**Image acquisition**

A NEMA NU2–2007 image quality phantom was imaged on a GEMINI TF 64/TOF Performance 2010 (Philips Medical Systems International B.V.) according to the requirements for the EANM/EARL FDG-PET/CT accreditation [7]. The phantom was composed by a fillable torso compartment acting as background, by a cylindrical insert in the centre of the torso compartment and by 6 fillable spheres of different diameters (10 mm, 13 mm, 17 mm, 22 mm, 28 mm and 37 mm) placed around the central insert. The fillable torso compartment and the spheres have been filled with a solution of water and F18-FDG. At the starting moment of the scan the activity concentration was 2,10 MBq/ml in the torso background compartment and 20,04 MBq/ml in the spheres, resulting in a sphere to background ratio of 9,6:1 (aim is 10:1). [8]

The original dataset was acquired with 150 s frame duration. The total acquisition time was 10 minutes. An attenuation corrected reconstruction was performed at different reconstruction lengths, varying from 4 s to 30 s, generating as many images as possible per subset, without using the same coincidences by varying the starting time of the reconstruction. For example, for the first subset (4 s reconstruction length), the first image was reconstructed using the coincidences recorded between 0 and 4 seconds, the second image by using the coincidences recorded between 5 and 8 seconds and so on, varying the starting moment of the reconstruction, generating a total of 37 images. The longest frame length was 30 s, generating a subset of 5 images. A total of 14 subsets was generated, of respectively 4s, 6s, 8s, 10s, 12s, 15s, 17s, 19s, 20s, 22s, 24s 26s, 28s and 30s acquisition length.

The Philips reconstruction software automatically corrected each reconstruction for the decay of F18 (half-life of 109.7 minutes [9]), compensating the time difference between the start of the study and the start of the reconstruction by using an opportune scaling factor.

**Image analysis**

The datasets were analysed using a Python 3.7.0 script (default, Jun 28 2018, 08:04:48) [MSC v.1912 64 bit (AMD64)]. Different SUV parameters were calculated in each image of the subsets:

- the maximum in the central 2D plane of each sphere, defined as SUVMax 2D;
- the maximum of each 3D sphere, defined as SUVMax 3D;
- the average value in the central 2D plane of each sphere, defined as SUVMean 2D;
- the average value of each 3D sphere, defined as SUVMean 3D;
- the average value within a 1 cm
3 sphere centred in the maximum value of the sphere [10], defined as SUVPeak.

The SUVMean was calculated by using a ROI of dimensions according to the specifications of the diameters of the spheres, without using for example a thresholding technique on pixel values or a percentage of the maximum value.

The SUV values were calculated per each image in a subset. The SUV values population has been tested for normality with a Kolmogorov-Smirnov test and all subsets matched the characteristics of a normal distribution. We report data until reconstruction length 30 s because the subsequent subset (32 s), composed by 4 images, did not test for normality with a Kolmogorov-Smirnov test. The SUV parameters of the different images in a subset were averaged and their standard deviation was calculated. The variation of the SUV parameters was calculated as the standard deviation divided by their average value multiplied by 100.

In our measurements we can assume a random sampling model, with no correlations, for independent and identically distributed random measurements. The different subsets do not differ in activity nor voxel dimensions and the quantification of the SUV parameters has been done by using the same ROI dimension. We can therefore describe the ratio of the standard deviations SD of two independent repetitions of PET measurements as:

With SD1 and SD2 being the standard deviations and RL1 and RL2 being the reconstruction lengths. [5].

By using the measured variation of the SUV values in a subset as SD1 and the length of the reconstruction of the specific subset as RL1, it is possible to estimate the variation SD2 between repetitions of scans at the total acquisition time RL2=150 s.

Since we divided our acquisition into 14 different subsets, we could calculate 14 different estimations of the SD2, using the described method above. We validated our method by testing whether the value of the estimated SD2 was independent of the acquisition length of the images in the subset.