PT  - JOURNAL ARTICLE
AU  - Sundaram, Senthil K.
AU  - Freedman, Nanette M.T.
AU  - Carrasquillo, Jorge A.
AU  - Carson, Joann M.
AU  - Whatley, Millie
AU  - Libutti, Steven K.
AU  - Sellers, David
AU  - Bacharach, Stephen L.
TI  - Simplified Kinetic Analysis of Tumor <sup>18</sup>F-FDG Uptake: A Dynamic Approach
DP  - 2004 Aug 01
TA  - Journal of Nuclear Medicine
PG  - 1328--1333
VI  - 45
IP  - 8
4099  - http://jnm.snmjournals.org/content/45/8/1328.short
4100  - http://jnm.snmjournals.org/content/45/8/1328.full
SO  - J Nucl Med2004 Aug 01; 45
AB  - 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 > 0.99, P < 0.001, and r = 0.96, P < 0.001, respectively), as did multilevel SKA-M (r > 0.99 and P < 0.001 for both). Mean values of SKA-M (25-min start time) and SKA-S were statistically different from Patlak analysis (P < 0.001 and P < 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.