• First comprehensive open-source web tool for SNR evaluation in MRI • Supports multiple SNR estimation methods and image reconstruction algorithms- • Modular cloud-native architecture for accessible, reproducible, scalable SNR assessment • Interactive GUI allows visualization and analysis of SNR, g factor, and coil sensitivity maps • Results are exportable in NIfTI, MATLAB, and JSON format for downstream integration and QA Signal-to-noise ratio (SNR) is a key performance metric in magnetic resonance imaging (MRI) to evaluate pulse sequences, receive coils, and image reconstruction algorithms. A variety of methods have been proposed to estimate SNR. However, the lack of consistent and broadly available open-source implementations has been a challenge for reliable SNR comparisons in clinical and research settings. To address this gap, this work introduces MR Optimum, a cloud-native, open-source platform for standardized SNR analysis. MR Optimum integrates established SNR estimation techniques within a flexible, modular software architecture. A web-based user interface supports data upload, task configuration, cloud computations, and real-time results visualization. MR Optimum leverages serverless computing technologies (AWS Lambda and Fargate) to perform scalable, event-driven processing of MRI rawdata and allow users to calculate SNR using established methods: multiple replicas, pseudo multiple replicas, generalized pseudo multiple replicas, and analytic methods. Results include SNR maps, noise covariance and noise coefficient matrices, coil sensitivity profiles, and g factor maps. The web interface enables interactive visualization and histogram analysis based on regions of interest. Results can be exported in MATLAB, NIfTI, and JSON formats. By providing a unified computational environment, MR Optimum ensures reproducibility, and democratizes access to state-of-the-art SNR estimation, promoting multi-center harmonization and quality assurance.
Montin et al. (2026) studied this question.