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The purpose of this study was to determine the actual standardized uptake value (SUV) by using the lesion size from computer tomography (CT) scan to correct for resolution and partial volume effects in positron emission tomography (PET) imaging. This retrospective study included 47 patients with lung lesions seen on CT scan whose diagnoses were confirmed by biopsy or by follow up CT scan when the PET result was considered negative for malignancy. Each lesion's FDG uptake was quantified by the SUV using two methods: by measuring the maximum voxel SUV (maxSUV) and by using the lesion's size on CT to calculate the actual SUV (corSUV). Among small lesions (2.0 cm or smaller on CT scan), ten were benign and 17 were malignant. The average maxSUV was 1.43+/-0.77 and 3.02+/-1.74 for benign and malignant lesions respectively. When using an SUV of 2.0 as the cutoff to differentiate benignity and malignancy, the sensitivity, specificity, and accuracy were 65%, 70%, and 67% respectively. When an SUV of 2.5 was used for cutoff, the sensitivity, specificity, and accuracy were 47%, 80%, and 59% respectively. The average corSUV was 1.65+/-1.09 and 5.28+/-2.71 for benign and malignant lesions respectively. Whether an SUV of either 2.0 or 2.5 was used for cutoff, the sensitivity, specificity, and accuracy remained 94%, 70%, and 85% respectively. The only malignant lesion that was falsely considered benign with both methods was a bronchioalveolar carcinoma which did not reveal any elevated uptake of fluorine-18 fluorodeoxyglucose (FDG). Of the large lesions (more than 2.0 cm and less than 6.0 cm), one was benign and 19 were malignant and the corSUV technique did not significantly change the accuracy. It is concluded that measuring the SUV by using the CT size to correct for resolution and partial volume effects offers potential value in differentiating malignant from benign lesions in this population. This approach appears to improve the accuracy of FDG-PET for optimal characterization of small lung nodules.

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We describe a new implementation of a single scatter simulation (SSS) algorithm for the prediction and correction of scatter in 3D PET. In this implementation, out of field of view (FoV) scatter and activity, side shields and oblique tilts are explicitly modelled. Comparison of SSS predictions with Monte Carlo simulations and experimental data from uniform, line and cold-bar phantoms showed that the code is accurate for uniform as well as asymmetric objects and can model different energy resolution crystals and low level discriminator (LLD) settings. Absolute quantitation studies show that for most applications, the code provides a better scatter estimate than the tail-fitting scatter correction method currently in use at our institution. Several parameters such as the density of scatter points, the number of scatter distribution sampling points and the axial extent of the FoV were optimized to minimize execution time, with particular emphasis on patient studies. Development and optimization were carried out in the case of GSO-based scanners, which enjoy relatively good energy resolution. SSS estimates for scanners with lower energy resolution may result in different agreement, especially because of a higher fraction of multiple scatter events. The algorithm was applied to a brain phantom as well as to clinical whole-body studies. It proved robust in the case of large patients, where the scatter fraction increases. The execution time, inclusive of interpolation, is typically under 5 min for a whole-body study (axial FoV: 81 cm) of a 100 kg patient.