As a result of the limited spatial resolution of PET scanners, the measured tissue time-activity curve (TTAC) inside a VOI (even inside single image voxel) is a mixture of the TTACs of more than one tissue component (Blomqvist et al., 1995). At its best, model parameters calculated from this kind of TTAC represents the average of true parameter values inside the VOI. Non-uniformity affects visual interpretation of diagnostic images and VOI definition (Hatt et al., 2011; Dong et al., 2015).
In PET data analysis, tissue heterogeneity is usually considered only as a nuisance, as far as it is caused by partial volume effect. But there also exists biological tissue heterogeneity. Large-scale spatial heterogeneity can even be quantified using PET, possibly providing useful data for survival analysis in oncological patients (O’Sullivan et al., 2003 and 2005; Eary et al., 2008; Asselin et al., 2012; Tixier et al., 2012; Chen et al., 2013; Hatt et al., 2013 and 2015; Willaime et al., 2013) and physiological studies of skeletal muscle (Kalliokoski et al., 2000, 2001a, 2001b, and 2003a; Heinonen et al., 2007).
Atherosclerotic vascular diseases often lead to localized tissue areas with more severe disease or local infarction.
In addition to the spatial heterogeneity observable with medical imaging, tissues are heterogeneous also on microscopic level. Animal studies with intravital multiphoton microscopy has revealed marked temporal variations at the capillary level (vasomotion), with very variable oscillation times (Aalkjær et al., 2011). Vasomotion affects the perfusion distribution in tissue, and may be associated with insulin resistance in skeletal muscle (Kusters and Barrett, 2016).
Analysis of heterogeneous data
Methods based on assumption of homogeneous tissue may lead to over- or underestimation of model parameters. Compartmental model results (even macroparameters) may be biased. Because of tissue heterogeneity, a wrong compartmental model may be selected: for example, apparent k4>0 in FDG studies may be caused by tissue heterogeneity, not by dephosphorylation of FDG-6-phosphate (Schmidt et al., 1992). In the commonly used two-tissue compartmental model the two settings, where tissue compartments are in series or in parallel, are kinetically indistinguishable from each other.
Also regional SUV represents the non-biased average SUV of tissues inside the VOI. However, the optimal scan time for SUV calculation may change, and thus affect the results.
Measurement of tissue heterogeneity
Stochastic dynamic model has been proposed as one possibility to estimate mean compartmental model rate constants and their variance (Niemi et al., 2007). Considering also the image reconstruction error may further improve the tissue heterogeneity estimation (Forma et al., 2013).
Fractal analysis (FA) has been used in several PET and SPET studies to quantitate the distribution of blood flow in tissues (Kuikka et al., 1997; Venegas & Galletti, 2000; Nagao et al., 2001; Kalliokoski et al., 2001a; and 2003b).
The simplest method to quantitate heterogeneity is the calculation of variance of voxel values inside a VOI drawn inside an organ. This method is naturally affected by all factors that affect the PET image quality, but it still provides meaningful results as long as the study protocol is exactly same for the whole study population. This method has been applied to the skeletal muscle (Kalliokoski et al., 2004; Laaksonen et al., 2010).
Tissue heterogeneity and PVE lead to biases in results of compartmental models which assume tissue homogeneity. Especially K1/k2 tends to be biased (underestimated), which is apparent in the partition coefficient in [15O]H2O brain studies.
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Updated at: 2019-03-17
Created at: 2014-05-07
Written by: Vesa Oikonen