This work enables reproducibility of advanced computational MRI methods within a comprehensive end-to-end open-source framework and proves that quantitative MRI methods consisting of acquisition and reconstruction were successfully implemented in BART.
Abstract
Purpose: In advanced computational MRI techniques, acquisition and reconstruction techniques are jointly designed. For reproducibility, it is therefore important to provide an open implementation of both. At the same time, any use in a clinical environment usually requires a close integration with the MRI scanner. Ensuring long-time reproducibility and maintenance then poses additional challenges. In this work, we aim to provide a fully integrated open-source framework that can meet these demands. Methods: A software framework to develop pulse sequences is added to the BART toolbox. In addition, a vendor-specific driver sequence is developed that can be used to run the sequence on a clinical MRI scanner enabling online adjustment of all relevant sequence parameters. Using the Pulseq format, the exact same sequence can also be reproduced offline. As proof-of-concept, quantitative MRI methods for T1 and joint water/fat R2∗ mapping using radial FLASH and model-based reconstruction are implemented in the proposed framework. Consistency between online and offline acquisition is validated in phantom and in vivo experiments. Results: Quantitative MRI methods consisting of acquisition and reconstruction were successfully implemented in BART. Acquisition parameters and FOV can be adapted online on a clinical MRI system. Quantitative parameter maps from model-based reconstruction agree for online and offline regenerated Pulseq acquisitions. Conclusion: This work enables reproducibility of advanced computational MRI methods within a comprehensive end-to-end open-source framework.
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.
Qingping Chen, Thomas H. M. Roos, Amaya Murguia et al.· Magnetic Resonance in Medici...· 0 citations
The Gradient Impulse Response Function (GIRF) is widely used to model and correct gradient system imperfections in MRI, but scanner-specific GIRF measurement remains inaccessible to many research groups because existing approaches rely on specialised field monitoring hardware or fragmented and non-reproducible software workflows. To address this limitation, an open-source, end-to-end framework for phantom-based GIRF measurement is presented, providing a reproducible workflow requiring only standard MRI hardware and a spherical water phantom. The framework integrates vendor-independent pulse sequence generation, phantom-based data acquisition, automated data processing, and GIRF estimation. The framework was validated by comparing GIRF-predicted non-Cartesian k-space trajectories with independent measurements acquired using NMR field probes, which served as the gold-standard for trajectory characterization. Accurate prediction of rosette and spiral trajectories was demonstrated across multiple imaging orientations, with substantially lower trajectory error than the corresponding nominal trajectories. By providing the first openly available end-to-end implementation for phantom-based GIRF measurement, the barrier to routine scanner-specific GIRF characterisation is reduced, facilitating broader adoption of GIRF-based methods across the MRI community.
James B. Bacon, Rudy Rizzo, Simon M. Finney et al.· bioRxiv· 0 citations
Purpose: Motion compromises the utility of high-resolution 3D MRI, an established tool in quantitative neuroimaging research. Deep learning-based methods have shown promise for mitigating motion-induced artifacts, but their development typically requires simulated motion-corrupted data. Several open-source tools exist for this task, each implementing different algorithms. However, no scheme currently exists for evaluating the accuracy of these simulations, making it difficult for users to choose the most suitable tool. Developing such a scheme is the aim of this study. Methods: The essential ingredient of the desired scheme is a ground-truth reference simulation that does not suffer from sampling-induced error. To meet this requirement, the proposed scheme, APHABAMAS, leverages a digital phantom whose representations in both the image and Fourier domains can be expressed analytically under arbitrary rigid-body transformations. Results: APHABAMAS is used to quantify the sampling-induced errors of three existing simulation algorithms, establishing their first definitive accuracy-based ranking. Conclusions: APHABAMAS provides a rigorous tool for assessing the accuracy of high-resolution 3D MRI motion-artifact simulations. It allows the accuracy-based ranking of existing simulation algorithms to be established, thereby enabling informed selection of the most suitable algorithm for synthesizing motion-corrupted data.
An open-source reference system for portable low-field MRI designed to support replication, reproducibility, benchmarking, and quantitative comparison is presented, aiming to support cross-site comparability, reproducible research, and collaborative development of future portable low-field MRI technologies.
D. Schote, H. Herthum, Umberto Zanovello et al.· 0 citations
Real‐time phase‐contrast MRI (RT‐PC) enables continuous quantification of physiological flow dynamics across multiple temporal frequencies, but its broader application is limited by complex and fragmented post‐processing workflows. We developed Flow 2.0, a standardized toolbox for RT‐PC designed to integrate segmentation, flow quantification, visualization, and multi‐frequency signal analysis within a unified environment.
Pan Liu, Olivier Balédent· Magnetic Resonance in Medici...· 0 citations
Registration of histological sections to a reference atlas is essential for anatomical localization and region-based quantitative analysis. Although established workflows are powerful, image preparation, registration, quantification, and visualization often rely on multiple software packages, some of which require platform-specific installation or locally configured programming environments. Here, we present NeuroFlow, a browser-based workflow for quantitative analysis of mouse brain histology. NeuroFlow integrates image registration, signal detection, quantification, and visualization within a single interface and operates across major operating systems without additional software installation. It supports affine and nonlinear alignment, as well as real-time oblique reslicing of the reference atlas. All processing is performed locally in a desktop browser, without requiring a local Python environment, MATLAB installation, or associated packages and toolboxes, and without uploading images to a remote server. This design preserves user control over data and keeps intermediate results accessible for inspection and review. NeuroFlow is available at https://guangweizhang.com/tool-neuroflow.html.
A. Rao, Henry Oo, Can Tao et al.· bioRxiv· 0 citations