RealKeyMorph：基于真实世界坐标关键点的分辨率无关图像配准
RealKeyMorph: Keypoints in Real-world Coordinates for Resolution-agnostic Image Registration
AI总结：
提出分辨率无关的图像配准方法RealKeyMorph，通过利用扫描仪仿射矩阵将关键点转换至真实世界坐标并集成到训练中，避免了重采样伪影，在腹部MRI和脑部数据集上验证了其优势。
摘要：
许多现实场景需要对一对空间分辨率不同的医学图像进行配准，这可能是由于图像采集参数（如像素间距、层厚和视野）的差异造成的。然而，以往所有基于机器学习的配准技术都将图像重采样到固定分辨率。这是次优的，因为重采样可能会因插值引入伪影。为了解决这个问题，我们提出了RealKeyMorph（RKM），一种分辨率无关的图像配准方法。RKM是KeyMorph的扩展，KeyMorph是一种配准框架，其工作原理是训练网络学习给定图像对的对应关键点，然后使用闭式关键点匹配步骤推导出对齐它们的变换。为了避免重采样并实现对原始数据的操作，RKM在扫描仪的真实世界坐标中输出关键点。为此，我们利用扫描仪（例如MRI机器）产生的仿射矩阵，该矩阵编码了从体素坐标到真实世界坐标的映射。通过将关键点转换到真实世界空间并将其集成到训练过程中，RKM有效地使提取的关键点具有分辨率无关性。在我们的实验中，我们展示了RKM在腹部MRI正交2D堆栈配准任务以及大脑数据集中具有不同分辨率的3D体积上的优势。
英文摘要：
Many real-world settings require registration of a pair of medical images that differ in spatial resolution, which may arise from differences in image acquisition parameters like pixel spacing, slice thickness, and field-of-view. However, all previous machine learning-based registration techniques resample images onto a fixed resolution. This is suboptimal because resampling can introduce artifacts due to interpolation. To address this, we present RealKeyMorph (RKM), a resolution-agnostic method for image registration. RKM is an extension of KeyMorph, a registration framework which works by training a network to learn corresponding keypoints for a given pair of images, after which a closed-form keypoint matching step is used to derive the transformation that aligns them. To avoid resampling and enable operating on the raw data, RKM outputs keypoints in real-world coordinates of the scanner. To do this, we leverage the affine matrix produced by the scanner (e.g., MRI machine) that encodes the mapping from voxel coordinates to real world coordinates. By transforming keypoints into real-world space and integrating this into the training process, RKM effectively enables the extracted keypoints to be resolution-agnostic. In our experiments, we demonstrate the advantages of RKM on the registration task for orthogonal 2D stacks of abdominal MRIs, as well as 3D volumes with varying resolutions in brain datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
