Image-derived input function with factor analysis and a priori information - Simončič, Urban (Nucl Med Commun, 2015)
BACKGROUND: Quantitative positron emission tomography (PET) studies often require the cumbersome and invasive procedure of arterial cannulation to measure the input function. This study sought to minimize the number of necessary blood samples by developing a factor-analysis based image-derived input function (IDIF) methodology for dynamic PET brain studies. METHODS: IDIF estimation was performed as follows: 1) carotid and background regions were segmented manually on early PET time frame; 2) blood-weighted and tissue-weighted time activity curves (TAC) were extracted with factor analysis; 3) factor analysis results were de-noised and scaled by using the voxels with the highest blood signal; 4) using population data and one blood sample at 40 minutes, the whole-blood TAC was estimated from post-processed factor analysis results; and 5) the parent concentration was finally estimated by correcting the whole blood curve with measured radiometabolite concentrations. The methodology was tested using data from 10 healthy subjects imaged with [(11)C](R)-rolipram. The accuracy of IDIFs was assessed against full arterial sampling by comparing the area under the curve (AUC) of the input functions and by calculating the total distribution volume (V(T)). RESULTS: The shape of the image-derived whole-blood TAC matched well the reference arterial curves, and the whole-blood AUCs was accurately estimated (mean error 1.0 ± 4.3%). The relative Logan-V(T) error was −4.1 ± 6.4%. Compartmental modeling and spectral analysis gave less accurate V(T) results than Logan. CONCLUSION: A factor-analysis based IDIF for [(11)C](R)-rolipram brain PET studies that relies on single blood sample and population data can be used for accurate quantification of Logan-V(T) values.