title: Quantitative PET image reconstruction employing nested expectation-maximization deconvolution for motion compensation.
abstract: Bulk body motion may randomly occur during PET acquisitions introducing blurring, attenuation-emission mismatches and, in dynamic PET, discontinuities in the measured time activity curves between consecutive frames. Meanwhile, dynamic PET scans are longer, thus increasing the probability of bulk motion. In this study, we propose a streamlined 3D PET motion-compensated image reconstruction (3D-MCIR) framework, capable of robustly deconvolving intra-frame motion from a static or dynamic 3D sinogram. The presented 3D-MCIR methods need not partition the data into multiple gates, such as 4D MCIR algorithms, or access list-mode (LM) data, such as LM MCIR methods, both associated with increased computation or memory resources. The proposed algorithms can support compensation for any periodic and non-periodic motion, such as cardio-respiratory or bulk motion, the latter including rolling, twisting or drifting. Inspired from the widely adopted point-spread function (PSF) deconvolution 3D PET reconstruction techniques, here we introduce an image-based 3D generalized motion deconvolution method within the standard 3D maximum-likelihood expectation-maximization (ML-EM) reconstruction framework. In particular, we initially integrate a motion blurring kernel, accounting for every tracked motion within a frame, as an additional MLEM modeling component in the image space (integrated 3D-MCIR). Subsequently, we replaced the integrated model component with a nested iterative Richardson-Lucy (RL) image-based deconvolution method to accelerate the MLEM algorithm convergence rate (RL-3D-MCIR). The final method was evaluated with realistic simulations of whole-body dynamic PET data employing the XCAT phantom and real human bulk motion profiles, the latter estimated from volunteer dynamic MRI scans. In addition, metabolic uptake rate Kiparametric images were generated with the standard Patlak method. Our results demonstrate significant improvement in contrast-to-noise ratio (CNR) and noise-bias performance in both dynamic and parametric images. The proposed nested RL-3D-MCIR method is implemented on the Software for Tomographic Image Reconstruction (STIR) open-source platform and is scheduled for public release.
authors.affiliation: Geneva University Hospital, Division of Nuclear Medicine and Molecular Imaging, Geneva, CH-1211, Switzerland; Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. Electronic address: nikolaos.karakatsanis@unige.ch.
authors.forename: Nicolas A
authors.initials: NA
authors.lastname: Karakatsanis
authors.affiliation: University of Leeds, Division of Biomedical Imaging, 6.36 LIGHT labs, Leeds, LS2 9JT, United Kingdom; Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
authors.forename: Charalampos
authors.initials: C
authors.lastname: Tsoumpas
authors.affiliation: Geneva University Hospital, Division of Nuclear Medicine and Molecular Imaging, Geneva, CH-1211, Switzerland; University of Geneva, Neuroscience Center, Geneva, CH-1211, Switzerland; University of Groningen, University Medical Center of Groningen, Department of Nuclear Medicine and Molecular Imaging, Groningen, 9700 RB, Netherlands.
authors.forename: Habib
authors.initials: H
authors.lastname: Zaidi
mesh_terms: D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional / Q000379:methods; D009038:Motion; D019047:Phantoms, Imaging; D049268:Positron-Emission Tomography / Q000379:methods*
keywords: Deblurring; Motion compensation; PET; Reconstruction; Richardson-Lucy deconvolution