Iterative reconstruction has become the standard for routine clinical positron emission tomography (PET) imaging. However, iterative reconstruction is computationally expensive, especially for time-of-flight (TOF) data. Block-iterative algorithms such as ordered-subsets expectation-maximization (OSEM) are commonly used to accelerate the reconstruction. There is a tradeoff between the number of subsets and reconstructed image quality. The objective of this work was to evaluate the effect of varying the number of OSEM subsets upon lesion-detection for general oncologic PET imaging. Methods Experimental phantom data were taken from the Utah PET Lesion Detection Database resource, modeling whole-body oncologic PET imaging of a 92 Kg patient with [18]F-fluorodeoxyglucose. The experiment consisted of 24 scans over 4 days on a TOF PET/CT scanner, with up to 23 lesions (diameter 6–16mm) distributed throughout the thorax, abdomen, and pelvis. Images were reconstructed with maximum-likelihood expectation-maximization (MLEM) and with OSEM using 2–84 subsets. The reconstructions were repeated both with and without TOF. Localization receiver operating characteristics (LROC) analysis was applied using the channelized non-prewhitened observer. The observer was first used to optimize the number of iterations and smoothing filter for each case that maximized lesion-detection performance for these data; this was done to ensure that fair comparisons were made with each test case operating near its optimal performance. The probability of correct localization (PLOC) and the area under the LROC curve (ALROC) were then analyzed as functions of the number of subsets to characterize the effect of OSEM on lesion-detection performance. Results Compared to the baseline MLEM algorithm, lesion-detection performance with OSEM declined as the number of subsets increased. The decline was moderate out to about 12–14 subsets, and then became progressively steeper as the number of subsets increased. Comparing TOF with non-TOF results, the magnitude of the performance drop was larger for TOF reconstructions. Conclusion PET lesion-detection performance is degraded when using OSEM with a large number of subsets. This loss of image quality can be controlled by using a moderate number of subsets (e.g. 12–14 or fewer), retaining a large degree of acceleration while maintaining high image quality. The use of more aggressive subsetting can result in image quality degradations that offset the benefits of using TOF or longer scan times.

Positron emission tomography (PET) images are typically reconstructed with an in-plane pixel size of approximately 4mm for cancer imaging. The objective of this work was to evaluate the effect of using smaller pixels on general oncologic lesion-detection. A series of observer studies was performed using experimental phantom data from the Utah PET Lesion Detection Database, which modeled whole-body FDG PET cancer imaging of a 92kg patient. The data comprised 24 scans over 4 days on a Biograph mCT time-of-flight (TOF) PET/CT scanner, with up to 23 lesions (diam. 6–16mm) distributed throughout the phantom each day. Images were reconstructed with 2.036mm and 4.073mm pixels using ordered-subsets expectation-maximization (OSEM) both with and without point spread function (PSF) modeling and TOF. Detection performance was assessed using the channelized non-prewhitened numerical observer with localization receiver operating characteristic (LROC) analysis. Tumor localization performance and the area under the LROC curve were then analyzed as functions of the pixel size. In all cases, the images with ~2mm pixels provided higher detection performance than those with ~4mm pixels. The degree of improvement from the smaller pixels was larger than that offered by PSF modeling for these data, and provided roughly half the benefit of using TOF. Key results were confirmed by two human observers, who read subsets of the test data. This study suggests that a significant improvement in tumor detection performance for PET can be attained by using smaller voxel sizes than commonly used at many centers. The primary drawback is a 4-fold increase in reconstruction time and data storage requirements.

Multi-tracer positron emission tomography (PET) can image two or more tracers in a single scan, characterizing multiple aspects of biological functions to provide new insights into many diseases. The technique uses dynamic imaging, resulting in time-activity curves that contain contributions from each tracer present. The process of separating and recovering separate images and/or imaging measures for each tracer requires the application of kinetic constraints, which are most commonly applied by fitting parallel compartment models for all tracers. Such multi-tracer compartment modeling presents challenging nonlinear fits in multiple dimensions. This work extends separable parameter space kinetic modeling techniques, previously developed for fitting single-tracer compartment models, to fitting multi-tracer compartment models. The multi-tracer compartment model solution equations were reformulated to maximally separate the linear and nonlinear aspects of the fitting problem, and separable least-squares techniques were applied to effectively reduce the dimensionality of the nonlinear fit. The benefits of the approach are then explored through a number of illustrative examples, including characterization of separable parameter space multi-tracer objective functions and demonstration of exhaustive search fits which guarantee the true global minimum to within arbitrary search precision. Iterative gradient-descent algorithms using Levenberg–Marquardt were also tested, demonstrating improved fitting speed and robustness as compared to corresponding fits using conventional model formulations. The proposed technique overcomes many of the challenges in fitting simultaneous multi-tracer PET compartment models.