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Do you ever get confused by the difference in the terms SNR and CNR? What do these terms even mean? 

SNR and CNR are basically Signal to Noise Ratio and Contrast to Noise Ratio, respectively, and they are both measurement parameters for the quality of the image generated during an X-ray examination exercise. 

The contrast-to-noise ratio (CNR) is a standard for evaluating the quality of an image. On the other hand, the Contrast to Noise Ratio is closely related and somewhat homogeneous to the Signal-to-Noise Ratio model but deducts a term before taking the ratio. Signal-to-Noise Ratio (SNR) places side by side the degree of a chosen signal to the degree of background noise. 

This guide investigates these terms to help you better understand them. So, if you want to learn more, let’s dig in further.


I. What is SNR?

Signal to Noise Ratio (SNR) refers to the measure of the signals of an image in correspondence to the region of its background. A vivid representation of an image’s region of interest is achieved when there’s an accurate SNR value. 

When there’s a drop in the ideal value of SNR, this leads to the image appearing noisy. The whole image consists of tactful volume elements (also known as voxels), and a depletion in the voxel size depletes the signal and consequently reduces SNR. 

The primary obstacle to micro-imaging when compared to macro-imaging stems from the size of its voxels. But then, the alternative high magnetic fields for micro-imaging presents an effortless way to obtain a better Signal Noise Ratio.


II. SNR: Measurement and Image Quality

Quality assessment of component images is considerably related to quality assurance, hence, the importance of image explanation and decision making. Signal-to-noise ratio (SNR) is typically used as a standard for quality, and the consistency of its varying observers has been proven to be of minute knowledge. 

When deriving SNR measurement, it is essential to note that the Signal to Noise Ratio (SNR) is the average image signal in a stated region divided by the noise around that same region.

Therefore, for optimal image quality, accurate derivation of Signal to Noise Ratio (SNR) is paramount for the observers to have the capacity to make consistent decisions about the manufacturing equipment and likewise to ensure excellent quality.


III. What is CNR?

Contrast-to-noise ratio (CNR) evaluation analyzes contrast resolution for clarity purposes in an X-ray image. The CNR presents evaluation conclusions closely related to those of a visual assessment. Consequently, it’s frequent use in the electrical and manufacturing industries for quality inspection and quality assessment and other purposes such as performance comparison of modalities, quantitative evaluation of the detectability of lesions or defects, and image quality control.

In numerous quality inspection imaging, there is usually an exchange between the noise and the image’s mode. Mainly the amount of radiation on the object of interest has to be correct, or the noise disrupts the quality of the image. The finest images, therefore, have a quantifiable amount of noise.


IV. CNR: Measurement and Image Quality

Contrast to noise ratio (CNR) is a metric employed to calculate an image’s quality. It evaluates the contrast between the object of interest and its background. With that in mind, we can therefore proceed to understand how it is measured and calculated for optimal image quality. 

Signal to noise ratio enables you to calculate for contrast to noise ratio. Thus, essential averages are required, and the signal to noise ratio is the average signal divided by the noise. Therefore, the contrast to noise is calculated as the average contrast divided by the noise. 

It is also crucial to note that when given the formulas to evaluate both the signal-to-noise ratio and contrast-to-noise ratio, their ratio is noteworthy in measuring the image quality. The reason is the numerator indicates outright contrast in connection with intensity difference which concerns its background and the object of interest. Also, the denominator represents the image noise indicated in connection with the standard deviation of the background.


V. CNR vs. SNR

So far, it has been quite clear now that these two concepts work hand in hand and interchangeably as one is required for the other to exist. So, it is safe to deduce that they work together towards the same goal of a quality image as the output.

Contrast-to-noise ratio (CNR) is an assessment method deployed to determine image quality, and it is indistinguishable from the metric signal-to-noise ratio (SNR), except that it deducts a term before taking the ratio. Nonetheless, the signal-to-noise ratio (SNR) contrasts the level of a chosen signal to the background noise level. It is also vital to adopt a suitable radiation dose (mA) so that the noise is not too elevated, and the object’s signal can be viewed above the background scope on the detector.




The significant difference between the CNR and the SNR is that the CNR depends on the local contrast, which means that as the CNR is increased, the objects can be seen easily with reference to the background.

Modifying the contrast and the noise in images has some techniques to it as well, and it includes lowering the peak voltage (kVp) as this will increase the contrast. Consequently, this increased contrast reduces the radiation dose and will also increase the noise. So, as the CNR reduces, separating the small signal region from the background is more complicated.



To wrap up, the subject of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) is critical as it concerns the image quality of an X-ray imaging system. The importance of these two elements cannot be overemphasized, seeing that the accuracy of any X-ray quality assessment process depends on the quality of the image. The benefits of these metrics are also numerous, including their help in evaluating the parameters that need adjustments to achieve a suitable setting for the desired image quality.

Ultimately, careful consideration of the CNR and SNR of an X-ray imaging system will be advantageous to you if you desire a high-quality X-ray image and an accurate X-ray inspection result.

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