• Journal of radiological science and technology
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• v.29 no.4
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• pp.249-255
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• 2006

Purpose: The diagnostic utility of fluorine-18 2-deoxy-D-glucose positron emission tomograhpy ($^{18}F-FDG $PET) for the non-invasive differentiation of focal lung lesions originated from cancer or inflammation disease by combined visual image interpretation and semi-quantitative uptake value analysis has been documented. In general, Standardized Uptake Value(SUV) is used to diagnose lung disease. But SUV does not contain dynamic information of lung tissue for the glucose. Therefore, this study was undertaken to hypothesis that analysis of dynamic kinetics of focal lung lesions base on $^{18}F-FDG$ PET may more accurately determine the lung disease. So we compared Time Activity Curve(TAC), Standardized Uptake Value-Dynamic Curve(SUV-DC) graph pattern with Glucose Metabolic Rate(MRGlu) from Patlak analysis. Methods: With lung disease, 17 patients were examined. They were injected with $^{18}F-FDG$ over 30-s into peripheral vein while acquisition of the serial transaxial tomographic images were started. For acquisition protocol, we used twelve 10-s, four 30-s, sixteen 60-s, five 300-s and one 900-s frame for 60 mins. Its images were analyzed by visual interpretation TAC, SUV-DC and a kinetic analysis(Patlak analysis). The latter was based on region of interest(ROIs) which were drawn with the lung disease shape. Each optimized patterns were compared with itself. Results: In TAC patterns, it hard to observe cancer type with inflammation disease in early pool blood area but over the time cancer type slope more remarkably increased than inflammation disease. SUV-DC was similar to TAC pattern. In the result of Patlak analysis, In time activity curve of aorta, even though inflammation disease showed higher blood activity than cancer, at first as time went by, blood activity of inflammation disease became the lowest. However, in time activity curve of tissue, cancer had the highest uptake and inflammation disease was in the middle. Conclusion: Through the examination, TAC and SUV-DC could approached the results that lung cancer type and inflammation disease type has it's own difference shape patterns. Also, it has outstanding differentiation between cancer type and inflammation in Patlak and MRGlu analysis. Through these analysis methods, it will helpful to separation lung disease.