Program Information
Irreversible Two-Tissue Compartment Model Fitting for Dynamic 18F-FDG PET: A Practical Comparison of Methods Using Simulated Time-Activity Data
S McDermott1*, D Yan2, (1) Beaumont Research Institute, Royal Oak, MI, (2) William Beaumont Health System, Royal Oak, MI
WE-C-217BCD-12 Wednesday 10:30:00 AM - 12:30:00 PM Room: 217BCDPurpose:
To compare techniques of fitting simulated Dynamic 18F-FDG PET time-activity curves to the irreversible two-tissue compartment model. The precision and accuracy of various algorithms were assessed in the presence of measurement error and with a practical focus on CPU time.
Methods:
Dynamic PET analysis is often applied to multiple individual voxels, leading to concerns regarding time efficiency, yet a reluctance to compromise on accuracy. This study evaluates selected fitting algorithms in terms of the precision/bias of fitted parameters and run-time. As a standard for comparison, biological parameters were fixed at typical values (K1=0.2, k2=0.25, k3=0.05, Kᵢ=0.3334). Tissue time-activity curves were generated using the model equation and by incorporating activity/time dependent Gaussian noise. The following algorithms were then applied: Genetic, Conjugate Gradient (CG), Gradient Descent (GD), Simulated Annealing (SA), Levenberg-Marquardt (LMQ), Gauss-Newton (GN), and Limited-BFGS (L-BFGS). Non-iterative, problem specific approaches were also considered: Patlak analysis (K? only), and Blomqvist linearization (BL). Parameter accuracy and precision were quantified with relative errors (REs).
Results:
At typical noise levels, maximal REs were >60% (resulting in |δK?|>10%) for GD and SA, <20% for BL (|δK?|<6%) and Patlak (|δK?|<4%), and <15% (|δK?|<2%) with other methods. On average, the highest fidelity parameter estimates (rate-constant REs<2.5%, |δK?|<0.15%) were attained with L-BFGS, GN, and LMQ. In contrast, the non-iterative methods provided poorer estimates (RE between 1-7%), though run-time (<0.05 ms) was 100-1000x less. Using BL results as initial estimates for L-BFGS (BL+L-BFGS), GN (BL+GN) or LMQ (BL+LMQ) yielded rapid convergence (<0.1 ms), while maintaining superiority with respect to parameter bias.
Conclusions:
Poor choice of a fitting algorithm may lead to significant errors in estimated kinetic parameters and/or impractically high computation times. Thus, our recommendation is the use of BL+L-BFGS, BL+GN or BL+LMQ, which is contrary to the gold standards, Patlak and LMQ (alone), commonly seen in the literature.
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