Practical signal-to-noise ratio mapping using single clinical MR images

Accurate signal-to-noise ratio (SNR) measurement is essential for evaluating image quality in magnetic resonance imaging (MRI). While the subtraction-map method provides precise SNR measurements, it requires two consecutive acquisitions, limiting its clinical applicability. This study aims to develop and validate a method for practical SNR measurement using clinical MRI images. The proposed method generates an SNR map by computing a noise-only image from a single MRI image using pixel shifting and edge component removal. The accuracy of our method was compared with the subtraction-map method in three evaluations: (1) optimization of a key parameter for edge component removal, (2) analysis of spatial resolution and SNR level effects, and (3) validation using brain MRI images. The study included brain MRI from 188 patients, and SNR measurements were performed on the resulting images. Correlation coefficients and Bland-Altman analysis were used for comparisons. Parameter optimization identified an optimal threshold for separating noise and edge components. Higher spatial resolution improved accuracy, whereas lower resolution and low SNR conditions led to overestimation. In clinical MRI, the proposed method showed a strong correlation with the subtraction-map method (Spearman r = 0.96), and the highest average error rate was 8.1% in T1-weighted images. Bland-Altman analysis demonstrated good agreement across sequences and regions. This method enables practical SNR estimation from a single image, eliminating the need for repeated acquisitions. While limitations remain in low-SNR or structurally complex regions, the method shows promise as a practical tool for retrospective and routine clinical image quality assessments.