Single-input-dual-output modeling of image-based input function estimation

Abstract

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.

FIGURE 1

Illustration of typical ROI definition. (A) Cropped transverse slice of mouse FDG PET image. (B) Left ventricle cavity ROI. (C) Myocardial ROI.

FIGURE 2

(A) Example estimated PIFs using HSIDO and CWRU methods for a simulation study as compared to true PIF and the LV ROI TAC. (B) Example estimated PIFs using HSIDO and CWRU methods for a UCLA mouse dataset as compared to blood sample measurements and LV ROI TAC. The difference between HSIDO estimated PIF and blood measurement is mainly due to the delay and dispersion during the travel from heart to the blood sampling location, which resulted in a shorter and wider peak in the blood sample measurement in comparison to a taller and sharper peak at the heart region.