Direct reconstruction of parametric images for brain PET with event-by-event motion correction: evaluation in two tracers across count levels

Physics in Medicine and Biology
Mary GerminoRichard E Carson

Abstract

Parametric images for dynamic positron emission tomography (PET) are typically generated by an indirect method, i.e. reconstructing a time series of emission images, then fitting a kinetic model to each voxel time activity curve. Alternatively, 'direct reconstruction', incorporates the kinetic model into the reconstruction algorithm itself, directly producing parametric images from projection data. Direct reconstruction has been shown to achieve parametric images with lower standard error than the indirect method. Here, we present direct reconstruction for brain PET using event-by-event motion correction of list-mode data, applied to two tracers. Event-by-event motion correction was implemented for direct reconstruction in the Parametric Motion-compensation OSEM List-mode Algorithm for Resolution-recovery reconstruction. The direct implementation was tested on simulated and human datasets with tracers [11C]AFM (serotonin transporter) and [11C]UCB-J (synaptic density), which follow the 1-tissue compartment model. Rigid head motion was tracked with the Vicra system. Parametric images of K 1 and distribution volume (V T  =  K 1/k 2) were compared to those generated by the indirect method by regional coefficient of variation (CoV)....Continue Reading

References

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Citations

Aug 17, 2019·Frontiers in Neuroscience·Yuankai Zhu, Xiaohua Zhu
Jun 9, 2021·Physics in Medicine and Biology·Andre Z Kyme, Roger R Fulton

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