Purpose: Image-derived input functions are desirable for quantifying biological functions in dynamic mouse micro positron emission tomography (PET) studies, but the input function so derived needs to be validated. Conventional validation using serial blood samples is difficult in mice. We introduced the theoretical basis and used computer simulations to show the capability of a new approach that requires only a small number of blood samples per mouse but uses multiple animals.
Procedures: 2-Deoxy-2-[(18)F]fluoro-D-glucose (FDG) kinetics (60 minutes) were simulated for 10 to 20 animals with three to six blood samples available per animal. Various amounts/types of noise/errors in the blood measurements were assumed, and different amounts/types of errors were added to the true input function to simulate image-derived input function. Deviations between blood samples and the derived input function were examined by statistical techniques to evaluate the capability of the approach for detecting the simulated errors in the derived input function.
Results: For a total of 60 blood samples and a 10% measurement noise, a 5% contaminating error in image-derived input function can be detected with a statistical power of approximately 0.9 and with a 95% confidence. The power of the approach is directly related to the error magnitude in the image-derived input function, and is related to the total number of blood samples taken, but is inversely related to the measurement noise of the blood samples.
Conclusion: The new validation approach is expected to be useful for validating input functions derived with image-based methods in dynamic mouse microPET studies.