Nested Parametric Image Reconstruction using Time-of-Flight PET Histoimages

Due to limited counts in voxel-wise time activity curves, the indirect methods that generate kinetic parametric image from dynamic PET reconstructions often have poor image quality. The TOF PET data can be naturally and efficiently stored in histoimage, and 4D tracer distribution can be efficiently reconstructed using the DIRECT (Direct Image REConstruction for Tof) approaches. We aim to develop efficient dynamic/parametric reconstruction with improved quantitative quality from time-of-flight PET data by taking advantage of its intrinsic kinetic models in our DIRECT frameworks. We have implemented volume of interest (VOI) based and voxel-wise parametric fitting using the linearized Patlak model. We further modified DIRECT reconstructions to support 4D nested reconstruction approach with interleaving tomographic reconstruction and parametric fitting at each iteration. To evaluate proposed dynamic reconstruction approaches, we generate 4D dynamic data sets using the synthetic lesion embedding technique. First, a human subject injected with FDG was scanned on the PennPET Explorer scanner configured with 70 cm axial FOV. Then lung and liver lesions were synthetically embedded using pre-scanned sphere data sets with predetermined time-activity curves. We showed that VOI based method can accurately estimate the Patlak parameters from the frame based reconstructions after corrections. The 4D nested DIRECT reconstruction with Patlak fitting can substantially reduce noise in the reconstructed image frames and provide efficient and feasible tool for direct reconstruction of kinetic parametric image.