Scatter Correction Based on GPU-Accelerated Full Monte Carlo Simulation for Brain PET/MRI

Magnetic Resonance-Based Attenuation Correction and Scatter Correction in Neurological Positron Emission Tomography/Magnetic Resonance Imaging—Current Status With Emerging Applications

“…While there are differences in implementation of how single Compton scattered events are modeled between these two approaches, some common principles can be identified. These are as follows: (1) scatter is due to single Compton scatter events, (2) single scatter distribution can be calculated by application of the Klein-Nishina formula using the known emitter density and attenuation coefficients from emission and attenuation sinogram data, and (3) the derived scatter estimate can be scaled to the emission data tails for subtraction (tail fitting) and to account [146], and Ollinger [147] in comparison to Monte Carlo simulation-based scatter correction shown in Kim et al [148], Magota et al [149], and Ma et al [150].…”

“…Recently, it was shown that improved scatter correction is helpful for increasing the visual and quantitative accuracy of PET and could also result in improved attenuation correction with data-driven methodologies using PET and MRI [45]. Furthermore, accurate scatter correction methods could be useful in improving the quantitative accuracy of dynamic PET data with low count statistics [150], which is often the case in neuroimaging research. While studies in the head region do not suffer from the same effects from, e.g., truncation or large bladder-to-background ratio as PET/MR studies in the body region, accurate methodologies developed for the head region might eventually become useful for whole-body PET/MR as well.…”

“…Doing a full MC simulation combined with a GPU implementation could offer a feasible way to implement a very accurate method for scatter correction [166]. Recently, a method based on the paper of Gaens et al [166] was further refined and applied in a phantom and patient study using a brain PET/MR imaging system [150], with promising initial results. MC simulation has been also applied to implement more robust scaling of the scatter sinograms in the presence of high activity in [ 15 O]-inhalation studies [149].…”

SimPET—An open online platform for the Monte Carlo simulation of realistic brain PET data. Validation for 18F‐FDG scans

“…17 The simulated data can then be reconstructed to generate PET images, which can be used to validate the quantification methods using the original digital phantoms as ground truth. [18][19][20][21][22][23][24] For this, different brain digital phantoms such as the Zubal, 25 the XCAT brain, 26 the BigBrain atlas, 27 and the digital Hoffman 28 are available, but these have, in general, similar limitations in terms of changing shapes and volumes similar to those of the physical phantoms. These limitations can be overcome by deriving the synthetic phantoms from patient data, 29,30 allowing the generation of large numbers of different phantoms incorporating voxel-wise physiological variability.…”

“…Several toolkits exist for MC simulation, such as the Geant4 Application for Tomographic Emission (GATE), 15 Simulation System for Emission Tomography (SimSET), 16 or PeneloPET 17 . The simulated data can then be reconstructed to generate PET images, which can be used to validate the quantification methods using the original digital phantoms as ground truth 18–24 . For this, different brain digital phantoms such as the Zubal, 25 the XCAT brain, 26 the BigBrain atlas, 27 and the digital Hoffman 28 are available, but these have, in general, similar limitations in terms of changing shapes and volumes similar to those of the physical phantoms.…”