标题： MRpro - 基于 PyTorch 的 MR 重建和处理开源包 显示英文标题

标题： MRpro - open PyTorch-based MR reconstruction and processing package

摘要： 我们引入了MRpro，一个基于PyTorch和开放数据格式的开源图像重建包。 该框架包括三个主要领域。 首先，它提供了统一的数据结构，用于一致地操作MRI数据集及其相关元数据（例如，k空间轨迹）。 其次，它提供了一个可组合算子、可逼近泛函和优化算法的库，包括适用于所有常见轨迹的统一傅里叶算子以及用于定量MRI的扩展相位图仿真。 这些组件用于创建可以直接使用的关键重建算法实现。 第三，对于深度学习，MRpro包括数据一致性层、可微分优化层以及最先进的主干网络，并整合公共数据集以促进可重复性。 MRpro作为一项由自动化质量控制支持的协作项目开发。 我们在多个应用中展示了MRpro的通用性，包括笛卡尔、径向和螺旋采集；运动校正重建；心脏MRI指纹识别；学习的空间自适应正则化权重；基于模型的学习图像重建和定量参数估计。 MRpro为MRI图像重建提供了一个可扩展的框架。 以可重复性和可维护性为核心，它促进了协作开发，并为未来的MRI成像研究提供了基础。 显示英文摘要

摘要： We introduce MRpro, an open-source image reconstruction package built upon PyTorch and open data formats. The framework comprises three main areas. First, it provides unified data structures for the consistent manipulation of MR datasets and their associated metadata (e.g., k-space trajectories). Second, it offers a library of composable operators, proximable functionals, and optimization algorithms, including a unified Fourier operator for all common trajectories and an extended phase graph simulation for quantitative MR. These components are used to create ready-to-use implementations of key reconstruction algorithms. Third, for deep learning, MRpro includes essential building blocks such as data consistency layers, differentiable optimization layers, and state-of-the-art backbone networks and integrates public datasets to facilitate reproducibility. MRpro is developed as a collaborative project supported by automated quality control. We demonstrate the versatility of MRpro across multiple applications, including Cartesian, radial, and spiral acquisitions; motion-corrected reconstruction; cardiac MR fingerprinting; learned spatially adaptive regularization weights; model-based learned image reconstruction and quantitative parameter estimation. MRpro offers an extensible framework for MR image reconstruction. With reproducibility and maintainability at its core, it facilitates collaborative development and provides a foundation for future MR imaging research.