ArticleActa radiologica open2025
Automatic SNR measurement of brain MR images using a deep learning-based approach.
Article in Acta radiologica open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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7 authors.
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Abstract
Background: Signal-to-noise ratio (SNR) is a key metric for evaluating MRI image quality, but conventional measurement methods are time-consuming and operator-dependent. Deep learning offers potential for automating this process. Purpose: To develop and validate a deep learning-based method for automatic SNR measurement from single MRI images. Material and methods: A Pix2Pix framework with a U-Net++ generator and GAN-based discriminator was trained using axial brain MRI images (T1WI, T2WI, and FLAIR) from a 3T scanner. The model generated signal and noise maps from a single image, and SNR maps were computed by pixel-wise division. Whole-brain, white matter (WM), and cerebrospinal fluid (CSF) regions were automatically segmented for regional SNR measurement. The subtraction-map method served as the reference. Structural similarity index (SSIM), correlation coefficients, and Bland-Altman analyses were used to evaluate agreement. Results: Across all sequences, the mean SSIM was 0.95 ± 0.02. SNR values showed strong correlations with the reference method ( Conclusion: The proposed deep learning method enables automatic, accurate, and observer-independent SNR quantification from single MR images, supporting clinical and research image quality evaluation.
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