BackgroundThis study is an assessment of the impact of acquisition times on SUV with [18F]FDG-PET/CT on healthy livers (reference organ with stable uptake over time) and on tumors.MethodsOne hundred six [18F]FDG-PET/CT were acquired in list mode over a single-bed position (livers (n = 48) or on tumors (n = 58)). Six independent datasets of different durations were reconstructed (from 1.5 to 10 min). SUVmax (hottest voxel), SUVpeak (maximum average SUV within a 1-cm3 spherical volume), and SUVaverage were measured within a 3-cm-diameter volume of interest (VOI) in the right lobe of the liver. For [18F]FDG avid tumors (SUVmax ≥ 5), the SUVmax, SUVpeak, and SUV41% (isocontour threshold method) were computed.ResultsFor tumors, SUVpeak values did not vary with acquisition time. SUVmax displayed significant differences between 1.5- and 5–10-min reconstruction times. SUV41% was the most time-dependent parameter. For the liver, the SUVaverage was the sole parameter that did not vary over time.ConclusionsFor [18F]FDG avid tumors, with short acquisition times, i.e., with new generations of PET systems, the SUVpeak may be more robust than the SUVmax. The SUVaverage over a 3-cm-diameter VOI in the right lobe of the liver appears to be a good method for a robust and reproducible assessment of the hepatic metabolism.
Providing efficient and easy-to-use graphical tools to users is a promising challenge of data mining (DM). These tools must be able to generate explicit knowledge and to restitute it. Visualization techniques have shown to be an efficient solution to achieve such goal. Even though considered as a key step in the mining process, the visualization step of association rules received much less attention than that paid to the extraction one. Nevertheless, some graphical tools have been developed to extract and visualize association rules. In those tools, various approaches are proposed to filter the huge number of association rules before the visualization step. However both DM steps (association rule extraction and visualization) are treated separately in a one way process. Our approach differs, and uses meta-knowledge to guide the user during the mining process. Standing at the crossroads of DM and Human-Computer Interaction (HCI), we present an integrated framework covering both steps of the DM process. Furthermore, our approach can easily integrate previous techniques of association rule visualization.
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