Sometimes, things are exactly what they seem. This is one of those cases The signal to noise ratio (SNR) is a metric that is used in MRI to describe how much of a signal is noise, and how much is “true” signal. Since “true” signal is what we are interested in and noise is not, the SNR of an image is a reflection of its quality. A large SNR means that our “true signal” is relatively big compared to our noise, and a small SNR means the exact opposite.
How do we change SNR?
- SNR can be used as a metric to assess image quality, but more importantly, we would want to know how to change it. Here are a few tricks to increase SNR:
- average several measurements of the signal, because if noise is random, then random contributions will cancel out to leave the “true” signal
- sample larger volumes (increase field of view or slice thickness)… albeit when you do this… you lose spatial resolution!
- increase the strength of the magnetic field (this is an example of the type (i.e., GET A BIGGER MAGNET!)
How do we measure SNR?
I’ve never done this, but I’ve just read about it. You would want to record signal from a homogeneous region with high signal intensity, and record the standard deviation for the image background (outside of your region). Then calculate:
SNR = Mean Signal/Standard Deviation of Background Noise
When I was more involved with scanning at Duke, we used to have a Quality report generated automatically for each scan that included the SNR. I would guess that most machine setups these days will calculate it automatically for you.
Suggested Citation:
Sochat, Vanessa. "Signal to Noise Ratio (SNR)." @vsoch (blog), 16 Jun 2013, https://vsoch.github.io/2013/signal-to-noise-ratio-snr/ (accessed 28 Nov 24).