title: Whole-body direct 4D parametric PET imaging employing nested generalized Patlak expectation-maximization reconstruction.
abstract: Whole-body (WB) dynamic PET has recently demonstrated its potential in translating the quantitative benefits of parametric imaging to the clinic. Post-reconstruction standard Patlak (sPatlak) WB graphical analysis utilizes multi-bed multi-pass PET acquisition to produce quantitative WB images of the tracer influx rate K i as a complimentary metric to the semi-quantitative standardized uptake value (SUV). The resulting K i images may suffer from high noise due to the need for short acquisition frames. Meanwhile, a generalized Patlak (gPatlak) WB post-reconstruction method had been suggested to limit K i bias of sPatlak analysis at regions with non-negligible (18)F-FDG uptake reversibility; however, gPatlak analysis is non-linear and thus can further amplify noise. In the present study, we implemented, within the open-source software for tomographic image reconstruction platform, a clinically adoptable 4D WB reconstruction framework enabling efficient estimation of sPatlak and gPatlak images directly from dynamic multi-bed PET raw data with substantial noise reduction. Furthermore, we employed the optimization transfer methodology to accelerate 4D expectation-maximization (EM) convergence by nesting the fast image-based estimation of Patlak parameters within each iteration cycle of the slower projection-based estimation of dynamic PET images. The novel gPatlak 4D method was initialized from an optimized set of sPatlak ML-EM iterations to facilitate EM convergence. Initially, realistic simulations were conducted utilizing published (18)F-FDG kinetic parameters coupled with the XCAT phantom. Quantitative analyses illustrated enhanced K i target-to-background ratio (TBR) and especially contrast-to-noise ratio (CNR) performance for the 4D versus the indirect methods and static SUV. Furthermore, considerable convergence acceleration was observed for the nested algorithms involving 10-20 sub-iterations. Moreover, systematic reduction in K i % bias and improved TBR were observed for gPatlak versus sPatlak. Finally, validation on clinical WB dynamic data demonstrated the clinical feasibility and superior K i CNR performance for the proposed 4D framework compared to indirect Patlak and SUV imaging.
authors.affiliation: Division of Nuclear Medicine and Molecular Imaging, School of Medicine, University of Geneva, Geneva, CH-1211, Switzerland.
authors.forename: Nicolas A
authors.initials: NA
authors.lastname: Karakatsanis
authors.forename: Michael E
authors.initials: ME
authors.lastname: Casey
authors.forename: Martin A
authors.initials: MA
authors.lastname: Lodge
authors.forename: Arman
authors.initials: A
authors.lastname: Rahmim
authors.forename: Habib
authors.initials: H
authors.lastname: Zaidi
mesh_terms: D000465:Algorithms; D001692:Biological Transport; D019788:Fluorodeoxyglucose F18 / Q000378:metabolism; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D007700:Kinetics; D019047:Phantoms, Imaging; D049268:Positron-Emission Tomography
chemical_list: D019788:Fluorodeoxyglucose F18