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BackgroundAlthough quantitative analysis using standardized uptake value (SUV) becomes realistic in clinical single-photon emission computed tomography/computed tomography (SPECT/CT) imaging, reconstruction parameter settings can deliver different quantitative results among different SPECT/CT systems. This study aims to propose a use of the digital reference object (DRO), which is a National Electrical Manufacturers Association (NEMA) phantom-like object developed by the Quantitative Imaging Biomarker Alliance (QIBA) fluorodeoxyglucose-positron emission tomography technical committee, for the purpose of harmonizing SUVs in Tc-99m SPECT/CT imaging.MethodsThe NEMA body phantom with determined Tc-99m concentration was scanned with the four state-of-the-art SPECT/CT systems. SPECT data were reconstructed using different numbers of the product of subset and iteration numbers (SI) and the width of 3D Gaussian filter (3DGF). The mean (SUVmean), maximal (SUVmax), and peak (SUVpeak) SUVs for six hot spheres (10, 13, 17, 22, 28, and 37 mm) were measured after converting SPECT count into SUV using Becquerel calibration factor. DRO smoothed by 3DGF with a FWHM of 17 mm (DRO17 mm) was generated, and the corresponding SUVs were measured. The reconstruction condition to yield the lowest root mean square error (RMSE) of SUVmeans for all the spheres between DRO17 mm and actual phantom images was determined as the harmonized condition for each SPECT/CT scanner. Then, inter-scanner variability in all quantitative metrics was measured before (i.e., according to the manufacturers’ recommendation or the policies of their own departments) and after harmonization.ResultsRMSE was lowest in the following reconstruction conditions: SI of 100 and 3DGF of 13 mm for Brightview XCT, SI of 160 and 3DGF of 3 pixels for Discovery NM/CT, SI of 60 and 3DGF of 2 pixels for Infinia, and SI of 140 and 3DGF of 15 mm for Symbia. In pre-harmonized conditions, coefficient of variations (COVs) among the SPECT/CT systems were greater than 10% for all quantitative metrics in three of the spheres, SUVmax and SUVmean, in one of the spheres. In contrast, all metrics except SUVmax in the 17-mm sphere yielded less than 10% of COVs after harmonization.ConclusionsOur proposed method clearly reduced inter-scanner variability in SUVs. A digital phantom developed by QIBA would be useful for harmonizing SUVs in multicenter trials using SPECT/CT.

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From the standpoint of host‐parasite interactions, family studies help us understand the host defensive factors and the molecular mechanisms involved in the periodontal immune response. In this study, we report the immunological profile of host‐defensive functions, human leukocyte antigen (HLA) phenotypes, and the microflora of a mother (rapidly progressive periodontitis), an older son (periodontally healthy), a younger son (localized juvenile periodontitis), and a daughter (localized juvenile periodontitis). We examined the peripheral neutrophil functions, phenotypic and functional analysis of peripheral lymphocytes, serum immunoglobulin G (IgG) antibody titers against periodontopathic bacteria, serological type of HLA class II antigens, and bacterial flora in all periodontal pockets. The results showed that Actinobacillus actinomycetemcomitans was dominant in the pockets of all subjects. The mother and two sons showed a depressed neutrophil chemotaxis to N‐formyl‐methionyl‐leucyl‐phenylalanine. All subjects except the older son exhibited low T4/T8 ratios. The mother and daughter had raised levels of IgG titers to Porphyromonas gingivalis. All subjects had HLA phenotypes of DRw52 and DQ1 in common. We found that the family members had similar disorders in certain defensive functions. This family has been a model for our understanding of the host defensive factors in the development of early‐onset periodontitis. J Periodontol 1996;67:254–263.

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