run_GageRR.m

clear all; close all; clc;

%% Set up time components and determine AIF with Cp10 function
time=0/60:6.4/60:5.4; time=time';
Cp=Cp10(time);

%% Simulate 30 values for ktrans and ve
ktrans=.1*rand(30,1)+0.25;
ve=.1*rand(30,1)+0.4;

%% Generate enhancement curves
for j=1:30
    Ct_tumor(:,j)=myToftsCt2(ktrans(j),ktrans(j)/ve(j),time,Cp)'; %#ok<*SAGROW>
end

%% Generate enhancement curve for reference region and add white gaussian noise
S_RR=myToftsCt2(.1,1,time,Cp)';
S_RR=awgn(S_RR,40,'measured');

%% Define guesses for non-linear analysis
lb=zeros(3,1); lb=lb';
ub=ones(3,1)*10; ub=ub';

%% Set initial SNR

%% Set up info for semi-quant analysis
NumBaselineImages=10;
    
%% Loop through analysis changing SNR by 1 each time
mySNR=[10,20];
% mySNR=5:1:40; UNCOMMENT THIS IF YOU WANT TO RUN THE ENTIRE RANGE
Total_Reps=10;
%Totla_Resp=1000; ; UNCOMMENT THIS IF YOU WANT TO RUN THE ENTIRE RANGE


for SNR=1:length(mySNR)   
    %% Define number of reptitions to test 30 curves for 3 days by gage analysis
    for Reps=1:Total_Reps
        %%Create 3 simulated enhancement curves by adding white gaussian noise to 
        %%original enhancement curve
        for q=1:3    
            for r=1:30
                S_toi=awgn(Ct_tumor(:,r),mySNR(SNR),'measured');
                [maxValue,index] = max(S_toi);
                MER(r,q) = maxValue;
                TTP(r,q) = time(index);
                iauc64(r,q) = trapz(time(1:11),S_toi(1:11));
                slope(r,q) = MER(r,q)./TTP(r,q);
                % Non-Negative Linear
                B=fitdcemri(S_toi,S_RR,time,'nonneg');
                RKtrans1_linear_nonNeg(r,q)=B(1);
                                
                x0=abs(randn(3,1));
                %x0=B(1:3); Uncomment if you want to use initial guesses
                            %for NRRM from LRRM to make NRRM*
                B = fitdcemri(S_toi,S_RR,time,x0,lb,ub,'RRM');
                RKtrans_nonlinear_nonNeg(r,q)=B(1);
                B=fitdcemri(S_toi,S_RR,time,'lsq');
                RKtrans2_linear_lsq(r,q)=B(1);
            end
        end
        
        m=1:30;
        Parts=[m,m,m]; Parts=Parts(:);
        operators=ones(1,90)';
        
        %%Perform gage analysis for semi-quant analysis
        observations_MER=MER(:);
        observations_TTP=TTP(:);
        observations_iauc64=iauc64(:);
        observations_slope=slope(:);
        T=gagerr( observations_MER,{Parts,operators},'printtable','off','printgraph','off'); 
        MER_Repeatibility(Reps,SNR)=T(2,2);
        
        T=gagerr( observations_TTP,{Parts,operators},'printtable','off','printgraph','off'); 
        TTP_Repeatibility(Reps,SNR)=T(2,2);
        
        T=gagerr( observations_iauc64,{Parts,operators},'printtable','off','printgraph','off'); 
        iauc64_Repeatibility(Reps,SNR)=T(2,2);
        
        T=gagerr( observations_slope,{Parts,operators},'printtable','off','printgraph','off'); 
        slope_Repeatibility(Reps,SNR)=T(2,2);
        
        %%Perform gage analysis for linear methods
        T=gagerr( RKtrans1_linear_nonNeg(:),{Parts,operators},'printtable','off','printgraph','off'); 
        Nonneg_Repeatibility(Reps,SNR)=T(2,2);
        
        T=gagerr( RKtrans2_linear_lsq(:),{Parts,operators},'printtable','off','printgraph','off');
        Lsq_Repeatibility(Reps,SNR)=T(2,2);
   
        %%Perform gage analysis for nonlinear method
        T=gagerr( RKtrans_nonlinear_nonNeg(:),{Parts,operators},'printtable','off','printgraph','off');
        Nonlinear_Repeatibility(Reps,SNR)=T(2,2);
    end
end
%% Create table and displat results
X(:,1)=median(MER_Repeatibility);
X(:,2)=median(iauc64_Repeatibility);
X(:,3)=median(TTP_Repeatibility);
X(:,4)=median(slope_Repeatibility);
X(:,5)=median(Nonneg_Repeatibility);
X(:,6)=median(Lsq_Repeatibility);
X(:,7)=median(Nonlinear_Repeatibility);

X=[mySNR',X];
 array2table(X,'VariableNames',{'SNR','MER','iauc64','TTP','slope','NonNeg','Lsq','NonLinear'})