Model-based input function

Quantitative PET studies require that the input function (IF), representing cumulative availability of authentic radiotracer in arterial plasma, is measured. Traditionally this is achieved via arterial cannulation (AIF). Image-derived input function (IDIF) and model-based input function (MBIF) are noninvasive alternatives to arterial cannulation and sampling, but also associated with several problems that must be solved and methods well validated before using in clinical studies (Zanotti-Fregonara et al., 2011; Christensen et al., 2014). IDIF and MBIF can only be implemented with a minority of PET tracers, and even then some blood samples usually need to be taken for scaling. Total body PET scanners that can dynamically scan the whole body would be very useful for obtaining image-derived input function, as large blood containing regions (heart cavities), many blood rich organs (liver, spleen, kidneys), and other organs with variable uptake kinetics could be assessed simultaneously.

Model-based input methods do not require that pixels representing blood curve were to be found in the image, but rely on assumption that the input function is common to all tissue regions in the image, and can be solved from the data (simultaneous estimation method, SIME). The outcome does not need any further corrections, since it already represents delay-, dispersion-, and metabolite-corrected arterial plasma (or reference region). Usually the input function is described by some continuous function, parameters of which are fitted together with compartmental model parameters for several tissue curves. In simplified approaches some of the input function parameters can be based on population means, some on administered dose and (lean) body mass (Hapdey et al., 2011), effectively combining the MBIF to population-based input function methods. SIME has also been used to correct the underestimated peak of image-derived input function (Sanabria-Bohórquez et al., 2003).

For metabolic radiotracers the input function still needs to be scaled to the correct level using either blood sample(s), or injected dose, BMI or BSA, or other patient information (Feng et al., 1997; Wong et al., 2001; Ogden et al., 2010; Zanderigo et al., 2015; Mikhno et al., 2015). Usage of venous sample for the scaling must be validated for each tracer independently (Bartlett et al., 2018). In case of radiowater, if the partition volume of the water can be fixed to a certain value, perfusion can be calculated without blood sampling (Watabe et al., 1996). Simultaneous DCE- or DSC-MRI scan can improve the robustness of SIME. Su et al (2013) combined the model of [15O]H2O and image-derived PSF-corrected carotid TACs to estimate the AIF without further scaling.

Binding potential for radioligands in brain receptor imaging is calculated using reference region input methods, if reference region exists. But when imaging target is found ubiquitously in the brain, SIME has been applied, based on the assumption of similar VND in all brain regions (Ogden et al., 2015). For instance, SIME approach has been tested with MAO A tracer [11C]harmine, although the test-retest results were not encouraging (Zanderigo et al., 2018). An alternative approach is to measure blood curve, but apply SIME to estimate the metabolite correction (Burger & Buck, 1996; Sanabria-Bohórquez et al., 2000).


Small animals

Image- and model-based methods in small-animal studies are especially important, because reliable and frequent arterial blood sampling may not be possible. PET devices that can dynamically scan the whole body of the animal simultaneously are useful in obtaining MBIF. Model-based spill-over correction of LV cavity -derived input has been used in several mice studies. Ferl et al (2007) have fitted simultaneously muscle and liver TACs to estimate input curve in mice FDG study. Wong et al. (2008, 2013) used kinetics of urine radioactivity in the whole bladder.


See also:



References

Christensen AN, Reichkendler MH, Larsen R, Auerbach P, Højgaard L, Nielsen HB, Ploug T, Stallknecht B, Holm S. Calibrated image-derived input functions for the determination of the metabolic uptake rate of glucose with [18F]-FDG. Nucl Med Commun. 2014; 35: 353-361. doi: 10.1097/MNM.0000000000000063.

Feng D, Wong K-P, Wu C-M, Siu W-C. A technique for extracting physiological parameters and the required input function simultaneously from PET image measurements: theory and simulation study. IEEE Trans Inf Technol Biomed. 1997; 1(4): 243-254. doi: 10.1109/4233.681168.

Huang J, O’Sullivan F. An analysis of whole body tracer kinetics in dynamic PET studies with application to image-based blood input function extraction. IEEE trans Med Imaging 2014; 33(5): 1093-1108. doi: 10.1109/TMI.2014.2305113.

Su Y, Arbelaez AM, Benzinger TLS, Snyder AZ, Vlassenko AG, Mintun MA, Raichle ME. Noninvasive estimation of the arterial input function in positron emission tomography imaging of cerebral blood flow. J Cereb Blood Flow Metab. 2013; 33: 115-121. doi: 10.1038/jcbfm.2012.143.

Zanderigo F, Ogden RT, Parsey RV. Noninvasive blood-free full quantification of positron emission tomography radioligand binding. J Cereb Blood Flow Metab. 2015; 35: 148-156. doi: 10.1038/jcbfm.2014.191.

Zanotti-Fregonara P, Chen K, Liow J-S, Fujita M, Innis RB. Image-derived input function for brain PET studies: many challenges and few opportunities. J Cereb Blood Flow Metab. 2011; 31: 1986-1998. doi: 10.1038/jcbfm.2011.107.



Tags: , , , ,


Updated at: 2019-01-18
Created at: 2018-01-09
Written by: Vesa Oikonen