像评估的工具）进行了比较。
We propose using a coordinate network decoder for the task of super-resolution in MRI. The continuous signal representation of coordinate networks enables this approach to be scale-agnostic, i.e. one can train over a continuous range of scales and subsequently query at arbitrary resolutions. Due to the difficulty of performing super-resolution on inherently noisy data, we analyze network behavior under multiple denoising strategies. Lastly we compare this method to a standard convolutional decoder using both quantitative metrics and a radiologist study implemented in Voxel, our newly developed tool for web-based evaluation of medical images.
