Axial-dependent SUV variance with and without knitting: implication for automated reconstruction optimization

1777 Objectives: we are developing an expert system to optimize reconstruction parameters to improve lesion standardized uptake value (SUV) quantification in clinical PET studies. Specifically, the expert system can compute the patient- and lesion-specific SUV correction and uncertainty (SUV variance) using a lesion-embedding and bootstrapping (LEB) techniques for automated reconstruction optimization. The clinical whole-body PET images are often obtained by knitting images reconstructed from multiple step-and-shot bed acquisitions. We would like to estimate the SUV variance of the lesion in the knitted image based on the variance estimated using LEB for a single-bed acquisition, since separately acquired embeddable lesions cannot practically be embedded over multiple bed positions. Methods: We performed Monte-Carlo simulation based on a generic PET scanner (76.4 cm diameter and 16.4 cm axial) comprised of 18 modules. Each module is assembled with 5 (axial) x 4 (transverse) tiles; each tile has 8 x 8 crystals of 4 x 4 x 19 mm3 with crystal pitch of 4.1 mm. We used a uniform 27 cm (diameter) x 24 cm (length) cylinder phantom, and inserted different sizes (10, 13, 17, 22, 28, and 37 mm) of hot spheres with concentration ratio of 5. We generated data for two bed positions with 50% axial overlap (82 mm) without TOF information, and the hot spheres were axially placed at 10 different locations uniformly sampled from the center to a quarter of the scanner’s axial length after considering the symmetry of the two bed positions. For each sphere location and bed position, we generated 30 noise replicates with 30M counts in each list data. A 3D list-mode OSEM algorithm with generalized second-order Kaiser-Bessel basis function (blob) was used for all reconstructions. The blob has a 5-mm radius placed on a 4-mm body centered cubic grid with shape parameter of 8.63. The reconstructed blob images were then resampled into voxel images with voxel sizes of 2 x 2 x 2 mm3. All reconstructions had 25 chronologically ordered subsets, and 10 iterations were used to ensure convergence. The whole images were then knitted from the two frames of images weighted by the triangular axial sensitivity profiles. We computed the Normalized Uptake Value (NUV) for each size of hot sphere for each image. The NUV, closely related to SUV, is the mean uptake in the sphere normalized by the average background uptake. The standard deviation of the NUV of each hot sphere was computed using the 30 noise replicates. Results: The variance of NUV of the frame image increases with axial offset from the center of the PET scanner. The variance of NUV of the knitted image is independent with the axial distance. For hot spheres at the center of the PET scanner, the variance of NUV of the single frame image is the same as the variance of NUV of the knitted image. The average NUV variance ratio between the single frame image and the knitted image follows 1/(1-2z/L), where z is axial offset and L is the scanner’s axial length. For hot spheres offset a quarter of axial length, the average variance ratio is 1.95, and the variance ratio is independent of lesion sizes. Conclusion: The SUV uncertainty/variance of a specific lesion in the knitted whole-body image can be determined from the SUV variance of a single frame image that contains the lesion. The SUV variance of a single frame image can be determined using bootstrapping of the single frame list data. The lesion-specific corrected SUVs and estimated variance computed from the expert system can improve the lesion quantification, provide a measure of SUV uncertainty for clinicians, and have implication in patient-centered PET imaging.