开源医学影像重建框架，支持深度学习与定量成像
MRpro: open framework for model-based, learned, and quantitative MR imaging
- 基于PyTorch构建，统一管理数据与元信息
- 集成多种重建算法，支持低场磁共振数据
- 提供可复现的深度学习模块与公开数据集
我们提出一个基于PyTorch的开源图像重建工具包MRpro，支持现代深度学习重建。其采用ISMRMRD、DICOM、NIfTI等开放数据格式，便于集成到现有流程并兼容多设备数据。框架包含三大核心：一是统一的MR数据结构，用于一致处理数据集及元信息（如k空间轨迹）；二是可组合的操作符库、可近似函数与优化算法，包括适用于所有常见轨迹的统一傅里叶算子，以及专为低场应用设计的B0校正算子；三是深度学习组件，如数据一致性层、可微优化层、先进骨干网络，及对公共数据集的访问，保障可复现性。我们在自动重建、迭代SENSE、基于深度学习的重建和定量参数估计中验证了MRpro的性能，使用了公开数据、模拟数据及在0.3 T、0.6 T和47 mT实测的低场数据。
原文摘要 · Abstract (English)
We preseent an open-source image reconstruction package built upon PyTorch, enabling modern deep-learning reconstructions. It uses open data formats for input and output (ISMRMRD, DICOM, NIfTI), allowing easy integration into existing pipelines and support for data from different devices. 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 operators specifically developed for low-field applications, such as a B0-correction operator. 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, state-of-the-art backbone networks, and access to public datasets to facilitate reproducibility. We demonstrate MRpro across automatic reconstruction, iterative SENSE, deep-learning-based reconstruction, and quantitative parameter estimation. Applications use public and simulated datasets and measured lowfield data acquired at 0.3 T, 0.6 T, and 47 mT.
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