Title | Whole-body direct 4D parametric PET imaging employing nested generalized Patlak expectation-maximization reconstruction. |
Publication Type | Journal Article |
Year of Publication | 2016 |
Authors | Karakatsanis NA, Casey ME, Lodge MA, Rahmim A, Zaidi H |
Journal | Phys Med Biol |
Volume | 61 |
Issue | 15 |
Pagination | 5456-85 |
Date Published | 2016 08 07 |
ISSN | 1361-6560 |
Keywords | Algorithms, Biological Transport, Fluorodeoxyglucose F18, Humans, Imaging, Three-Dimensional, Kinetics, Phantoms, Imaging, Positron-Emission Tomography |
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. |
DOI | 10.1088/0031-9155/61/15/5456 |
Alternate Journal | Phys Med Biol |
PubMed ID | 27383991 |
PubMed Central ID | PMC5884686 |
Grant List | S10 RR023623 / RR / NCRR NIH HHS / United States |