Reference region input compartmental models

Reference tissue (input) compartmental models do not require or use plasma sampling as the model input function, but instead are using time-activity curve of a reference region with non-existent (or very low) specific uptake (Cunningham et al., 1991; Lammertsma & Hume, 1996).

Usually reference tissue models are used to estimate binding potential (BPND) from reversible ligand-receptor PET studies, but there are also modified models that can be applied to irreversible binding. While models are usually applied to dynamic PET data collected after injection of one tracer, the models can be extended to dual-tracer PET studies, where two tracers targeting different transmitter systems are injected ∼20-30 min apart (Joshi et al., 2009).

The advantage of all reference tissue models is that since blood sampling and plasma metabolite analysis are not needed, the errors caused by the uncertainties in the measured plasma metabolite fractions are avoided.

One assumption common to all of reference tissue models is that K1/k2 is similar in all studied regions. If K1 and 22 are used to represent these rate constants in the reference tissue, we can write:

, and introduce R1 (or Rinflux):

Reversible uptake

Two reference tissue compartment models are available for quantitation of binding potential (BPND): the (original) full reference tissue compartment model (FRTM or RTCM), and the simplified reference tissue model (SRTM).

There are many binding potentials, but BPND is the one that can be estimated with reference region input compartmental models.

Full reference tissue compartment model (RTCM)

The (full) reference tissue compartmental model (Cunningham et al., 1991) is the original and “gold standard” of reference tissue input methods for the estimation of BPND from reversible ligand receptor studies, and is based on the two-tissue compartmental model.

Reference tissue compartmental model (original)

Figure 1. The original reference tissue compartmental model. The compartments for free tracer (FT) and non-specifically bound tracer (NS) in tissue are combined into a single compartment, called non-displaceable (ND) tracer in tissue, that is, CND = CFT + CNS.

The four parameters R1 (ratio of the K1 values of regions of interest and reference tissue), k2, k3, and BPND (k3/k4) can be estimated using nonlinear fitting (Cunningham et al., 1991).

Assumptions:
  1. Reference region has no specific binding (devoid of receptors)
  2. K1/k2 is same in the regions of interest and in the reference region

This model has some advantages over the Logan plot: dynamic study can be used from the beginning with no need to wait for any equilibrium or search for linear phase. Reference tissue model also provides an index for the perfusion and transport of tracer to the tissue (R1).

Differential equations for the FRTM:

, where CR(t) represents the radioactivity concentration as a function of time in the reference tissue.

Analytical solution for FRTM using Laplace transformation is:

The full reference tissue compartmental model has four free parameters, and often the model is too complex for the noisy PET data. In practise it is usually replaced by the simplified reference tissue model, although that may introduce some bias into the BPND estimates.

Simplified reference tissue model (SRTM)

Simplified reference tissue compartmental model

Figure 2. Simplified reference tissue compartmental model (SRTM).

Simplified reference tissue (or region) model (SRTM or SRRM) can be used when two-compartmental model (one-tissue compartmental model) could reasonable describe the kinetics of the tracer in tissue (Lammertsma and Hume, 1996). Differential equation for SRTM:

Analytical solution using Laplace transformation for SRTM gives:

The three parameters of simplified model (R1, k2 and BPND) can be solved not only using nonlinear fitting but also using linearized methods (Blomqvist, 1984), spectral analysis, or with basis function approach (Gunn et al. 1997). This makes it possible to produce parametric images of model parameters, and, when linearized, also to do the calculations at the sinogram level.

Assumptions:
  1. Reference region has no specific binding (devoid of receptors)
  2. K1/k2 (=R1) is same in the regions of interest and in the reference region
  3. kinetics in all brain regions is fast and simple: if we had an arterial plasma input function, we could fit one-tissue compartmental model to tissue curves fairly well.

If kinetics of the tracer are simple enough to fulfill the requirements of SRTM, then the more complicated RTCM would produce results with high variance. If this requirement is not fulfilled, then results will be biased.

Basis function implementations

The widely used basis function (BF) implementation of SRTM (Gunn et al., 1997) produces unbiased BPND maps presuming that the range of basis functions is carefully optimized. The selection of a specific range of basis functions has been criticized of being slow and inefficient as it is based on a compromise between accuracy and precision (Cselényi et al., 2006; Schuitemaker et al., 2007).

Wu and Carson (2002) proposed that the washout rate constant of the reference tissue could be first estimated using RPM (the original software implementation of BF-SRTM), and the median value (including voxels where BPND>0) would be fixed for all voxels during the second basis function evaluation. This approach mostly improves the quality of R1 images, but leads to negative bias in low BPND values (Schuitemaker et al., 2007).

SRTM with parameter coupling

Coupling of k’2 (k2 in the reference region) to a common value across brain regions or to a first-pass estimate reduces the variance of parameter estimates (Endres et al., 2011).

Modified SRTM to detect neurotransmitter release

SRTM assumes a steady physiological state throughout the PET experiment, from radiotracer injection to the end of scanning. Steady-state can be intentionally perturbed to study the effect of task- or drug-induced changes in neurotransmission (Friston et al., 1997). To analyze this kind of data the reference tissue model had to be modified to account for the changes in the dissociation rate of the radioligand (LSSRM, Alpert et al., 2003).

Ichise’s multilinear regression techniques

Ichise et al (1996) presented a method in which the equations of Logan graphical analysis are solved with multilinear regression. This approach may lead to marked negative bias with noisy data (Schuitemaker et al., 2007). The modified method (Ichise et al., 2002 and 2003) may lead to higher variance with slightly reduced bias (Schuitemaker et al., 2007).

Irreversible uptake

Reference tissue compartmental models are usually applied to quantification of reversible binding, but two reference tissue compartmental models are available for quantitation of irreversible binding or metabolism (k3), when k4=0: the (reduced) reference tissue compartment model (RRTM), and the transport limited reference tissue model (TRTM).

Reduced reference tissue model (RRTM)

If tracer uptake is irreversible during the PET experiment, i.e. k4=0, the full reference tissue model is reduced into a model where three parameters R1, k2, and k3 can be estimated with nonlinear fitting. The k3 value provided by the RRTM model will be proportional to the concentration of unoccupied receptors (Wong et al., 1986).

Reduced reference tissue compartmental model

Figure 3. Reference tissue compartmental model for situations where binding is irreversible in the region of interest, and reversible in the reference region.

Assumptions:
  1. Reference region has no specific uptake (devoid of the target enzyme or receptor)
  2. K1/k2 is same in the regions of interest and in the reference region
  3. k4=0 in the regions of interest

In reference region the rate constant k3 is assumed to be negligible. Differential equations for RRTM:

As an alternative, if a region exists, where the irreversible binding or metabolism is very rapid (k3‘>>k2), it can be used as the reference region in TRTM (see below).

Transport-limited reference tissue model (TRTM)

If tracer uptake is irreversible during PET experiment, i.e. k4=0, and there is no traditional reference region where k3=0, but there exists a region where irreversible binding or metabolism is very rapid (k3‘>>k2), then this region can be used as a “positive” reference region in transport-limited reference tissue model (Herholz et al., 2001; Nagatsuka et al., 2001) to estimate parameters R1, k2, and k3 in regions where k3 is not too high.

Transport-limited reference tissue compartmental model

Figure 4. Reference tissue compartmental model for situations where binding is irreversible in the region of interest and reference region, and binding in reference region is so fast that uptake there is only limited by transport.

Assumptions:
  1. k3‘>>k2 in the reference region
  2. K1/k2 is same in the regions of interest and in the reference region
  3. k4=0 in all regions

Differential equation for TRTM:

Units

The parameters R1 and BPND are unitless. The units of k2 and k3 are min-1.


See also:



References:

Alpert NM, Badgaiyan RD, Livini E, Fischman AJ. A novel method for noninvasive detection of neuromodulatory changes in specific neurotransmitter systems. Neuroimage 2003;19: 1049-1060. doi: 10.1016/S1053-8119(03)00186-1.

Blomqvist G. On the construction of functional maps in positron emission tomography. J Cereb Blood Flow Metab. 1984; 4:629-632. doi: 10.1038/jcbfm.1984.89.

Cselényi Z, Olsson H, Halldin C, Gulyás B, Farde L. A comparison of recent parametric neuroreceptor mapping approaches based on measurements with the high affinity PET radioligands [11C]FLB 457 and [11C]WAY 100635. Neuroimage 2006; 32: 1690-1708. doi: 10.1016/j.neuroimage.2006.02.053.

Cunningham VJ, Hume SP, Price GR Ahier RG, Cremer JE, Jones AKP. Compartmental analysis of diprenorphine binding to opiate receptors in the rat in vivo and its comparison with equilibrium data in vitro. J Cereb Blood Flow Metab. 1991; 11: 1-9. doi: 10.1038/jcbfm.1991.1.

Endres CJ, Hammoud DA, Pomper MG. Reference tissue modeling with parameter coupling: application to a study of SERT binding in HIV. Phys Med Biol. 2011; 56: 2499-2513. doi: 10.1088/0031-9155/56/8/011.

Friston KL, Malizia AL, Wilson S, Cunningham VJ, Jones T, Nutt DJ. Analysis of dynamic radioligand displacement or “activation” studies. J Cereb Blood Flow Metab. 1997; 17: 80-93. doi: 10.1097/00004647-199701000-00011.

Gunn RN, Lammertsma AA, Hume SP, Cunningham VJ. Parametric imaging of ligand-receptor binding in PET using a simplified reference region model. Neuroimage 1997; 6:279-287. doi: 10.1006/nimg.1997.0303.

Herholz K, Lercher M, Wienhard K, Bauer B, Lenz O, Heiss W-D. PET measurement of cerebral acetylcholine esterase activity without blood sampling. Eur J Nucl Med. 2001; 28:472-477.

Ichise M, Ballinger JR, Golan H, Vines D, Luong A, Tsai S, Kung HF. Noninvasive quantification of dopamine D2 receptors with iodine-123-IBF SPECT. J Nucl Med. 1996; 37: 513-520.

Ichise M, Liow JS, Lu JQ, Takano A, Model K, Toyama H, Suhara T, Suzuki K, Innis RB, Carson RE. Linearized reference tissue parametric imaging methods: application to [11C]DASB positron emission tomography studies of the serotonin transporter in human brain. J Cereb Blood Flow Metab. 2003; 23: 1096-1112. doi: 10.1097/01.WCB.0000085441.37552.CA.

Ichise M, Toyama H, Innis RB, Carson RE. Strategies to improve neuroreceptor parameter estimation by linear regression analysis. J Cereb Blood Flow Metab. 2002; 22: 1271-1281. doi: 10.1097/01.WCB.0000038000.34930.4E.

Joshi AD, Koeppe RA, Fessler JA, Kilbourn MR. Signal separation and parameter estimation in noninvasive dual-tracer PET scans using reference-region approaches. J Cerebr Blood Flow Metab. 2009; 29: 1346-1357. doi: 10.1038/jcbfm.2009.53.

Lammertsma AA, Hume SP. Simplified reference tissue model for PET receptor studies. NeuroImage 1996; 4:153-158. doi: 10.1006/nimg.1996.0066.

Nagatsuka S, Fukushi K, Shinotoh H, Namba H, Iyo M, Tanaka N, Aotsuka A, Ota T, Tanada S, Irie T. Kinetic analysis of [11C]MP4A using a high-radioactivity brain region that represents an integrated input function for measurement of cerebral acetylcholinesterase activity without arterial blood sampling. J Cereb Blood Flow Metab. 2001; 21: 1354-1366. doi: 10.1097/00004647-200111000-00011.

Normandin MD, Koeppe RA, Morris ED. Selection of weighting factors for quantification of PET radioligand binding using simplified reference tissue models with noisy input functions. Phys Med Biol. 2012; 57: 609-629.

Oikonen V, Sederholm K. Model equations for reference tissue compartmental models. TPCMOD0002.

Salinas CA, Searle GE, Gunn RN. The simplified reference tissue model: model assumption violations and their impact on binding potential. J Cereb Blood Flow Metab. 2015; 35: 304-311. doi: 10.1038/jcbfm.2014.202.

Schuitemaker A, van Berckel BNM, Kropholler MA, Kloet RW, Jonker C, Scheltens P, Lammertsma AA, Boellaard R. Evaluation of methods for generating parametric (R)-[11C]PK11195 binding images. J Cereb Blood Flow Metab. 2007; 1603-1615. doi: 10.1038/sj.jcbfm.9600459.

Wong DF, Gjedde A, Wagner Jr HN. Quantification of neuroreceptors in the living human brain. I. Irreversible binding of ligands. J Cereb Blood Flow Metab. 1986; 6: 137-146. doi: 10.1038/jcbfm.1986.27.

Wu Y, Carson RE. Noise reduction in the simplified reference tissue model for neuroreceptor functional imaging. J Cereb Blood Flow Metab. 2002; 22: 1440-1452. doi: 10.1097/01.WCB.0000033967.83623.34.

Zanderigo F, Ogden RT, Parsey RV. Reference region approaches in PET: a comparative study on multiple radioligands. J Cereb Blood Flow Metab. 2013; 33: 888-897. doi: 10.1038/jcbfm.2013.26.



Tags: , , , , ,


Created at: 2005-11-30
Updated at: 2018-12-13
Written by: Vesa Oikonen