SNR Estimation for Image Quality Evaluation in X-ray CT

Abstract

Purpose: Although the signal-to-noise ratio (SNR) currently used in the field of medical X-ray CT is utilized for local image evaluation in a linear system, it is not used as a comprehensive evaluation index for an entire image. Additionally, since X-ray CT cannot produce a noiseless image for obtaining the signal power required to calculate the SNR, it is impossible to calculate SNR precisely even applying the conventional method. To resolve these problems, we propose SNR * , which is a new method for calculating the estimated value of SNR that can evaluate an entire image even when the original image cannot be obtained. Methods: First, we obtained SNR * using the signal power and noise power calculated respectively from covariance and the difference in the pair of observed images, which are noise-containing images scanned under the same imaging conditions. Next, we verified the error and the accuracy of SNR * . Third, we demonstrated the behavior and accuracy of the SNR * applied to the actually observed image. Results: In the verification experiment, the relative error of SNR * concerning the true value was 0.06% or less, and the coefficient of variation value of SNR * in the demonstration experiment was 0.015 or less, which denoted the accuracy of SNR * . Conclusion: The proposed method realizes SNR measurement even in cases in which only observed images can be obtained and original images cannot be obtained, such as Xray CT images.

Qualitative and Quantitative Evaluation of the Image Quality of MDCT Multiphasic Liver Scans in HCC Patients

“…The CNR and SNR are pivotal metrics for assessing CT image quality. The CNR speci cally measures the discernibility of low-contrast details within CT images by accounting for object contrast and image noise's standard deviation (SD) [17], [18]. Moreover, the SNR offers a comprehensive assessment of the entire image [19].…”