Plant-Specific Modular PET: Data Processing with CASToR and Performance Evaluation

A modular PET detector system with variable geometry, BioPET, has been constructed to specifically study plant-microorganism-environment complexes. While the modular design in combination with rotation and translation mechanics provides greater flexibility in detector arrangement, adaptive data processing and image reconstruction methods are needed for such a transformable system. In this work, we developed a data processing pipeline by making used of a generic, modular, and extensible platform for tomography reconstruction, CASToR, and applied the process to evaluate performance of BioPET in rotational and stationary modes. The NEMA NU 4-2008 image quality phantom was utilized for analysis of recovery coefficients, spillover ratios, uniformity, and their relationship to selected reconstruction parameters. The rotational mode outperforms the stationary mode in exhibiting lower spillover effect as well as in reconstructing the activity concentration of smaller features, although partial volume effect is evidenced in both modes. The behaviour of the performance indices as a function of the selected reconstruction parameters is found specific to each detection mode. The procedure and the image quality characterization presented in this work are driving the development and optimization of data processing, image reconstruction and correction framework towards quantitative image analysis for PET systems with flexible geometry.