Generation of parametric Ki images for FDG PET using two 5‐min scans. Issue 9 (13th August 2021) - Wu, Jing; Liu, Hui; Ye, Qing; Gallezot, Jean‐Dominique; Naganawa, Mika; Miao, Tianshun; Lu, Yihuan; Chen, Ming‐Kai; Esserman, Denise A.; Kyriakides, Tassos C.; Carson, Richard E.; Liu, Chi
Abstract: Purpose: The net uptake rate constant ( K i ) derived from dynamic imaging is considered the gold standard quantification index for FDG PET. In this study, we investigated the feasibility and assessed the clinical usefulness of generating K i images for FDG PET using only two 5‐min scans with population‐based input function (PBIF). Methods: Using a Siemens Biograph mCT, 10 subjects with solid lung nodules underwent a single‐bed dynamic FDG PET scan and 13 subjects (five healthy and eight cancer patients) underwent a whole‐body dynamic FDG PET scan in continuous‐bed‐motion mode. For each subject, a standard K i image was generated using the complete 0–90 min dynamic data with Patlak analysis ( t * = 20 min) and individual patient's input function, while a dual‐time‐point K i image was generated from two 5‐min scans based on the Patlak equations at early and late scans with the PBIF. Different start times for the early (ranging from 20 to 55 min with an increment of 5 min) and late (ranging from 50 to 85 min with an increment of 5 min) scans were investigated with the interval between scans being at least 30 min (36 protocols in total). The optimal dual‐time‐point protocols were then identified. Regions of interest (ROI) were drawn on nodules for the lung nodule subjects, and on tumors, cerebellum, and bone marrow for the whole‐body‐imaging subjects. Quantification accuracy was compared using the mean value of each ROI between standard K i (gold standard) and dual‐time‐point K i, as well as between standard K i and relative standardized uptake value (SUV) change that is currently used in clinical practice. Correlation coefficients and least squares fits were calculated for each dual‐time‐point protocol and for each ROI. Then, the predefined criteria for identifying a reliable dual‐time‐point K i estimation for each ROI were empirically determined as: (1) the squared correlation coefficient ( R 2 ) between standard K i and dual‐time‐point K i is larger than 0.9; (2) the absolute difference between the slope of the equality line (1.0) and that of the fitted line when plotting standard K i versus dual‐time‐point K i is smaller than 0.1; (3) the absolute value of the intercept of the fitted line when plotting standard K i versus dual‐time‐point K i normalized by the mean of the standard K i across all subjects for each ROI is smaller than 10%. Using Williams' one‐tailed t test, the correlation coefficient ( R ) between standard K i and dual‐time‐point K i was further compared with that between standard K i and relative SUV change, for each dual‐time‐point protocol and for each ROI. Results: Reliable dual‐time‐point K i images were obtained for all the subjects using our proposed method. The percentage error introduced by the PBIF on the dual‐time‐point K i estimation was smaller than 1% for all 36 protocols. Using the predefined criteria, reliable dual‐time‐point K i estimation could be obtained in 25 of 36 protocols for nodules and in 34 of 36 protocols for tumors. A longer time interval between scans provided a more accurate K i estimation in general. Using the protocol of 20–25 min plus 80–85 or 85–90 min, very high correlations were obtained between standard K i and dual‐time‐point K i ( R 2  = 0.994, 0.980, 0.971 and 0.925 for nodule, tumor, cerebellum, and bone marrow), with all the slope values with differences ≤0.033 from 1 and all the intercept values with differences ≤0.0006 mL/min/cm 3 from 0. The corresponding correlations were much lower between standard K i and relative SUV change ( R 2  = 0.673, 0.684, 0.065, 0.246). Dual‐time‐point K i showed a significantly higher quantification accuracy with respect to standard K i than relative SUV change for all the 36 protocols ( p  < 0.05 using Williams' one‐tailed t test). Conclusions: Our proposed approach can obtain reliable K i images and accurate K i quantification from dual‐time‐point scans (5‐min per scan), and provide significantly higher quantification accuracy than relative SUV change that is currently used in clinical practice.