Single-Input-Dual-Output Modeling of Image-Based Input Function Estimation

Quantification of small-animal positron emission tomography (PET) images necessitates knowledge of the plasma input function (PIF). We propose and validate a simplified hybrid single-input-dual-output (HSIDO) algorithm to estimate the PIF. The HSIDO algorithm integrates the peak of the input function from two region-of-interest time-activity curves with a tail segment expressed by a sum of two exponentials. Partial volume parameters are optimized simultaneously. The algorithm is validated using both simulated and real small-animal PET images. In addition, the algorithm is compared to existing techniques in terms of area under curve (AUC) error, bias, and precision of compartmental model micro-parameters. In general, the HSIDO method generated PIF with significantly (P < 0.05) less AUC error, lower bias, and improved precision of kinetic estimates in comparison to the reference method. HSIDO is an improved modeling-based PIF estimation method. This method can be applied for quantitative analysis of small-animal dynamic PET studies.