The Contrast to Noise Ratio (CNR) in a medical image is a measure of the contrast between the tissue of interest and the background (i.e. the neighboring tissue). The Signal to Noise Ratio (SNR) is a measure of the image signal in a given region to the background. The ability to visualize objects in a noisy background is dependent on the size of the objects and the contrast of the objects. In this article we will cover the basics of Contrast to Noise for you as a Radiographer / Radiologic Technologist.

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Signal to Noise Ratio (SNR) and Contrast to Noise Ratio (CNR)
In all diagnostic imaging there is a trade-off between noise and image acquisition parameters. In x-ray and CT imaging the trade-off is the radiation dose the patient receives, while in MRI it is scan time.

Therefore, the optimal images will always have a reasonable amount of noise (otherwise the scan is acquired at a radiation dose that is too high or in the case of MR the scan time is too long).

A very common clinical task in x-ray or CT is to determine if there is something undesirable in the image such as a tumor or another type of lesion. In this case we call the tumor the image signal of interest and the task is differentiating it from the background tissue.

In this section we will discuss the concepts of Signal to Noise Ratio (SNR) and Contrast to Noise Ratio (CNR). These measures help us understand how well the diagnostic task can be performed, even when noise is present in the images.

Quantum Noise (a.k.a. Quantum Mottle)
In x-ray and CT images the source of noise in the images is referred to as quantum noise or quantum mottle. This is due to the photon counting statistics, i.e. when more photons are counted in the detector the images will be less noisy. However, in order to get more photons counted in the detector the x-ray radiation dose will be higher.

Moving from upper left to lower right the quantum noise is increased making it more difficult to visualize details.
Therefore, for each specific imaging task it is important to select the appropriate radiation dose such that the noise is not too high so that the signal of interest can be visualized above background tissue.

Here is an example of an image with increasing levels of quantum noise. In the upper left there are no noise fluctuations and then higher levels of noise are present until the lower right which has the highest level of quantum noise.

From this small example you can see that even at the higher noise cases it is still possible to differentiate many things within the image. If the clinical task was to tell if there are two eyes a nose and a mouth then even the noisiest image would be acceptable for this task.

However, if the task is to draw a border of the outline of the head including the hair then there is too much noise in the high noise images.

Rad Take-home Point: Image noise increases in x-ray and CT imaging when the radiation dose is lowered. The required radiation dose is strongly dependent on the clinical task for the given exam.

If we have different disks on the image, ability to see them depends on how big and bright they are. In this example we have disks with three different signal levels that are low, med and higher contrast levels.

Depending on the clinical task the signal in the image will have a different level above the background tissue.


The signal to noise ratio (SNR) is simply the average image signal in a given region divided by the noise around that region. This can be a useful first measurement but the more important quantity typically is the contrast to noise ratio (CNR), which is simply the ratio of the contrast between the signal in a given region and the background.

So how do we make these measurements in practice. We typically place what is called a Region of Interest (ROI) on the image. This ROI will select all the image pixels inside of the selected region.

Then the image viewing software will typically report at least a couple key measurements inside of each ROI. The average image signal in that ROI and the standard deviation of noise in that ROI.

With the capability to measure both the signal level and the noise level in given ROIs we can calculate the SNR and CNR for a given acquisition.

We give a small example here to demonstrate these concepts.

For instance, if have an average signal strength of 150 and standard deviation of the noise of 10 what will the SNR be? SNR=150/10=15

For instance, if we then measure the average in the background ROI around the signal ROI to be 100 what is the CNR? CNR=(150-100)/10=5

We can see that the CNR is different from the SNR and that the CNR is very dependent on the local contrast. As the CNR is increased the objects will be more easily visualized with respect to the background.

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