Implementation of a Spatially-Variant and Tissue-Dependent Positron Range Correction for PET/CT Imaging - Kertész, Hunor; Beyer, Thomas; Panin, Vladimir; Jentzen, Walter; Cal-Gonzalez, Jacobo; Berger, Alexander; Papp, Laszlo; Kench, Peter L.; Bharkhada, Deepak; Cabello, Jorge; Conti, Maurizio; Rausch, Ivo (Frontiers in Physiology, 2022)
Aim To develop and evaluate a new approach for spatially variant and tissue-dependent positron range (PR) correction (PRC) during the iterative PET image reconstruction. Materials and Methods The PR distributions of three radionuclides (18F, 68Ga, and 124I) were simulated using the GATE (GEANT4) framework in different material compositions (lung, water, and bone). For every radionuclide, the uniform PR kernel was created by mapping the simulated 3D PR point cloud to a 3D matrix with its size defined by the maximum PR in lung (18F) or water (68Ga and 124I) and the PET voxel size. The spatially variant kernels were composed from the uniform PR kernels by analyzing the material composition of the surrounding medium for each voxel before implementation as tissue-dependent, point-spread functions into the iterative image reconstruction. The proposed PRC method was evaluated using the NEMA image quality phantom (18F, 68Ga, and 124I); two unique PR phantoms were scanned and evaluated following OSEM reconstruction with and without PRC using different metrics, such as contrast recovery, contrast-to-noise ratio, image noise and the resolution evaluated in terms of full width at half maximum (FWHM). Results The effect of PRC on 18F-imaging was negligible. In contrast, PRC improved image contrast for the 10-mm sphere of the NEMA image quality phantom filled with 68Ga and 124I by 33 and 24%, respectively. While the effect of PRC was less noticeable for the larger spheres, contrast recovery still improved by 5%. The spatial resolution was improved by 26% for 124I (FWHM of 4.9 vs. 3.7 mm). Conclusion For high energy positron-emitting radionuclides, the proposed PRC method helped recover image contrast with reduced noise levels and with improved spatial resolution. As such, the PRC approach proposed here can help improve the quality of PET data in clinical practice and research.