Support:Documents:Examples:FDG with Time-varying Rate Constants

FDG Model with Time-varying Rate Constants

Overview

This example demonstrates how to implement the two-tissue compartment model with extended such that K₁ and k₂ vary in time.

This models the case where there are physiologic changes during the course of the PET scanning. This might happen if tumors are being treated during scanning.

The standard FDG model was described by Phelps and Huang .

It can also be described by the differential equations:

dC_e/dt = K₁ C_p - k₂ C_e - k₃ C_e + k₄ C_m

dC_m/dt = k₃ C_e - k₄ C_m

C_e is the tissue concentration of FDG and C_m as the tissue concentration of metabolized FDG (FDG-6-phosphate)

C_e and C_m are interpreted as molar concentrations (Salinas, Muzic and Saidel 2007).

In the standard model. the rate constants K₁, k₂, k₃ and k₄ are assumed to be independent of time.

Here we allow K₁ and k₂ to be linear functions of time:

K₁(t) = K₁⁰ + a t

k₂(t) = k₂⁰ + b t

K₁⁰ and k₂⁰ are the initial (0 superscript) values of the rate constants and a and b are the derivatives (slopes) of the rate constant with respect to time.

To implement the time-varying rate constants, create a MATLAB function in a .m file. By making the function general, the same function can be used for K₁ and k₂.

function varargout = kinLinearTime(t, c, ncomp, nsens, pName, pValue, pSensIdx, cm, xtra)
% rate constant varies linearly with time: k = k0 + m*t  where m = dk/dt

K0Name = pName{1};
dkdtName = pName{2};
K0Value = pValue{1};
dkdtValue = pValue{2};
K0SensIndex = pSensIdx{1};
dkdtSensIndex = pSensIdx{2};

if (nargout > 0), % return effective rate constant
    v = K0Value + dkdtValue * t;
    if (v < 0), % don't allow negative values for rate constant
        varargout{1} = 0;
    else
        varargout{1} = v;
    end        
    if (nargout > 1),  % return dk/dc 
        dkdc = zeros([1 ncomp]); 
        varargout{2} = dkdc;  
        if (nargout > 2), % return dk/dp
            dkdp = zeros([1 nsens]);
            if (v >= 0),
                dkdp(K0SensIndex) = 1;
                dkdp(dkdtSensIndex) = t;
            end        
            varargout{3} = dkdp; % dkdp
            if (nargout > 3),
                ddkdpdc = zeros([ncomp nsens]);
                varargout{4} = ddkdpdc; % ddkdpdc
            end
        end
    end
end
return

This function is pretty straightforward. The emphasis of our description here is on the mathematical details. The first output argument varargout{1} holds the value of the rate constant specified at the current time t. This value is first calculated and stored in the variable v.

v = K0Value + dkdtValue * t

k0 is the rate constant at time zero (t=0) and dkdtValue is the increase in the rate constant per unit time (time-derivative or slope). k0Value and dkdtValue are the MATLAB variables that hold the values of these parameters. The value of v is checked to see if it is negative and, if it is, the value zero is used instead since negative values for rate constants are non-physiologic.

The line

varargout{2} = dkdc;  

returns the derivative of the rate constant with respect to all the compartment concentrations (a vector). In this case, the rate constant is not dependent on the concentrations so the derivatives are all zeros.

The lines

dkdp(K0SensIndex) = 1;
dkdp(dkdtSensIndex) = t;

calculate the derivatives of the rate constant with respect to the model parameters k0 (the initial value of the rate constant) and dkdt (the increase in k per unit time). Since a model can have other parameters besides these, the function stores these derivatives in the appropriate element of the derivative vector. Derivatives of this rate constant with respect to all other parameters are zeros.

The line

varargout{4} = ddkdpdc; % ddkdpdc

returns the values of the mixed derivatives (derivative of rate constant with respect to concentrations and with respect to time), which, in this case are all zeros.

The function is stored in kinLinearTime.m in the comkat examples folder.

This function is called to evaluate both K₁ and k₂.

Implementing the Compartment Model

Create a new model

First define some MATLAB variables for clarity and to facilitate exploring what happens if values are changed

K10 =  0.15;  % K1 = K10 + dK1dt * t
dK1dt = -K10 * 0.5 / 60;  % 50% decrease per 60 minutes
k20 =  0.325; % k2 = k20 + dk2dt * t
dk2dt = -k20 * 0.5 / 60;  % 50% decrease per 60 minutes
k3 = 0.05;
k4 = 0.007;

Next, create a compartment model object and define the parameters within the model object

% create empty compartmentModel object
cm = compartmentModel;
  
% define the parameters
cm = addParameter(cm, 'K10',   K10);    % 1/min
cm = addParameter(cm, 'k20',   k20);    % 1/min
cm = addParameter(cm, 'k3',    k3);     % 1/min
cm = addParameter(cm, 'k4',    k4);     % 1/min
cm = addParameter(cm, 'dK1dt', dK1dt);  % 1/min/min
cm = addParameter(cm, 'dk2dt', dk2dt);  % 1/min/min

cm = addParameter(cm, 'sa',    1);            % specific activity of injection
cm = addParameter(cm, 'dk',    log(2)/109.8); % F-18 radioactive decay
cm = addParameter(cm, 'PV',    1);            % (none)

Now, define the model compartments. The first two are Ce and Cm as described above and Junk is a "sink" that collects the FDG that is cleared by the venous circulation.

cm = addCompartment(cm, 'Ce');
cm = addCompartment(cm, 'Cm');
cm = addCompartment(cm, 'Junk');

For the plasma concentration time-course of FDG or input function, we'll use the Feng input which is an analytic expression. Since piecewise polynomials (linear interpolation, cubic splines, etc...) are widely used in COMKAT and their implementation has been optimized, here we construct a cubic spline representation of the input function by sampling the Feng input.

x0=[2 0.5 16.000 2.188 2.081 -7.5 -10 -0.01043]; % Feng parameters
t = [0:.1:3 3.5 4 4.5 5:1:10 12 15:5:(expDuration+5)];
Cp = fengInput(x0, t);
ppCp = spline(t, Cp);
cm = addInput(cm, 'Cp', 'sa', 'dk', 'ppval', ppCp);  % Cp has units of pmol/ml

The PET scanner measurement is assumed to represent the sum of FDG and FDG-6-Phosphate so the model output is calculated as the sum of the C_e and C_m compartments

Wlist = {...
    'Ce', 'PV';
    'Cm', 'PV'};
Xlist = {};
cm = addOutput(cm, 'PET', Wlist, Xlist);

Next define the scan frames as the start and stop time of each image in the dynamic sequence.

ttt=[ ones(12,1)*10/60; ...  % 10 sec
      ones(10,1)*0.5; ...    %  0.5 min
      ones(10,1)*2;...       %  2 min
      ones(10,1)*5;...       %  5 min
      ones(4,1)*10];         % 10 min
scant=[[0;cumsum(ttt(1:(length(ttt)-1)))] cumsum(ttt)];
cm = set(cm, 'ScanTime', scant);

Note the acquisition begins with 12 frames of 10 sec duration, then has 10 frames of 0.5 minute duration, etc... The cumsum() function calculates the cumulative summation. Thus, the first column of scant holds the scan start times and the second column holds the scan end times. These are expressed in minutes:

scant =

         0    0.1667
    0.1667    0.3333
    0.3333    0.5000
    0.5000    0.6667
    0.6667    0.8333
    0.8333    1.0000
    1.0000    1.1667
    1.1667    1.3333
    1.3333    1.5000
    1.5000    1.6667
    1.6667    1.8333
    1.8333    2.0000
...

Notice thus far the links (arrows) that connect the compartments have not been defined. This has been delayed since we want to make two models: one will be the standard or reference model with time-invariant rate constants and one with the time-varying K₁ and k₂. To make two models, we now make a copy of what we have so far

cmRef = cm;

For the reference model, define the links as time-invariant

% define reference model with time-invariant rate constants
cmRef = addLink(cmRef, 'L',        'Cp', 'Ce',   'K10');
cmRef = addLink(cmRef, 'K',        'Ce', 'Junk', 'k20');
cmRef = addLink(cmRef, 'K',        'Ce', 'Cm',   'k3');
cmRef = addLink(cmRef, 'K',        'Cm', 'Ce',   'k4');

For the fancy model, define the links with the time-varying K₁ and k₂ rate constants

cm = addLink(cm, 'LSpecial', 'Cp', 'Ce',   'K1', 'kinLinearTime', {'K10', 'dK1dt'});
cm = addLink(cm, 'KSpecial', 'Ce', 'Junk', 'k2', 'kinLinearTime', {'k20', 'dk2dt'});

noting that these make use of the kinLinearTime.m function defined above.

Also, define the time-invariant links

cm = addLink(cm, 'K',        'Ce', 'Cm',   'k3');
cm = addLink(cm, 'K',        'Cm', 'Ce',   'k4');

OK, the models are ready for use. Begin by solving the model to obtain the time-courses of FDG activity that these models predict would be observed in PET images.

[PET, PETIndex] = solve(cm);
[PETRef, PETRefIndex] = solve(cmRef);

Now plot the time-courses. Column 1 of PET (and PETRef) holds the frame start and end times. For convenience, compute the mid-frame time

tmid = (PET(:,1) + PET(:,2)) / 2;

And finally do the plotting

plot(tmid, PET(:,3), 'b', tmid, PETRef(:,3), 'b:');
xlabel('Time (Minutes)');
ylabel('Activity (uCi/ml)');
legend('Time-varying', 'Reference', 'Location', 'SouthEast');

Well, we can get a bit fancy and plot the time-courses of the K₁ and k₂ rate constants above the model outputs and add other annotations.

clf
axK1k2 = axes('position', [0.2 0.75 0.7 0.2]);
phK = plot(axK1k2, tmid, K10 + dK1dt * tmid, 'b', tmid, k20 + dk2dt * tmid,  'r', ...
             tmid, K10 + 0 * tmid, 'b:',                tmid, k20 + 0 * tmid, 'r:');
set(phK, 'LineWidth', 1.5);
ax = axis; ax(3) = 0; ax(4) = 0.5; axis(ax);
ylabel('Rate Const. (1/min)');
lh = legend('K1', 'k2', 'K1', 'k2', 'Location', 'SouthEast');
set(lh, 'fontsize', 8);

axPET = axes('position', [0.2 0.2 0.7 0.4]);
phPET = plot(axPET, tmid, PET(:,3), 'b', tmid, PETRef(:,3), 'b:');
set(phPET, 'LineWidth', 1.5);
ax = axis; ax(3) = 0; axis(ax);
xlabel('Time (Minutes)');
ylabel('Activity (uCi/ml)');
legend('Time-varying', 'Reference', 'Location', 'SouthEast');

Fitting the standard (time-invariant rate constants) model to data with changing physiology

Lets examine the consequences of fitting the standard (time-invariant rate constants) model to data wherein the the physiology (rate constants) are changing. Use the fancy model to create "data" and fit the standard model to that "data".

data = PET(:,3);
cmRef = set(cmRef, 'ExperimentalData', data);

Set the initial guess and lower and upper bounds on the rate constants

pinit = [0.07; 0.1;   0.04;  0.005]; % inital guess
plb =   [0.01; 0.001; 0.001; 0.0001];  % lower bound
pub =   [0.5;  0.5;   0.2;   0.01]; % upper bound
cmRef = addSensitivity(cmRef, 'K10', 'k20', 'k3', 'k4');

Now do the fitting

[pfit, modelFit] = fit(cmRef, pinit, plb, pub);

Plot the fit over the "data"

figure
phFit = plot(tmid, data, 'bo', tmid, modelFit, 'b:');
set(phFit, 'LineWidth', 1.5);
ax = axis; ax(3) = 0; axis(ax);
xlabel('Time (Minutes)');
ylabel('Activity (uCi/ml)');
legend('Time-varying Data', 'Time-invariant Fit', 'Location', 'SouthEast');

Examine estimated values of the rate constants

  Parm.   True (Init->Final)  Est       Est  vs. Init     Units
   K1       0.150 -> 0.041     0.147    -2.3%            1/min
   k2       0.325 -> 0.089     0.316    -2.8%            1/min
   k3       0.050                   0.048    -4.2%            1/min
   k4       0.007                   0.007    5.6%             1/min
   ki        0.020 -> 0.020     0.019    -3.5%            1/min

It turns out that the estimated values are in close agreement with the values of the parameters at time zero (about the time of injection).

Get the full example

Note This code for this example can be found in fdgTimeVarying.m in the COMKAT examples folder. That version has a few extra bells and whistles that allow us, for example. to use the FDG model with or without k₄.