Reliability of predicting image signal‐to‐noise ratio using noise equivalent count rate in PET imaging
Authors: Chang Tingting , Chang Guoping , Clark John W. , Diab Rami H. , Rohren Eric , Mawlawi Osama R. 
Published: September 2012

Purpose:

Several investigators have shown that noise equivalent count rate (NECR) is linearly proportional to the square of image signal‐to‐noise ratio (SNR) when PET images are reconstructed using filtered back‐projection. However, to our knowledge, none have shown a similar relationship in fully 3D ordered‐subset expectation maximization (OSEM) reconstruction. This paper has two aims. The first is to investigate the NECR‐SNR relationship for 3D‐OSEM reconstruction using phantom studies while the second aim is to evaluate the NECR‐SNR relationship using patient data.

Methods:

An anthropomorphic phantom was scanned on a GE Discovery‐STE (DSTE) PET/CT scanner in 3D mode with an initial activity concentration of 66.34 kBq/cc. PET data were acquired over the lower chest/upper abdomen region in dynamic mode. The experiment was repeated with the same activity concentration on a GE Discovery‐RX (DRX) scanner. Care was taken to place the phantom at identical positions in both scanners. PET data were then reconstructed using 3D Reprojection (3D‐RP) and 3D‐OSEM with different reconstruction parameters and the NECR and SNR for each frame/image were calculated. SNR2 was then plotted versus the NECR for each scanner, reconstruction method and parameters. In addition, 40 clinical PET/CT studies from the two scanners (20 patients/scanner) were evaluated retrospectively. The patient studies from each scanner were further divided into two subgroups of body mass indices (BMI). Each PET study was acquired in 3D mode and reconstructed using both 3D‐OSEM and 3D‐RP. The NECR and SNR of the bed position covering the patient liver were calculated for each patient and averaged for each subgroup. Comparisons of the NECR and SNR between scanner types and BMIs were performed using a t‐test and a p value less than 0.05 was considered significant.

Results:

Phantom results showed that SNR2 versus NECR was linear for 3D‐RP reconstruction across all activity concentration on both scanners, as expected. However, when 3D‐OSEM was used, this relationship was nonlinear at activity concentrations beyond the peak NECR on both scanners. On the other hand, the plot of SNR2 versus trues count rate was linear for 3D‐OSEM across all activity concentrations on both scanners independent of reconstruction parameters used. In addition, for activity concentrations <30kBq/cc, phantom results showed a higher SNR (by 12 ± 10%; p < 0.05) and NECR for the DRX scanner compared to DSTE for 3D‐RP reconstruction. However, for 3D‐OSEM reconstruction, these two scanners had similar SNRs (different by 2% ± 9%; p > 0.05), despite having different NECRs. Patient studies showed a statistically significant difference in NECR as well as the SNR for 3D‐RP reconstruction between the two scanners. However, no statistically significant difference was found for 3D‐OSEM. A statistically significant difference in both NECR and SNR were found between the different BMI subgroups for both 3D‐RP and 3D‐OSEM reconstructions.

Conclusions:

For the scanners and reconstruction algorithm used in this study, our results suggest that the image SNR cannot be predicted by the NEC when using 3D‐OSEM reconstruction particularly for those clinical applications requiring high activity concentration. Instead, our results suggest that image SNR varies with activity concentration and is dominated by the 3D‐OSEM reconstruction algorithm and its associated parameters, while not being affected by the scanner type for the range of activity concentrations usually found in the clinic.