Yun Zhao

Dynamic PET must pick a kinetic model for each voxel's tracer curve, and the standard route fits every candidate by nonlinear least squares and compares AIC or BIC, which is slow and favors simple models under noise. A consistency model folds the continuous kinetic parameters and the discrete model index into one target vector conditioned on the measured time-activity curve and input function, so one forward sampling pass yields the joint posterior. On 7,000 simulated FDG total-body PET curves it reached 97.97% selection accuracy across seven compartment models versus 58.67% for NLS+AIC, and 91.4% versus 73.5% for NLS+BIC in simulated brain dopamine-release detection.

Dynamic PET must pick a kinetic model for each voxel's tracer curve, and the standard route fits every candidate by nonlinear least squares and compares AIC or BIC, which is slow and favors simple models under noise. A consistency model folds the continuous kinetic parameters and the discrete model index into one target vector conditioned on the measured time-activity curve and input function, so one forward sampling pass yields the joint posterior. On 7,000 simulated FDG total-body PET curves it reached 97.97% selection accuracy across seven compartment models versus 58.67% for NLS+AIC, and 91.4% versus 73.5% for NLS+BIC in simulated brain dopamine-release detection.