Automatic SNR measurement of brain MR images using a deep learning-based approach.

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 (r > 0.86) and low relative errors (<7%) for whole-brain, WM, and CSF. Bland-Altman analysis demonstrated a small paired bias and narrow 95% limits of agreement across sequences. 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.