Normal model construction for statistical image analysis of torso FDG-PET images based on anatomical standardization by CT images from FDG-PET/CT devices

PurposeA better understanding of the standardized uptake value (SUV) ranges of fludeoxyglucose positron emission tomography (FDG-PET) is crucial for radiologists. We have developed a statistical image analysis method for FDG-PET imaging of the torso, based on comparisons with normal data. The purpose of this study was to verify the accuracy of the normal model and usefulness of the statistical image analysis method by using typical cancer cases in the liver, lungs, and abdomen.MethodsOur study and the data collection (49 normal and 34 abnormal cases, in terms of PET/CT findings) were approved by the institutional review board. Our scheme consisted of the following steps: (1) normal model construction, (2) anatomical standardization of patient images, and (3) Z-score calculation to show the results of the statistical image analysis. To validate the Z-score index, we sampled 3603 and 1270 voxels in normal organs and abnormal regions, respectively, from the liver, lungs, and the abdomen. We then obtained the SUV and Z-score for each region. A receiver operating characteristics (ROC) analysis-based method was performed to evaluate the discrimination performances of the SUV and Z-score.ResultsThe discrimination performances of the SUV and Z-score for the objective regions of interest (ROIs) were evaluated by the areas under the ROC curves (AUCs). As a result of the ROC analysis and statistical tests, all AUCs were found to be larger than 0.98. When the ROIs in the objective regions were combined, the mean AUCs of the Z-score and SUV were 0.99 and 0.98, respectively, the difference being statistically significant ($$p < 0.001$$p<0.001).ConclusionsThe results suggested the possibility of applying a quantitative image reading method for torso FDG-PET imaging. Furthermore, a combination of the SUV and Z-score may provide increased accuracy of the determination methods, such as computer-aided detection and diagnosis.

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