Abstracts of the 33rd International Austrian Winter Symposium

K. Binzel1, A. Adelaja1, C. L. Wright1, D. Scharre2, J. Zhang1, M. V. Knopp11Wright Center of Innovation in Biomedical Imaging, Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH, USA; 2Department of Neurology, The Ohio State University Wexner Medical Center, Columbus, OH, USACorrespondence: K. Binzel Aim: Digital photon counting allows for higher definition image reconstruction than on conventional PET systems due to increased system sensitivity, time of flight timing resolution, and count densities (1). For neurologic applications, these improvements may greatly increase the utility and accuracy of PET imaging. We validated and optimized high definition image reconstruction methodologies using the Hoffman brain phantom. Methods: A Hoffman brain phantom was filled with 52 MBq 18F-FDG and imaged on a pre-commercial release digital photon counting PET/CT (Philips Vereos). Five minute acquisitions were repeated over several runs using the dedicated brain 256 mm field of view (FOV) and the whole body 576 mm FOV. Listmode data from each acquisition was reconstructed using the high definition (HD) 2x2x2 mm voxel matrix. Reconstructions were performed with 3 iterations and a range of subsets, 21, 17, 13, and 9. Additionally, the use of system point spread function (PSF) correction and/or a Gaussian filter were enabled. Regions of interest (ROIs) were placed in 10 unique regions of the phantom for quantitative assessment. Blinded reader reviews were performed to assess image quality. Results: We found that the optimal reconstruction settings for each FOV were distinct. For the 576 mm FOV the use of PSF alone, no Gaussian filter, gave the most accurate quantitative results. The addition of the Gaussian filter resulted in underestimation of activity concentrations. The average recovery coefficients (RCs) of all ROIs were very similar among reconstructions with different numbers of subsets. The average RCs were 0.97, 0.95, 0.94, and 0.92 for the 21, 17, 13, and 9 subsets reconstructions, respectively. Blinded review conveyed that the 13 subset images were most preferable with regard to contrast and image noise. Thus the HD reconstruction with PSF only using 3 iterations and 13 subsets was optimal for the whole body FOV acquisitions. For the 256 mm FOV a Gaussian filter was used in reconstruction as the PSF alone lead to overestimation of activity concentrations. As with the whole body FOV, each subset setting gave similar quantitative results, average RCs were 1.02, 1.01, 0.98, and 0.95 for the 21, 17, 13, and 9 subset reconstructions. Image review again showed that fewer subsets were preferred, with the 9 subset reconstruction now being most ideal. For acquisitions with the dedicated brain FOV, HD reconstruction with both PSF correction and a Gaussian filter using 3 iterations with 9 subsets is optimal for imaging with FDG. Conclusion: Optimization of Neuro-PET reconstruction settings revealed that these setting must be tailored to acquisition characteristics, namely the chosen field of view. Phantom validation demonstrated that high quantitative accuracy and excellent image quality is readily achieved for neurologic imaging on next-generation digital PET systems when using either a dedicated brain field of view or the wider whole body field of view. Reference 1. Wright CL, Binzel K, Zhang J, Knopp MV. Advanced Functional Tumor Imaging and Precision Nuclear Medicine Enabled by Digital PET Technologies. Contrast Media & Molecular Imaging. 2017;2017:5260305. https://doi.org/10.1155/2017/5260305.