MR Physics, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.
MR R&D Collaborations, Siemens Medical Solutions USA Inc, Chicago, Illinois.
Magn Reson Med. 2022 Dec;88(6):2395-2407. doi: 10.1002/mrm.29384. Epub 2022 Aug 15.
This work presents an end-to-end open-source MR imaging workflow. It is highly flexible in rapid prototyping across the whole imaging process and integrates vendor-independent openly available tools. The whole workflow can be shared and executed on different MR platforms. It is also integrated in the JEMRIS simulation framework, which makes it possible to generate simulated data from the same sequence that runs on the MRI scanner using the same pipeline for image reconstruction.
MRI sequences can be designed in Python or JEMRIS using the Pulseq framework, allowing simplified integration of new sequence design tools. During the sequence design process, acquisition metadata required for reconstruction is stored in the MR raw data format. Data acquisition is possible on MRI scanners supported by Pulseq and in simulations through JEMRIS. An image reconstruction and postprocessing pipeline was implemented into a Python server that allows real-time processing of data as it is being acquired. The Berkeley Advanced Reconstruction Toolbox is integrated into this framework for image reconstruction. The reconstruction pipeline supports online integration through a vendor-dependent interface.
The flexibility of the workflow is demonstrated with different examples, containing 3D parallel imaging with controlled aliasing in volumetric parallel imaging (CAIPIRINHA) acceleration, spiral imaging, and B mapping. All sequences, data, and the corresponding processing pipelines are publicly available.
The proposed workflow is highly flexible and allows integration of advanced tools at all stages of the imaging process. All parts of this workflow are open-source, simplifying collaboration across different MR platforms or sites and improving reproducibility of results.
本工作提出了一个端到端的开源磁共振成像工作流程。它在整个成像过程中的快速原型设计中具有高度的灵活性，并集成了与供应商无关的开源工具。整个工作流程可以在不同的磁共振平台上共享和执行。它还集成在 JEMRIS 模拟框架中，这使得可以使用在 MRI 扫描仪上运行的相同序列从相同序列生成模拟数据，使用相同的图像重建管道进行图像重建。
可以使用 Pulseq 框架在 Python 或 JEMRIS 中设计 MRI 序列，从而简化新序列设计工具的集成。在序列设计过程中，重建所需的采集元数据存储在 MR 原始数据格式中。可以在 Pulseq 支持的 MRI 扫描仪上以及通过 JEMRIS 进行模拟中进行数据采集。实现了一个图像重建和后处理管道到 Python 服务器中，允许实时处理正在采集的数据。Berkeley 高级重建工具箱集成到这个框架中用于图像重建。该重建管道支持通过与供应商相关的接口进行在线集成。
通过不同的示例展示了工作流程的灵活性，其中包含具有容积并行成像（CAIPIRINHA）加速的控制混杂的 3D 并行成像、螺旋成像和 B 映射。所有序列、数据和相应的处理管道都公开可用。
所提出的工作流程具有高度的灵活性，并允许在成像过程的所有阶段集成先进的工具。该工作流程的所有部分都是开源的，简化了不同磁共振平台或站点之间的协作，并提高了结果的可重复性。
