Litcius/Paper detail

Monte Carlo Simulation of the Siemens Biograph Vision PET With Extended Axial Field of View Using Sparse Detector Module Rings Configuration

Sara A. Zein, Nicolas A. Karakatsanis, Maurizio Conti, Sadek A. Nehmeh

2020IEEE Transactions on Radiation and Plasma Medical Sciences27 citationsDOIOpen Access PDF

Abstract

We report on the NEMA-NU2-2012 performance of a hypothetical Monte Carlo (MC) model, Ex-PET, of the Siemens Biograph Vision positron emission tomography (PET)/CT (Bio-Vis) with sparse detector module rings and extended axial field of view (AFOV). MC simulations were performed with the detector module rings interleaved with 32-mm gaps, equivalent to the axial dimension of each detector module, yielding an AFOV of 48.0 cm (Bio-Vis has 25.6-cm AFOV). 3D-PET acquisition combined with a limited continuous-bed-motion (limited-CBM) was used to compensate for the loss in sensitivity within the gaps' regions. MC simulations of the Bio-Vis were performed for comparison purposes. All MC simulations were performed using GATE MC toolkit. Ex-PET exhibited 0.49, 0.16, and 0.16 mm deterioration in axial resolution at 1, 10, and 20 cm off-center of the transaxial field of view, respectively, compared to Bio-Vis. Only 1% reduction in system sensitivity and 6% reduction in peak NECR was observed with Ex-PET compared to Bio-Vis. 3D-OSEM image reconstruction, combined with CBM, allowed compensating for the lack of counts within the gaps' regions. NEMA Image Quality test showed <; 6% reduction in contrast recovery with Ex-PET versus Bio-Vis, yet the background variability was increased by up to 8%. The feasibility of PET imaging with an easily adoptable sparse detector configuration was demonstrated. This can lay the pathway for future development of cost-effective PET systems with long and conventional AFOV's.

Topics & Concepts

DetectorMonte Carlo methodPhysicsPositron emission tomographyImage qualitySiemensPet imagingNuclear medicineReduction (mathematics)OpticsComputer scienceArtificial intelligenceMathematicsGeometryMedicineStatisticsImage (mathematics)Quantum mechanicsMedical Imaging Techniques and ApplicationsRadiation Detection and Scintillator TechnologiesAdvanced X-ray and CT Imaging