Beyond Conventional Parametric Modeling: Data-Driven Framework for Estimation and Prediction of Time Activity Curves in Dynamic PET Imaging

The paper empirically compares a reaction–diffusion neural network (RDNN) against a 3TCM baseline under a fixed protocol (train on the first 11 frames and test on the last 4) and reports lower MSE for the RDNN on 35 patient–organ TACs (7 patients × 5 organs), with Tables 1–2 and discussion emphasizing superiority on this dataset . However, it does not provide or claim a universal guarantee that some RDNN hyperparameters will strictly beat 3TCM on all admissible datasets under the same protocol. The candidate solution correctly refutes such a universal dominance claim by constructing a valid counterexample (noiseless data generated exactly by 3TCM), for which 3TCM attains zero test MSE—making strict RDNN improvement impossible. Thus, as an audit: the model’s refutation is logically sound for a universal statement; the paper offers only dataset-specific evidence and is incomplete as a universal argument.