Open-Source MR Imaging and Reconstruction Workflow

Purpose 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. Methods 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. Results 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-0 mapping. All sequences, data, and the corresponding processing pipelines are publicly available. Conclusion 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.

Automated summary

This study presents an end-to-end open-source MR imaging workflow that is highly flexible and integrates vendor-independent tools. The workflow allows rapid prototyping across the whole imaging process and can be executed on different MR platforms. It also enables the generation of simulated data using the same sequence as the MRI scanner and the same pipeline for image reconstruction. The results demonstrate the flexibility of the workflow in different imaging scenarios, and all sequences, data, and processing pipelines are publicly available.