Purpose: We aimed to investigate whether a clinically feasible dual time-point (DTP) approach can accurately estimate the metabolic uptake rate constant (K_i) and to explore reliable acquisition times through simulations and clinical assessment considering patient comfort and quantification accuracy. Methods: We simulated uptake kinetics in different tumors for four sets of DTP PET images within the routine clinical static acquisition at 60-min post-injection (p.i.). We determined K_i for a total of 81 lesions. K_i quantification from full dynamic PET data (Patlak-K_i) and K_i from DTP (DTP-K_i) were compared. In addition, we scaled a population-based input function (PBIF_scl) with the image-derived blood pool activity sampled at different time points to assess the best scaling time-point for K_i quantifications in the simulation data. Results: In the simulation study, K_i estimated using DTP via (30,60–min), (30,90–min), (60,90-min), and (60,120-min) samples showed strong correlations (r ≥ 0.944, P < 0.0001) with the true value of K_i. The DTP results with the PBIF_scl at 60-min time-point in (30,60–min), (60,90-min), and (60,120-min) were linearly related to the true K_i with a slope of 1.037, 1.008, 1.013 and intercept of −6 × 10⁻⁴, 2 × 10⁻⁵, 5 × 10⁻⁵, respectively. In a clinical study, strong correlations (r ≥ 0.833, P < 0.0001) were observed between Patlak-K_i and DTP-K_i. The Patlak-derived mean values of K_i, tumor-to-background-ratio, signal-to-noise-ratio, and contrast-to-noise-ratio were linearly correlated with the DTP method. Conclusions: Besides calculating the retention index as a commonly used quantification parameter in DTP imaging, our DTP method can accurately estimate K_i.