Signal-to-noise ratio (SNR, S/N)

Image noise in PET quality assurance phantoms can be measured by acquiring multiple independent images that differ only due to statistical noise. In practise, image noise is often approximated by measuring the standard deviation of pixels within a large uniform region-of-interest (ROI) or between multiple smaller ROIs in a uniform background region (Lodge et al., 2010). Signal-to-noise ratio can then be calculated as the ratio of mean radioactivity concentration inside the ROI and the standard deviation of the pixel (or small ROI) values:

Alternatively, variance and other statistical properties of PET images can be estimated using bootstrap method (Dahlbom et al., 2002). Noise equivalent count (NEC) rates and SNR have a linear relationship (Dahlbom et al., 2005). Since the statistical quality and spatial resolution have an inverse relationship, it is preferable to measure both simultaneously (Lodge et al., 2010).

Lesion SNR

In clinical PET studies the lesion signal-to-noise ratio can be calculated as the difference between the lesion and background compared to the noise (standard deviation) in the background (Lois et al., 2010):

Liver is commonly used as the background tissue.

See also:


Budinger TF, Derenzo SE, Gullberg GT, Greenberg WL, Huesman RH. Emission computer assisted tomography with single-photon and positron annihilation photon emitters. J Comput Assist Tomogr. 1977; 1:131-145. doi: 10.1097/00004728-197701000-00015.

Budinger TF, Derenzo SE, Greenberg WL, Gullberg GT, Huesman RH. Quantitative potentials of dynamic emission computed tomography. J Nucl Med. 1978; 19(3): 309-315. PMID: 632910.

Strother SC, Casey ME, Hoffman EJ. Measuring PET scanner sensitivity: relating countrates to image signal-to-noise ratios using noise equivalent counts. IEEE Trans Nucl Sci. 1990; 37(2): 783-788. doi: 10.1109/23.106715.

Teräs M. Performance and methodological aspects in positron emission tomography. Annales Universitatis Turkuensis, 2008. ISBN: 978-951-29-3739-4.

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Updated at: 2022-10-08
Created at: 2022-10-08
Written by: Vesa Oikonen