Retrospective Data-Driven Motion Correction of 18 F-FDG Brain PET.

A 51-year-old man with severe multifactorial neurocognitive disorders subsequent to delirium, benzodiazepine withdrawal, and preexisting psychiatric illness was referred for 18 F-FDG PET/CT brain imaging in order to rule out an underlying neurodegenerative cause of the symptoms, particularly frontotemporal lobar degeneration. Imaging was impaired by severe motion artifacts, leading to a false-positive result. However, utilizing retrospective data-driven motion correction facilitated a change in diagnosis, ruling out the presence of neurodegenerative disease.

Whole-Body 18 F-FDG PET/CT Patlak Parametric Imaging of Hepatic Alveolar Echinococcosis.

We present dynamic 18 F-FDG PET/CT acquisition in a 52-year-old old woman with histologically proven hepatic alveolar echinococcosis (AE). Metabolic rate of FDG images generated with traditional and relative Patlak analysis show the AE manifestation in the liver significantly better the static SUV images. Dynamic PET may thus have the potential to increase sensitivity in the assessment of hepatic AE manifestations.

Can Dynamic Whole-Body FDG PET Imaging Differentiate between Malignant and Inflammatory Lesions?

Background: Investigation of the clinical feasibility of dynamic whole-body (WB) [18F]FDG PET, including standardized uptake value (SUV), rate of irreversible uptake (Ki), and apparent distribution volume (Vd) in physiologic tissues, and comparison between inflammatory/infectious and cancer lesions. Methods: Twenty-four patients were prospectively included to undergo dynamic WB [18F]FDG PET/CT for clinically indicated re-/staging of oncological diseases. Parametric maps of Ki and Vd were generated using Patlak analysis alongside SUV images.

An Investigation of Stochastic Variance Reduction Algorithms for Relative Difference Penalized 3D PET Image Reconstruction.

Penalised PET image reconstruction algorithms are often accelerated during early iterations with the use of subsets. However, these methods may exhibit limit cycle behaviour at later iterations due to variations between subsets. Desirable converged images can be achieved for a subclass of these algorithms via the implementation of a relaxed step size sequence, but the heuristic selection of parameters will impact the quality of the image sequence and algorithm convergence rates.

Deep learning-based time-of-flight (ToF) image enhancement of non-ToF PET scans.

Purpose: To improve the quantitative accuracy and diagnostic confidence of PET images reconstructed without time-of-flight (ToF) using deep learning models trained for ToF image enhancement (DL-ToF).

Methods: A total of 273 [F]-FDG PET scans were used, including data from 6 centres equipped with GE Discovery MI ToF scanners. PET data were reconstructed using the block-sequential-regularised-expectation-maximisation (BSREM) algorithm with and without ToF.

NEMA NU 2-2018 performance evaluation of a new generation 30-cm axial field-of-view Discovery MI PET/CT.

Purpose: The DMI PET/CT is a modular silicon photomultiplier-based scanner with an axial field-of-view (FOV) between 15 and 25 cm depending on ring configuration (3, 4, or 5 rings). A new generation of the system includes a reengineered detector module, featuring improved electronics and an additional 6th ring, extending the axial FOV to 30 cm. We report on the performance evaluation of the 6-ring upgraded Generation 2 (Gen2) system while values are also reported for the 5-ring configuration of the very same system prior to the upgrade.

BSREM for Brain Metastasis Detection with 18F-FDG-PET/CT in Lung Cancer Patients.

The aim of the study was to analyze the use of block sequential regularized expectation maximization (BSREM) with different β-values for the detection of brain metastases in digital fluorine-18 labeled 2-deoxy-2-fluoro-D-glucose (18F-FDG) PET/CT in lung cancer patients. We retrospectively analyzed staging/restaging 18F-FDG PET/CT scans of 40 consecutive lung cancer patients with new brain metastases, confirmed by MRI. PET images were reconstructed using BSREM (β-values of 100, 200, 300, 400, 500, 600, 700) and OSEM.

Image enhancement of whole-body oncology [F]-FDG PET scans using deep neural networks to reduce noise.

Purpose: To enhance the image quality of oncology [F]-FDG PET scans acquired in shorter times and reconstructed by faster algorithms using deep neural networks.

Methods: List-mode data from 277 [F]-FDG PET/CT scans, from six centres using GE Discovery PET/CT scanners, were split into ¾-, ½- and ¼-duration scans. Full-duration datasets were reconstructed using the convergent block sequential regularised expectation maximisation (BSREM) algorithm.

Impact of PET data driven respiratory motion correction and BSREM reconstruction of Ga-DOTATATE PET/CT for differentiating neuroendocrine tumors (NET) and intrapancreatic accessory spleens (IPAS).

To evaluate whether quantitative PET parameters of motion-corrected Ga-DOTATATE PET/CT can differentiate between intrapancreatic accessory spleens (IPAS) and pancreatic neuroendocrine tumor (pNET). A total of 498 consecutive patients with neuroendocrine tumors (NET) who underwent Ga-DOTATATE PET/CT between March 2017 and July 2019 were retrospectively analyzed. Subjects with accessory spleens (n = 43, thereof 7 IPAS) and pNET (n = 9) were included, resulting in a total of 45 scans.

Assessment of Lesion Detectability in Dynamic Whole-Body PET Imaging Using Compartmental and Patlak Parametric Mapping.

Purpose: Hybrid dynamic imaging allows not only the estimation of whole-body (WB) macroparametric maps but also the estimation of microparameters in the initial bed position targeting the blood pool region containing the pathology owing to the limited axial field of view of PET scanners. In this work, we assessed the capability of multipass WB F-FDG PET parametric imaging in terms of lesion detectability through qualitative and quantitative evaluation of simulation and clinical studies.

Methods: Simulation studies were conducted by generating data incorporating 3 liver and 3 lung lesions produced by 3 noise levels and 20 noise realizations for each noise level to estimate bias and lesion detection features.

Impact of different image reconstructions on PET quantification in non-small cell lung cancer: a comparison of adenocarcinoma and squamous cell carcinoma.

Objective:: Positron emission tomography (PET) using F-fludeoxyglucose (F-FDG) is an established imaging modality for tumor staging in patients with non-small cell lung cancer (NSCLC). There is a growing interest in using F-FDG PET for therapy response assessment in NSCLC which relies on quantitative PET parameters such as standardized uptake values (SUV). Different reconstruction algorithms in PET may affect SUV.

Accelerated time-of-flight (TOF) PET image reconstruction using TOF bin subsetization and TOF weighting matrix pre-computation.

Time-of-flight (TOF) positron emission tomography (PET) technology has recently regained popularity in clinical PET studies for improving image quality and lesion detectability. Using TOF information, the spatial location of annihilation events is confined to a number of image voxels along each line of response, thereby the cross-dependencies of image voxels are reduced, which in turns results in improved signal-to-noise ratio and convergence rate. In this work, we propose a novel approach to further improve the convergence of the expectation maximization (EM)-based TOF PET image reconstruction algorithm through subsetization of emission data over TOF bins as well as azimuthal bins.

Qualitative and Quantitative Evaluation of Blob-Based Time-of-Flight PET Image Reconstruction in Hybrid Brain PET/MR Imaging.

Purpose: Many neurological diseases affect small structures in the brain and, as such, reliable visual evaluation and accurate quantification are required. Recent technological developments made the clinical use of hybrid positron emission tomography/magnetic resonance (PET/MR) systems possible, providing both functional and anatomical information in a single imaging session. Nevertheless, there is a trade-off between spatial resolution and image quality (contrast and noise), which is dictated mainly by the chosen acquisition and reconstruction protocols.

Full field spatially-variant image-based resolution modelling reconstruction for the HRRT.

Accurate characterisation of the scanner's point spread function across the entire field of view (FOV) is crucial in order to account for spatially dependent factors that degrade the resolution of the reconstructed images. The HRRT users' community resolution modelling reconstruction software includes a shift-invariant resolution kernel, which leads to transaxially non-uniform resolution in the reconstructed images. Unlike previous work to date in this field, this work is the first to model the spatially variant resolution across the entire FOV of the HRRT, which is the highest resolution human brain PET scanner in the world.

A 5D computational phantom for pharmacokinetic simulation studies in dynamic emission tomography.

Introduction: Dynamic image acquisition protocols are increasingly used in emission tomography for drug development and clinical research. As such, there is a need for computational phantoms to accurately describe both the spatial and temporal distribution of radiotracers, also accounting for periodic and non-periodic physiological processes occurring during data acquisition.

Methods: A new 5D anthropomorphic digital phantom was developed based on a generic simulation platform, for accurate parametric imaging simulation studies in emission tomography.

Evaluation of a direct 4D reconstruction method using generalised linear least squares for estimating nonlinear micro-parametric maps.

Objective: Estimation of nonlinear micro-parameters is a computationally demanding and fairly challenging process, since it involves the use of rather slow iterative nonlinear fitting algorithms and it often results in very noisy voxel-wise parametric maps. Direct reconstruction algorithms can provide parametric maps with reduced variance, but usually the overall reconstruction is impractically time consuming with common nonlinear fitting algorithms.

Methods: In this work we employed a recently proposed direct parametric image reconstruction algorithm to estimate the parametric maps of all micro-parameters of a two-tissue compartment model, used to describe the kinetics of [[Formula: see text]F]FDG.

Experimental evaluation and basis function optimization of the spatially variant image-space PSF on the Ingenuity PET/MR scanner.

Purpose: The Ingenuity time-of-flight (TF) PET/MR is a recently developed hybrid scanner combining the molecular imaging capabilities of PET with the excellent soft tissue contrast of MRI. It is becoming common practice to characterize the system's point spread function (PSF) and understand its variation under spatial transformations to guide clinical studies and potentially use it within resolution recovery image reconstruction algorithms. Furthermore, due to the system's utilization of overlapping and spherical symmetric Kaiser-Bessel basis functions during image reconstruction, its image space PSF and reconstructed spatial resolution could be affected by the selection of the basis function parameters.

Isotope specific resolution recovery image reconstruction in high resolution PET imaging.

Purpose: Measuring and incorporating a scanner-specific point spread function (PSF) within image reconstruction has been shown to improve spatial resolution in PET. However, due to the short half-life of clinically used isotopes, other long-lived isotopes not used in clinical practice are used to perform the PSF measurements. As such, non-optimal PSF models that do not correspond to those needed for the data to be reconstructed are used within resolution modeling (RM) image reconstruction, usually underestimating the true PSF owing to the difference in positron range.

An ordered-subsets proximal preconditioned gradient algorithm for edge-preserving PET image reconstruction.

Purpose: In iterative positron emission tomography (PET) image reconstruction, the statistical variability of the PET data precorrected for random coincidences or acquired in sufficiently high count rates can be properly approximated by a Gaussian distribution, which can lead to a penalized weighted least-squares (PWLS) cost function. In this study, the authors propose a proximal preconditioned gradient algorithm accelerated with ordered subsets (PPG-OS) for the optimization of the PWLS cost function and develop a framework to incorporate boundary side information into edge-preserving total variation (TV) and Huber regularizations.

Methods: The PPG-OS algorithm is proposed to address two issues encountered in the optimization of PWLS function with edge-preserving regularizers.