TY - JOUR T1 - Simplified Kinetic Analysis of Tumor <sup>18</sup>F-FDG Uptake: A Dynamic Approach JF - Journal of Nuclear Medicine JO - J Nucl Med SP - 1328 LP - 1333 VL - 45 IS - 8 AU - Senthil K. Sundaram AU - Nanette M.T. Freedman AU - Jorge A. Carrasquillo AU - Joann M. Carson AU - Millie Whatley AU - Steven K. Libutti AU - David Sellers AU - Stephen L. Bacharach Y1 - 2004/08/01 UR - http://jnm.snmjournals.org/content/45/8/1328.abstract N2 - Standardized uptake value (SUV) is often used to quantify 18F-FDG tumor use. Although useful, SUV suffers from known quantitative inaccuracies. Simplified kinetic analysis (SKA) methods have been proposed to overcome the shortcomings of SUV. Most SKA methods rely on a single time point (SKA-S), not on tumor uptake rate. We describe a hybrid between Patlak analysis and existing SKA-S methods, using multiple time points (SKA-M) but reduced imaging time and without measurement of an input function. We compared SKA-M with a published SKA-S method and with Patlak analysis. Methods: Twenty-seven dynamic 18F-FDG scans were performed on 11 cancer patients. A population-based 18F-FDG input function was generated from an independent patient population. SKA-M was calculated using this population input function and either a short, late, dynamic acquisition over the tumor (starting 25–35 min after injection and ending ∼55 min after injection) or dynamic imaging 10 or 25 min to ∼55 min after injection but using only every second or third time point, to permit a 2- or 3-field-of-view study. SKA-S was also calculated. Both SKA-M and SKA-S were compared with the gold standard, Patlak analysis. Results: Both SKA-M (1 field of view) and SKA-S correlated well with Patlak slope (r &gt; 0.99, P &lt; 0.001, and r = 0.96, P &lt; 0.001, respectively), as did multilevel SKA-M (r &gt; 0.99 and P &lt; 0.001 for both). Mean values of SKA-M (25-min start time) and SKA-S were statistically different from Patlak analysis (P &lt; 0.001 and P &lt; 0.04, respectively). One-level SKA-M differed from the Patlak influx constant by only −1.0% ± 1.4%, whereas SKA-S differed by 15.1% ± 3.9%. With 1-level SKA-M, only 2 of 27 studies differed from Ki by more than 20%, whereas with SKA-S, 10 of 27 studies differed by more than 20% from Ki. Conclusion: Both SKA-M and SKA-S compared well with Patlak analysis. SKA-M (1 or multiple levels) had lower variability and bias than did SKA-S, compared with Patlak analysis. SKA-M may be preferred over SUV or SKA-S when a large unmetabolized 18F-FDG fraction is expected and 1–3 fields of view are sufficient. ER -