Some of the parameters used for the quantification of PET images are the Standardized Uptake Value (SUV)Max, SUVMean and SUVPeak. In order to assess the significance of an increasing or decreasing of these parameters for diagnostic purpose it is relevant to determine their standard deviation. In this study we present a method to determine the standard deviation of the SUV.

Our method is based on dividing an original dataset into subsets of shorter time length. The variation between the SUV parameters of the subsets is used to estimate the standard deviation of the of the original acquisition. This method was tested on images of a NEMA quality phantom with acquisition time of 150 s per bed position and foreground to background activity ratio of 10:1. This original dataset has been reconstructed with different reconstruction lengths, generating new data subsets. The SUVMax, Mean and Peak were calculated for each image in the subsets. Their standard deviation has been calculated per subset for the different spheres included in the phantom. The variation of each subset has then been used to estimate the expected variation between images at 150 s reconstruction length.

We report the largest standard deviation of the SUV parameters for the smallest sphere, and the smallest variation for the largest sphere. The expected variation at 150 s reconstruction length does not exceed 6% for the smallest sphere and 2% for the largest sphere. We also report a larger variation in SUVMax then in SUVMean and SUVPeak. This is in line with expectations that the standard deviation of the SUV Mean or SUVPeak parameter is lower, since the value of more voxels is included in the calculation, as opposed to the SUVMax, where a single voxel is decisive. With the presented method we are able to determine the standard deviation of SUV parameters and to evaluate the effect of parameter selection and lesion size on the standard deviation, and therefore to evaluate its relevance on the total variation of the SUV value between studies.

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Posted 29 Apr, 2020

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Posted 29 Apr, 2020

###### No community comments so far

Some of the parameters used for the quantification of PET images are the Standardized Uptake Value (SUV)Max, SUVMean and SUVPeak. In order to assess the significance of an increasing or decreasing of these parameters for diagnostic purpose it is relevant to determine their standard deviation. In this study we present a method to determine the standard deviation of the SUV.

Our method is based on dividing an original dataset into subsets of shorter time length. The variation between the SUV parameters of the subsets is used to estimate the standard deviation of the of the original acquisition. This method was tested on images of a NEMA quality phantom with acquisition time of 150 s per bed position and foreground to background activity ratio of 10:1. This original dataset has been reconstructed with different reconstruction lengths, generating new data subsets. The SUVMax, Mean and Peak were calculated for each image in the subsets. Their standard deviation has been calculated per subset for the different spheres included in the phantom. The variation of each subset has then been used to estimate the expected variation between images at 150 s reconstruction length.

We report the largest standard deviation of the SUV parameters for the smallest sphere, and the smallest variation for the largest sphere. The expected variation at 150 s reconstruction length does not exceed 6% for the smallest sphere and 2% for the largest sphere. We also report a larger variation in SUVMax then in SUVMean and SUVPeak. This is in line with expectations that the standard deviation of the SUV Mean or SUVPeak parameter is lower, since the value of more voxels is included in the calculation, as opposed to the SUVMax, where a single voxel is decisive. With the presented method we are able to determine the standard deviation of SUV parameters and to evaluate the effect of parameter selection and lesion size on the standard deviation, and therefore to evaluate its relevance on the total variation of the SUV value between studies.

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Figure 12

Figure 13

Figure 14

Figure 15

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