Purpose: 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. Procedures: 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. Results: 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. Conclusions: HSIDO is an improved modeling-based PIF estimation method. This method can be applied for quantitative analysis of small-animal dynamic PET studies.

Original languageEnglish
Pages (from-to)286-294
Number of pages9
JournalMolecular Imaging and Biology
Issue number3
StatePublished - Jun 2010


  • Compartment model
  • Input function
  • PET
  • Small-animal imaging


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