An Open-Source, Reproducible MRI Data Acquisition and Reconstruction Workflow Using Pulseq: Multi-Site and Cross-Vendor Validation.
PURPOSE Reproducibility in MRI is limited by variability in data acquisition, formatting, and reconstruction across sites and vendors. This work aimed to mitigate these challenges through an open-source, vendor-independent workflow originally developed for the 2023-24 ISMRM Repeat It with Me: Reproducibility Team Challenge. METHODS Pulseq, extended with advanced features including LABELs and Sequence Definitions for k-space description and execution instructions, was used to harmonize data acquisition. Pulseq-generated raw k-space data were converted into the MRD format using these metadata. Open-source, vendor-independent image reconstruction and post-processing were performed with Gadgetron. Additionally, vendor-native online reconstructions on Siemens platforms (ICE and OpenRecon) were enabled for Pulseq-based sequences. The workflow was validated using MPRAGE and EPI protocols in phantoms and in vivo across nine scanners from five sites and four vendors. Vendor-native acquisition and reconstruction protocols were executed for comparison. RESULTS Pulseq-based sequences executed successfully on all systems, with all data converted and reconstructed without errors. Pulseq-based, Gadgetron-reconstructed images showed good agreement with corresponding vendor-native images across scanners; however, qualitative and quantitative differences remained. Despite detailed natural-language descriptions and meticulous console configuration, vendor-native protocols introduced subtle but notable cross-platform differences in sequence execution and reconstruction. In contrast, the proposed workflow achieved highly consistent sequence execution via Pulseq and standardized reconstruction through Gadgetron across scanners. CONCLUSION We established an open-source MRI workflow that harmonizes data acquisition, formatting, and reconstruction across sites and vendors. This workflow represents a critical step toward fully open and reproducible MRI, facilitating transparency, standardization, and collaboration across the research community.