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GannetFit.m
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function MRS_struct = GannetFit(MRS_struct, varargin)
% Signal fitting in the frequency domain using nonlinear least-squares optimization
if nargin == 0
fprintf('\n');
error('MATLAB:minrhs', 'Not enough input arguments.');
end
MRS_struct.version.fit = '241007';
if MRS_struct.p.PRIAM
vox = MRS_struct.p.vox;
else
vox = MRS_struct.p.vox(1);
end
if MRS_struct.p.phantom
error('The loaded data are phantom data. Use GannetFitPhantom instead of GannetFit.');
end
if nargin > 1
% varargin = Optional arguments if user wants to specify a target
% metabolite, overwriting the parameter set in GannetPreInitialise.m
switch varargin{1}
case 'GABA'
MRS_struct.p.target = {'GABA'};
case 'Glx'
MRS_struct.p.target = {'Glx'};
case 'GABAGlx'
MRS_struct.p.target = {'GABAGlx'};
case 'GSH'
MRS_struct.p.target = {'GSH'};
case 'Lac'
MRS_struct.p.target = {'Lac'};
case 'EtOH'
MRS_struct.p.target = {'EtOH'};
end
end
target = MRS_struct.p.target;
freq = MRS_struct.spec.freq;
lsqopts = optimset('lsqcurvefit');
lsqopts = optimset(lsqopts,'MaxIter',800,'TolX',1e-4,'TolFun',1e-4,'Display','off');
nlinopts = statset('nlinfit');
nlinopts = statset(nlinopts,'MaxIter',400,'TolX',1e-6,'TolFun',1e-6,'FunValCheck','off');
warning('off','stats:nlinfit:ModelConstantWRTParam');
warning('off','stats:nlinfit:IllConditionedJacobian');
warning('off','stats:nlinfit:IterationLimitExceeded');
warning('off','MATLAB:rankDeficientMatrix');
% Loop over voxels if PRIAM
for kk = 1:length(vox)
if strcmp(MRS_struct.p.reference, 'H2O')
WaterData = MRS_struct.spec.(vox{kk}).water;
end
run_count = 0;
% Loop over edited spectra if HERMES
for jj = 1:length(target)
if strcmp(target{jj}, 'GABAGlx')
t = 'GABA+Glx';
else
t = target{jj};
end
if jj == 1
fprintf('\nFitting %s in...\n', t);
else
fprintf('Fitting %s in...\n', t);
end
DIFF = MRS_struct.spec.(vox{kk}).(target{jj}).diff;
SUM = MRS_struct.spec.(vox{kk}).(target{jj}).sum;
if MRS_struct.p.HERMES
OFF = MRS_struct.spec.(vox{kk}).(target{jj}).off_off;
else
OFF = MRS_struct.spec.(vox{kk}).(target{jj}).off;
end
error_report = cell(1);
catch_ind = 1;
for ii = 1:MRS_struct.p.numScans
[~,b,c] = fileparts(MRS_struct.metabfile{1,ii});
fprintf('%s...\n', [b c]);
try % pass to next dataset if errors occur
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 1. Metabolite Fitting
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
switch target{jj}
case 'GABA'
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% GABA
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if length(MRS_struct.p.target) == 3 && all(ismember(MRS_struct.p.target,{'EtOH','GABA','GSH'}))
freqbounds = find(freq <= 3.55 & freq >= 2.6);
plotbounds = find(freq <= 3.6 & freq >= 2.6);
maxinGABA = abs(max(real(DIFF(ii,freqbounds))) - min(real(DIFF(ii,freqbounds))));
grad_points = (real(DIFF(ii,freqbounds(end))) - real(DIFF(ii,freqbounds(1)))) ./ abs(freqbounds(end) - freqbounds(1));
LinearInit = grad_points ./ abs(freq(1) - freq(2));
constInit = (real(DIFF(ii,freqbounds(end))) + real(DIFF(ii,freqbounds(1))))./2;
GaussModelInit = [maxinGABA -90 3.026 -LinearInit constInit];
GaussModelInit([1 4 5]) = GaussModelInit([1 4 5]) / maxinGABA; % Scale initial conditions to avoid warnings about numerical underflow
lb = [0 -200 2.87 -40*maxinGABA -2000*maxinGABA];
ub = [4000*maxinGABA -40 3.12 40*maxinGABA 1000*maxinGABA];
lb([1 4 5]) = lb([1 4 5]) / maxinGABA;
ub([1 4 5]) = ub([1 4 5]) / maxinGABA;
% Down-weight co-edited Cho signal by including
% observation weights in nonlinear regression
w = ones(size(DIFF(ii,freqbounds)));
residfreq = freq(freqbounds);
ChoRange = residfreq >= 3.16 & residfreq <= 3.285;
weightRange = ChoRange;
w(weightRange) = 0.001;
% Least-squares model fitting
GaussModelInit = lsqcurvefit(@GaussModel, GaussModelInit, freq(freqbounds), real(DIFF(ii,freqbounds)) / maxinGABA, lb, ub, lsqopts);
modelFun_w = @(x,freq) sqrt(w) .* GaussModel(x,freq); % add weights to the model
[GaussModelParam, resid] = nlinfit(freq(freqbounds), sqrt(w) .* real(DIFF(ii,freqbounds)) / maxinGABA, modelFun_w, GaussModelInit, nlinopts); % add weights to the data
[~, residPlot] = nlinfit(freq(freqbounds), real(DIFF(ii,freqbounds)) / maxinGABA, @GaussModel, GaussModelParam, nlinopts); % re-run for residuals for output figure
% Rescale fit parameters and residuals
GaussModelParam([1 4 5]) = GaussModelParam([1 4 5]) * maxinGABA;
resid = resid * maxinGABA;
residPlot = residPlot * maxinGABA;
else
freqbounds = find(freq <= 3.55 & freq >= 2.79);
plotbounds = find(freq <= 3.6 & freq >= 2.7);
maxinGABA = abs(max(real(DIFF(ii,freqbounds))) - min(real(DIFF(ii,freqbounds))));
grad_points = (real(DIFF(ii,freqbounds(end))) - real(DIFF(ii,freqbounds(1)))) ./ abs(freqbounds(end) - freqbounds(1));
LinearInit = grad_points ./ abs(freq(1) - freq(2));
constInit = (real(DIFF(ii,freqbounds(end))) + real(DIFF(ii,freqbounds(1))))./2;
GaussModelInit = [maxinGABA -90 3.026 -LinearInit constInit];
GaussModelInit([1 4 5]) = GaussModelInit([1 4 5]) / maxinGABA; % Scale initial conditions to avoid warnings about numerical underflow
lb = [0 -200 2.87 -40*maxinGABA -2000*maxinGABA];
ub = [4000*maxinGABA -40 3.12 40*maxinGABA 1000*maxinGABA];
lb([1 4 5]) = lb([1 4 5]) / maxinGABA;
ub([1 4 5]) = ub([1 4 5]) / maxinGABA;
% Down-weight Cho subtraction artifact by including
% observation weights in nonlinear regression; improves
% accuracy of peak fitting (MM: 170701 - thanks to Alex
% Craven of University of Bergen for this idea)
w = ones(size(DIFF(ii,freqbounds)));
residfreq = freq(freqbounds);
ChoRange = residfreq >= 3.16 & residfreq <= 3.285;
weightRange = ChoRange;
w(weightRange) = 0.001;
% Weighted least-squares model fitting
GaussModelInit = lsqcurvefit(@GaussModel, GaussModelInit, freq(freqbounds), real(DIFF(ii,freqbounds)) / maxinGABA, lb, ub, lsqopts);
modelFun_w = @(x,freq) sqrt(w) .* GaussModel(x,freq); % add weights to the model
[GaussModelParam, resid] = nlinfit(freq(freqbounds), sqrt(w) .* real(DIFF(ii,freqbounds)) / maxinGABA, modelFun_w, GaussModelInit, nlinopts); % add weights to the data
[~, residPlot] = nlinfit(freq(freqbounds), real(DIFF(ii,freqbounds)) / maxinGABA, @GaussModel, GaussModelParam, nlinopts); % re-run for residuals for output figure
% Rescale fit parameters and residuals
GaussModelParam([1 4 5]) = GaussModelParam([1 4 5]) * maxinGABA;
resid = resid * maxinGABA;
residPlot = residPlot * maxinGABA;
end
GABAheight = GaussModelParam(1);
MRS_struct.out.(vox{kk}).(target{jj}).FitError(ii) = 100 * std(resid) / GABAheight;
MRS_struct.out.(vox{kk}).(target{jj}).Area(ii) = GaussModelParam(1) ./ sqrt(-GaussModelParam(2)) * sqrt(pi);
sigma = sqrt(1/(2*(abs(GaussModelParam(2)))));
MRS_struct.out.(vox{kk}).(target{jj}).FWHM(ii) = abs((2 * MRS_struct.p.LarmorFreq(ii)) * sigma);
MRS_struct.out.(vox{kk}).(target{jj}).ModelParam(ii,:) = GaussModelParam;
MRS_struct.out.(vox{kk}).(target{jj}).Resid(ii,:) = resid;
% Calculate SNR of GABA signal
noiseSigma_DIFF = CalcNoise(freq, DIFF(ii,:));
MRS_struct.out.(vox{kk}).(target{jj}).SNR(ii) = abs(GABAheight) / noiseSigma_DIFF;
case 'GSH'
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% GSH
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
freqbounds = find(freq <= 3.5 & freq >= 2.25);
plotbounds = find(freq <= 4.2 & freq >= 1.75);
GSHbounds = freq <= 3.3 & freq >= 2.85;
Aspartylbounds = freq <= 2.85 & freq >= 2.25;
maxinGSH = max(abs(real(DIFF(ii,GSHbounds))));
[maxinAspartyl, maxInd] = max(abs(real(DIFF(ii,Aspartylbounds))));
offset = real(DIFF(ii,freqbounds(end)));
grad_points = (real(DIFF(ii,freqbounds(end))) - real(DIFF(ii,freqbounds(1)))) ./ abs(freqbounds(end) - freqbounds(1));
LinearInit = grad_points ./ abs(freq(1) - freq(2));
tmp = DIFF(ii,Aspartylbounds);
s = sign(real(tmp(maxInd)));
maxinAspartyl = s * maxinAspartyl;
if MRS_struct.p.HERMES
s = -1;
else
s = 1;
end
if MRS_struct.p.TE(ii) < 100
GSHgaussModel = @FiveGaussModel;
GaussModelInit = [maxinGSH -300 2.95 ...
s*maxinAspartyl*0.25 -500 2.73 ...
maxinAspartyl -1000 2.61 ...
maxinAspartyl -1000 2.55 ...
s*maxinAspartyl*0.15 -600 2.45 ...
offset -LinearInit -LinearInit];
GaussModelInit([1 4 7 10 13 16 17 18]) = GaussModelInit([1 4 7 10 13 16 17 18]) / maxinGSH; % Scale initial conditions to avoid warnings about numerical underflow
lb = [-4000*maxinGSH -1000 2.95-0.02 ...
4000*maxinAspartyl*0.25 -1000 2.73-0.02 ...
4000*maxinAspartyl -1000 2.61-0.02 ...
4000*maxinAspartyl -1000 2.55-0.02 ...
4000*maxinAspartyl*0.15 -1000 2.45-0.02 ...
-2000*abs(offset) 2000*maxinAspartyl 2000*maxinAspartyl];
ub = [4000*maxinGSH -40 2.95+0.02 ...
-4000*maxinAspartyl*0.25 -40 2.73+0.02 ...
-4000*maxinAspartyl -40 2.61+0.02 ...
-4000*maxinAspartyl -40 2.55+0.02 ...
-4000*maxinAspartyl*0.15 -40 2.45+0.02 ...
1000*abs(offset) -1000*maxinAspartyl -1000*maxinAspartyl];
lb([1 4 7 10 13 16 17 18]) = lb([1 4 7 10 13 16 17 18]) / maxinGSH;
ub([1 4 7 10 13 16 17 18]) = ub([1 4 7 10 13 16 17 18]) / maxinGSH;
else
GSHgaussModel = @SixGaussModel;
GaussModelInit = [maxinGSH -300 2.95 ...
maxinAspartyl*0.7 -500 2.73 ...
maxinAspartyl -1000 2.63 ...
maxinAspartyl*0.7 -1000 2.58 ...
maxinAspartyl*0.5 -600 2.46 ...
maxinAspartyl*0.35 -600 2.37 ...
offset -LinearInit -LinearInit];
GaussModelInit([1 4 7 10 13 16 19 20 21]) = GaussModelInit([1 4 7 10 13 16 19 20 21]) / maxinGSH; % Scale initial conditions to avoid warnings about numerical underflow
lb = [-4000*maxinGSH -1000 2.95-0.02 ...
-4000*maxinAspartyl*0.7 -1000 2.73-0.02 ...
-4000*maxinAspartyl -1000 2.63-0.02 ...
-4000*maxinAspartyl*0.7 -1000 2.58-0.02 ...
-4000*maxinAspartyl*0.5 -1000 2.46-0.02 ...
-4000*maxinAspartyl*0.35 -1000 2.37-0.02 ...
-2000*abs(offset) -2000*maxinAspartyl -2000*maxinAspartyl];
ub = [4000*maxinGSH -40 2.95+0.02 ...
4000*maxinAspartyl*0.7 -40 2.73+0.02 ...
4000*maxinAspartyl -40 2.63+0.02 ...
4000*maxinAspartyl*0.7 -40 2.58+0.02 ...
4000*maxinAspartyl*0.5 -40 2.46+0.02 ...
4000*maxinAspartyl*0.35 -40 2.37+0.02 ...
1000*abs(offset) 1000*maxinAspartyl 1000*maxinAspartyl];
lb([1 4 7 10 13 16 19 20 21]) = lb([1 4 7 10 13 16 19 20 21]) / maxinGSH;
ub([1 4 7 10 13 16 19 20 21]) = ub([1 4 7 10 13 16 19 20 21]) / maxinGSH;
end
if length(MRS_struct.p.target) == 3 && all(ismember(MRS_struct.p.target, {'EtOH','GABA','GSH'}))
w = ones(size(DIFF(ii,freqbounds)));
residfreq = freq(freqbounds);
ChoRange = residfreq >= 3.13 & residfreq <= 3.3;
weightRange = ChoRange;
w(weightRange) = 0.001;
else
w = ones(size(DIFF(ii,freqbounds)));
end
% Weighted least-squares model fitting
GaussModelInit = lsqcurvefit(GSHgaussModel, GaussModelInit, freq(freqbounds), real(DIFF(ii,freqbounds)) / maxinGSH, lb, ub, lsqopts);
modelFun_w = @(x,freq) sqrt(w) .* GSHgaussModel(x,freq); % add weights to the model
[GaussModelParam, resid] = nlinfit(freq(freqbounds), sqrt(w) .* real(DIFF(ii,freqbounds)) / maxinGSH, modelFun_w, GaussModelInit, nlinopts); % add weights to the data
[~, residPlot] = nlinfit(freq(freqbounds), real(DIFF(ii,freqbounds)) / maxinGSH, GSHgaussModel, GaussModelParam, nlinopts); % re-run for residuals for output figure
% Rescale fit parameters and residuals
if MRS_struct.p.TE(ii) < 100
GaussModelParam([1 4 7 10 13 16 17 18]) = GaussModelParam([1 4 7 10 13 16 17 18]) * maxinGSH;
else
GaussModelParam([1 4 7 10 13 16 19 20 21]) = GaussModelParam([1 4 7 10 13 16 19 20 21]) * maxinGSH;
end
resid = resid * maxinGSH;
residPlot = residPlot * maxinGSH;
GSHGaussModelParam = GaussModelParam;
if MRS_struct.p.TE(ii) < 100 % MM (190613)
GSHGaussModelParam(4:3:13) = 0;
else
GSHGaussModelParam(4:3:16) = 0;
end
BaselineModelParam = GSHGaussModelParam;
BaselineModelParam(1) = 0;
MRS_struct.out.(vox{kk}).(target{jj}).Area(ii) = real(sum(FiveGaussModel(GSHGaussModelParam, freq(freqbounds)) - FiveGaussModel(BaselineModelParam, freq(freqbounds)))) ...
* abs(freq(1) - freq(2));
GSHheight = GSHGaussModelParam(1);
% Range to determine residuals for GSH
residfreq = freq(freqbounds);
residGSH = resid(residfreq <= 3.3 & residfreq >= 2.82);
MRS_struct.out.(vox{kk}).(target{jj}).FitError(ii) = 100 * std(residGSH) / GSHheight;
sigma = sqrt(1/(2*(abs(GSHGaussModelParam(2)))));
MRS_struct.out.(vox{kk}).(target{jj}).FWHM(ii) = abs((2*MRS_struct.p.LarmorFreq(ii))*sigma);
MRS_struct.out.(vox{kk}).(target{jj}).ModelParam(ii,:) = GaussModelParam;
MRS_struct.out.(vox{kk}).(target{jj}).Resid(ii,:) = residGSH;
% Calculate SNR of GSH signal
noiseSigma_DIFF = CalcNoise(freq, DIFF(ii,:));
MRS_struct.out.(vox{kk}).(target{jj}).SNR(ii) = abs(GSHheight) / noiseSigma_DIFF;
% MM (200728)
%MRS_struct.out.(vox{kk}).(target{jj}).FitError2(ii) = sqrt(mean(residGSH.^2)) / (0.5*noiseSigma_DIFF);
case 'Lac'
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Lac
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
freqbounds = find(freq <= 1.8 & freq >= 0.5);
plotbounds = find(freq <= 2.12 & freq >= 0);
offset = (mean(real(DIFF(ii,freqbounds(1:10))),2) + mean(real(DIFF(ii,freqbounds((end-9):end))),2)) / 2;
slope = (mean(real(DIFF(ii,freqbounds(1:10))),2) - mean(real(DIFF(ii,freqbounds((end-9):end))),2)) / abs(freq(freqbounds(1)) - freq(freqbounds(end)));
maxinLac = max(real(DIFF(ii,freqbounds)));
LacModelInit = [maxinLac/2 -1000 1.31 ...
maxinLac/4 -90 1.21 ...
offset slope 0 ...
maxinLac/2];
lb = [0 -4000 1.31-0.02 ...
0 -100 1.24-0.02 ...
-1 -1 -1 ...
0];
ub = [maxinLac -500 1.31+0.02 ...
maxinLac 0 1.24+0.02 ...
1 1 1 ...
maxinLac];
LacModelInit = lsqcurvefit(@LacModel, LacModelInit, freq(freqbounds), real(DIFF(ii,freqbounds)), lb, ub,lsqopts);
[LacPlusModelParam, resid] = nlinfit(freq(freqbounds), real(DIFF(ii,freqbounds)), @LacModel, LacModelInit, nlinopts);
MRS_struct.out.(vox{kk}).Lac.ModelParam(ii,:) = LacPlusModelParam;
%MMmodelParam = LacPlusModelParam;
%MMmodelParam([4 7]) = 0;
LacModelParam = LacPlusModelParam;
LacModelParam([4 10]) = 0;
BHBmodelParam = LacPlusModelParam;
BHBmodelParam(1) = 0;
MRS_struct.out.(vox{kk}).Lac.Area(ii) = sum(LacModel([LacPlusModelParam(1:3) zeros(1,7)], freq(freqbounds))) * abs(freq(1) - freq(2)); % NB: this is Lac+
modelHeight = max(LacModel([LacPlusModelParam(1:6) zeros(1,3) LacPlusModelParam(10)], freq(freqbounds)));
MRS_struct.out.(vox{kk}).Lac.FitError(ii) = 100 * std(resid) / modelHeight;
MRS_struct.out.(vox{kk}).Lac.FWHM(ii) = NaN; % MM (170818): Still need to calculate FWHM
MRS_struct.out.(vox{kk}).Lac.Resid(ii,:) = resid;
% Calculate SNR of Lac signal
noiseSigma_DIFF = CalcNoise(freq, DIFF(ii,:));
MRS_struct.out.(vox{kk}).Lac.SNR(ii) = abs(modelHeight) / noiseSigma_DIFF;
case 'Glx'
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Glx
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
freqbounds = find(freq <= 4.1 & freq >= 3.45);
plotbounds = find(freq <= 4.5 & freq >= 3);
maxinGlx = max(real(DIFF(ii,freqbounds)));
grad_points = (real(DIFF(ii,freqbounds(end))) - real(DIFF(ii,freqbounds(1)))) ./ abs(freqbounds(end) - freqbounds(1));
LinearInit = grad_points ./ abs(freq(1) - freq(2));
constInit = (real(DIFF(ii,freqbounds(end))) + real(DIFF(ii,freqbounds(1))))./2;
GaussModelInit = [maxinGlx -90 3.72 maxinGlx -90 3.77 -LinearInit constInit];
lb = [0 -200 3.72-0.01 0 -200 3.77-0.01 -40*maxinGlx -2000*maxinGlx];
ub = [4000*maxinGlx -40 3.72+0.01 4000*maxinGlx -40 3.77+0.01 40*maxinGlx 1000*maxinGlx];
GaussModelInit = lsqcurvefit(@DoubleGaussModel, GaussModelInit, freq(freqbounds), real(DIFF(ii,freqbounds)), lb, ub, lsqopts);
[GaussModelParam, resid] = nlinfit(freq(freqbounds), real(DIFF(ii,freqbounds)), @DoubleGaussModel, GaussModelInit, nlinopts);
Glxheight = max(GaussModelParam([1,4]));
MRS_struct.out.(vox{kk}).(target{jj}).FitError(ii) = 100 * std(resid) / Glxheight;
MRS_struct.out.(vox{kk}).(target{jj}).Area(ii) = (GaussModelParam(1) / sqrt(-GaussModelParam(2)) * sqrt(pi)) + ...
(GaussModelParam(4) / sqrt(-GaussModelParam(5)) * sqrt(pi));
sigma = (sqrt(1/(2*(abs(GaussModelParam(2)))))) + (sqrt(1/(2*(abs(GaussModelParam(5))))));
MRS_struct.out.(vox{kk}).(target{jj}).FWHM(ii) = abs((2*MRS_struct.p.LarmorFreq(ii)) * sigma);
MRS_struct.out.(vox{kk}).(target{jj}).ModelParam(ii,:) = GaussModelParam;
MRS_struct.out.(vox{kk}).(target{jj}).Resid(ii,:) = resid;
% Calculate SNR of Glx signal
noiseSigma_DIFF = CalcNoise(freq, DIFF(ii,:));
MRS_struct.out.(vox{kk}).Glx.SNR(ii) = abs(Glxheight) / noiseSigma_DIFF;
case 'GABAGlx'
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% GABA+Glx
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
freqbounds = find(freq <= 4.1 & freq >= 2.79);
plotbounds = find(freq <= 4.2 & freq >= 2.7);
GABAbounds = freq <= 3.2 & freq >= 2.79;
Glxbounds = freq <= 4.1 & freq >= 3.4;
maxinGABA = max(real(DIFF(ii,GABAbounds)));
maxinGlx = max(real(DIFF(ii,Glxbounds)));
grad_points = (real(DIFF(ii,freqbounds(end))) - real(DIFF(ii,freqbounds(1)))) ./ abs(freqbounds(end) - freqbounds(1));
LinearInit = grad_points ./ abs(freq(1) - freq(2));
GaussModelInit = [maxinGlx -700 3.71 maxinGlx -700 3.79 maxinGABA -90 3.02 -LinearInit 0 0];
GaussModelInit([1 4 7 10]) = GaussModelInit([1 4 7 10]) / maxinGlx; % Scale initial conditions to avoid warnings about numerical underflow
lb = [-4000*maxinGlx -1000 3.71-0.02 -4000*maxinGlx -1000 3.79-0.02 -4000*maxinGABA -200 3.02-0.05 -40*maxinGABA -2000*maxinGABA -2000*maxinGABA];
ub = [4000*maxinGlx -40 3.71+0.02 4000*maxinGlx -40 3.79+0.02 4000*maxinGABA -40 3.02+0.05 40*maxinGABA 1000*maxinGABA 1000*maxinGABA];
lb([1 4 7 10]) = lb([1 4 7 10]) / maxinGlx;
ub([1 4 7 10]) = ub([1 4 7 10]) / maxinGlx;
% Down-weight Cho subtraction artifact and (if HERMES)
% signals downfield of Glx by including observation weights
% in nonlinear regression; improves accuracy of peak
% fittings (MM: 170701 - thanks to Alex Craven of
% University of Bergen for this idea)
w = ones(size(DIFF(ii,freqbounds)));
residfreq = freq(freqbounds);
ChoRange = residfreq >= 3.16 & residfreq <= 3.285;
GlxDownfieldRange = residfreq >= 3.9 & residfreq <= 4.2;
if MRS_struct.p.HERMES && any(strcmp(MRS_struct.p.vendor, {'Philips','Philips_data','Philips_raw'}))
weightRange = ChoRange | GlxDownfieldRange;
else
weightRange = ChoRange;
end
w(weightRange) = 0.001;
% Weighted least-squares model fitting
GaussModelInit = lsqcurvefit(@GABAGlxModel, GaussModelInit, freq(freqbounds), real(DIFF(ii,freqbounds)) / maxinGlx, lb, ub, lsqopts);
modelFun_w = @(x,freq) sqrt(w) .* GABAGlxModel(x,freq); % add weights to the model
[GaussModelParam, resid] = nlinfit(freq(freqbounds), sqrt(w) .* real(DIFF(ii,freqbounds)) / maxinGlx, modelFun_w, GaussModelInit, nlinopts); % add weights to the data
[~, residPlot] = nlinfit(freq(freqbounds), real(DIFF(ii,freqbounds)) / maxinGlx, @GABAGlxModel, GaussModelParam, nlinopts); % re-run for residuals for output figure
% Rescale fit parameters and residuals
GaussModelParam([1 4 7 10 11 12]) = GaussModelParam([1 4 7 10 11 12]) * maxinGlx;
resid = resid * maxinGlx;
residPlot = residPlot * maxinGlx;
% Range to determine residuals for GABA and Glx
residGABA = resid(residfreq <= 3.55 & residfreq >= 2.79);
residGlx = resid(residfreq <= 4.10 & residfreq >= 3.45);
% GABA fitting output
MRS_struct.out.(vox{kk}).GABA.Area(ii) = GaussModelParam(7) ./ sqrt(-GaussModelParam(8)) * sqrt(pi);
GABAheight = GaussModelParam(7);
MRS_struct.out.(vox{kk}).GABA.FitError(ii) = 100 * std(residGABA) / GABAheight;
sigma = sqrt(1/(2*(abs(GaussModelParam(8)))));
MRS_struct.out.(vox{kk}).GABA.FWHM(ii) = abs((2*MRS_struct.p.LarmorFreq(ii))*sigma);
MRS_struct.out.(vox{kk}).GABA.ModelParam(ii,:) = GaussModelParam;
MRS_struct.out.(vox{kk}).GABA.Resid(ii,:) = residGABA;
% Calculate SNR of GABA signal
noiseSigma_DIFF = CalcNoise(freq, DIFF(ii,:));
MRS_struct.out.(vox{kk}).GABA.SNR(ii) = abs(GABAheight) / noiseSigma_DIFF;
% MM (200728)
%MRS_struct.out.(vox{kk}).GABA.FitError2(ii) = sqrt(mean(residGABA.^2)) / (0.5*noiseSigma_DIFF);
% Glx fitting output
MRS_struct.out.(vox{kk}).Glx.Area(ii) = (GaussModelParam(1) / sqrt(-GaussModelParam(2)) * sqrt(pi)) + ...
(GaussModelParam(4) / sqrt(-GaussModelParam(5)) * sqrt(pi));
Glxheight = max(GaussModelParam([1,4]));
MRS_struct.out.(vox{kk}).Glx.FitError(ii) = 100 * std(residGlx) / Glxheight;
sigma = sqrt(1/(2*(abs(GaussModelParam(2))))) + sqrt(1/(2*(abs(GaussModelParam(5)))));
MRS_struct.out.(vox{kk}).Glx.FWHM(ii) = abs(2 * MRS_struct.p.LarmorFreq(ii) * sigma);
MRS_struct.out.(vox{kk}).Glx.ModelParam(ii,:) = GaussModelParam;
MRS_struct.out.(vox{kk}).Glx.Resid(ii,:) = residGlx;
% Calculate SNR of Glx signal
MRS_struct.out.(vox{kk}).Glx.SNR(ii) = abs(Glxheight) / noiseSigma_DIFF;
% MM (200728)
%MRS_struct.out.(vox{kk}).Glx.FitError2(ii) = sqrt(mean(residGlx.^2)) / (0.5*noiseSigma_DIFF);
case 'EtOH'
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% EtOH
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
freqbounds = find(freq <= 1.8 & freq >= 0.6);
plotbounds = find(freq <= 1.9 & freq >= 0.4);
maxinEtOH = max(real(DIFF(ii,freqbounds)));
grad_points = (real(DIFF(ii,freqbounds(end))) - real(DIFF(ii,freqbounds(1)))) ./ abs(freqbounds(end) - freqbounds(1));
LinearInit = grad_points ./ abs(freq(1) - freq(2));
LorentzModelInit = [maxinEtOH 1.11 1/500 ...
maxinEtOH 1.23 1/500 ...
-LinearInit 0];
LorentzModelInit([1 4 7]) = LorentzModelInit([1 4 7]) / maxinEtOH; % Scale initial conditions to avoid warnings about numerical underflow
lb = [0 1.11-0.01 1/700 0 1.23-0.01 1/700 -40*maxinEtOH -2e3*maxinEtOH];
ub = [100*maxinEtOH 1.11+0.01 1/300 100*maxinEtOH 1.23+0.01 1/300 40*maxinEtOH 1e3*maxinEtOH];
lb([1 4 7]) = lb([1 4 7]) / maxinEtOH;
ub([1 4 7]) = ub([1 4 7]) / maxinEtOH;
% Down-weight co-edited Lac signal by including observation
% weights in nonlinear regression
w = ones(size(DIFF(ii,freqbounds)));
residfreq = freq(freqbounds);
LacRange = residfreq >= 1.29 & residfreq <= 1.51;
weightRange = LacRange;
w(weightRange) = 0.001;
% Weighted least-squares model fitting
LorentzModelInit = lsqcurvefit(@EtOHModel, LorentzModelInit, freq(freqbounds), real(DIFF(ii,freqbounds)) / maxinEtOH, lb, ub, lsqopts);
modelFun_w = @(x,freq) sqrt(w) .* EtOHModel(x,freq); % add weights to the model
[LorentzModelParam, resid] = nlinfit(freq(freqbounds), sqrt(w) .* real(DIFF(ii,freqbounds)) / maxinEtOH, modelFun_w, LorentzModelInit, nlinopts); % add weights to the data
[~, residPlot] = nlinfit(freq(freqbounds), real(DIFF(ii,freqbounds)) / maxinEtOH, @EtOHModel, LorentzModelParam, nlinopts); % re-run for residuals for output figure
% Rescale fit parameters and residuals
LorentzModelParam([1 4 7 8]) = LorentzModelParam([1 4 7 8]) * maxinEtOH;
resid = resid * maxinEtOH;
residPlot = residPlot * maxinEtOH;
EtOHheight = max(EtOHModel([LorentzModelParam(1:end-2) 0 0],freq(freqbounds)));
MRS_struct.out.(vox{kk}).(target{jj}).FitError(ii) = 100 * std(resid) / EtOHheight;
MRS_struct.out.(vox{kk}).(target{jj}).Area(ii) = sum(EtOHModel([LorentzModelParam(1:end-2) 0 0],freq(freqbounds))) * abs(freq(1) - freq(2));
MRS_struct.out.(vox{kk}).(target{jj}).FWHM(ii) = (LorentzModelParam(3) + LorentzModelParam(6)) * MRS_struct.p.LarmorFreq(ii);
MRS_struct.out.(vox{kk}).(target{jj}).ModelParam(ii,:) = LorentzModelParam;
MRS_struct.out.(vox{kk}).(target{jj}).Resid(ii,:) = resid;
% Calculate SNR of EtOH signal
noiseSigma_DIFF = CalcNoise(freq, DIFF(ii,:));
MRS_struct.out.(vox{kk}).EtOH.SNR(ii) = abs(EtOHheight) / noiseSigma_DIFF;
otherwise
error('Metabolite ''%s'' not recognized.', target{jj});
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 1a. Initialize the output figure
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if ishandle(102)
clf(102);
end
if MRS_struct.p.hide
h = figure('Visible', 'off');
else
h = figure(102);
end
% Open figure in center of screen
scr_sz = get(0,'ScreenSize');
fig_w = 1000;
fig_h = 707;
set(h,'Position',[(scr_sz(3)-fig_w)/2, (scr_sz(4)-fig_h)/2, fig_w, fig_h]);
set(h,'Color',[1 1 1]);
figTitle = 'GannetFit Output';
set(h,'Name',figTitle,'Tag',figTitle,'NumberTitle','off');
% Spectra plot
subplot(2,2,1);
metabmin = min(real(DIFF(ii,plotbounds)));
metabmax = max(real(DIFF(ii,plotbounds)));
resmax = max(resid);
resid = resid + metabmin - resmax;
switch target{jj}
case 'GABA'
residPlot = residPlot + metabmin - max(residPlot);
residPlot2 = residPlot;
residPlot2(weightRange) = NaN;
hold on;
plot(freq(plotbounds), real(DIFF(ii,plotbounds)), 'b', ...
freq(freqbounds), GaussModel(GaussModelParam,freq(freqbounds)), 'r', ...
freq(freqbounds), residPlot2, 'k');
plot(freq(freqbounds(ChoRange)), residPlot(ChoRange), 'Color', [255 160 64]/255);
hold off;
set(gca,'XLim',[2.6 3.6]);
case 'GSH'
residPlot = residPlot + metabmin - max(residPlot);
residPlot2 = residPlot;
if length(MRS_struct.p.target) == 3 && all(ismember(MRS_struct.p.target,{'EtOH','GABA','GSH'}))
residPlot2(weightRange) = NaN;
hold on;
plot(freq(plotbounds), real(DIFF(ii,plotbounds)), 'b' , ...
freq(freqbounds), GSHgaussModel(GaussModelParam,freq(freqbounds)), 'r', ...
freq(freqbounds), residPlot2, 'k');
plot(freq(freqbounds(ChoRange)), residPlot(ChoRange), 'Color', [255 160 64]/255);
hold off;
else
plot(freq(plotbounds), real(DIFF(ii,plotbounds)), 'b' , ...
freq(freqbounds), GSHgaussModel(GaussModelParam,freq(freqbounds)), 'r', ...
freq(freqbounds), residPlot2, 'k');
end
set(gca, 'XLim', [1.8 4.2]);
case 'Lac'
hold on;
p1 = plot(freq(plotbounds), real(DIFF(ii,plotbounds)), 'k');
p2 = plot(freq(freqbounds), LacModel(BHBmodelParam,freq(freqbounds)), 'Color', [31 120 180]/255, 'LineWidth', 1);
p3 = plot(freq(freqbounds), LacModel(LacModelParam,freq(freqbounds)), 'Color', [51 160 44]/255, 'LineWidth', 1);
p4 = plot(freq(freqbounds), LacModel(LacPlusModelParam,freq(freqbounds)), 'r', 'LineWidth', 1);
p5 = plot(freq(freqbounds), resid, 'k');
hold off;
set(gca, 'XLim', [0.7 1.9], 'XTick', 0:0.25:10);
case 'Glx'
plot(freq(plotbounds), real(DIFF(ii,plotbounds)), 'b', ...
freq(freqbounds), DoubleGaussModel(GaussModelParam,freq(freqbounds)), 'r', ...
freq(freqbounds), resid, 'k');
set(gca,'XLim',[3.4 4.2]);
case 'GABAGlx'
residPlot = residPlot + metabmin - max(residPlot);
residPlot2 = residPlot;
residPlot2(weightRange) = NaN;
hold on;
plot(freq(plotbounds), real(DIFF(ii,plotbounds)), 'b', ...
freq(freqbounds), GABAGlxModel(GaussModelParam,freq(freqbounds)), 'r', ...
freq(freqbounds), residPlot2, 'k');
% Plot weighted portion of residuals in different color
if MRS_struct.p.HERMES && any(strcmp(MRS_struct.p.vendor,{'Philips','Philips_data','Philips_raw'}))
plot(freq(freqbounds(ChoRange)), residPlot(ChoRange), 'Color', [255 160 64]/255);
plot(freq(freqbounds(GlxDownfieldRange)), residPlot(GlxDownfieldRange), 'Color', [255 160 64]/255);
else
plot(freq(freqbounds(ChoRange)), residPlot(ChoRange), 'Color', [255 160 64]/255);
end
hold off;
set(gca,'XLim',[2.7 4.2]);
case 'EtOH'
residPlot = residPlot + metabmin - max(residPlot);
residPlot2 = residPlot;
residPlot2(weightRange) = NaN;
hold on;
plot(freq(plotbounds), real(DIFF(ii,plotbounds)), 'b', ...
freq(freqbounds), EtOHModel(LorentzModelParam,freq(freqbounds)), 'r', ...
freq(freqbounds), residPlot2, 'k');
plot(freq(freqbounds(LacRange)), residPlot(LacRange), 'Color', [255 160 64]/255);
hold off;
set(gca,'XLim',[0.4 1.9]);
end
% From here on is cosmetic - adding labels etc.
switch target{jj}
case 'GABA'
text(3, metabmax/4, target{jj}, 'HorizontalAlignment', 'center');
labelbounds = freq <= 2.4 & freq >= 2;
tailtop = max(real(DIFF(ii,labelbounds)));
tailbottom = min(real(DIFF(ii,labelbounds)));
text(2.8, min(resid), 'residual', 'HorizontalAlignment', 'left');
text(2.8, tailtop + metabmax/20, 'data', 'Color', [0 0 1]);
text(2.8, tailbottom - metabmax/20, 'model', 'Color', [1 0 0]);
if length(MRS_struct.p.target) ~= 3
text(3.2, min(residPlot) - 0.5*abs(max(residPlot)), 'down-weighted', 'Color', [255 160 64]/255, 'HorizontalAlignment', 'center');
end
case 'GSH'
text(2.95, maxinGSH+maxinGSH/4, target{jj}, 'HorizontalAlignment', 'center');
labelbounds = freq <= 2.4 & freq >= 1.75;
tailtop = max(real(DIFF(ii,labelbounds)));
tailbottom = min(real(DIFF(ii,labelbounds)));
text(2.25, min(resid), 'residual', 'HorizontalAlignment', 'left');
text(2.25, tailtop + metabmax/20, 'data', 'Color', [0 0 1]);
text(2.45, tailbottom - 20*metabmax/20, 'model', 'Color', [1 0 0]);
if length(MRS_struct.p.target) == 3 && all(ismember(MRS_struct.p.target,{'EtOH','GABA','GSH'}))
text(3.3, max(residPlot) + 0.5*abs(max(residPlot) - min(residPlot)), 'down-weighted', 'Color', [255 160 64]/255, 'HorizontalAlignment', 'right');
end
case 'Lac'
% text(1.27, metabmax/3.5, 'Lac+', 'HorizontalAlignment', 'center');
% labelbounds = freq <= 0.8 & freq >= 0.42;
% tailtop = max(real(DIFF(ii,labelbounds)));
% tailbottom = min(real(DIFF(ii,labelbounds)));
% text(0.44, mean(real(DIFF(ii,labelbounds))) + 4*std(real(DIFF(ii,labelbounds))), 'data', 'HorizontalAlignment', 'right');
% text(0.44, mean(resid) - 4*std(resid), 'residual', 'HorizontalAlignment', 'right');
% text(0.85, tailbottom, 'model', 'Color', [1 0 0]);
legend([p1, p2, p3, p4, p5], {'data','BHB+','Lac+','model','residual'}, 'box', 'off', 'Location', 'northeast');
case 'Glx'
text(3.8, metabmax/4, target{jj}, 'HorizontalAlignment', 'center');
labelbounds = freq <= 3.6 & freq >= 3.4;
tailtop = max(real(DIFF(ii,labelbounds)));
tailbottom = min(real(DIFF(ii,labelbounds)));
text(3.5, min(resid),'residual', 'HorizontalAlignment', 'left');
text(3.5, tailtop + metabmax/20, 'data', 'Color', [0 0 1]);
text(3.5, tailbottom - metabmax/20, 'model', 'Color', [1 0 0]);
case 'GABAGlx'
text(3, metabmax/4, 'GABA', 'HorizontalAlignment', 'center');
text(3.755, metabmax/4, 'Glx', 'HorizontalAlignment', 'center');
text(2.8, min(resid), 'residual', 'HorizontalAlignment', 'left');
labelbounds = freq <= 2.8 & freq >= 2.7;
tailtop = max(real(DIFF(ii,labelbounds)));
tailbottom = min(real(DIFF(ii,labelbounds)));
text(2.8, tailtop + metabmax/20, 'data', 'Color', [0 0 1]);
text(2.8, tailbottom - metabmax/20, 'model', 'Color', [1 0 0]);
text(3.2, min(residPlot) - 0.5*abs(max(residPlot)), 'down-weighted', 'Color', [255 160 64]/255, 'HorizontalAlignment', 'center');
case 'EtOH'
text(1.45, metabmax/4, target{jj}, 'HorizontalAlignment', 'center');
labelbounds = freq <= 0.9 & freq >= 0.5;
tailtop = max(real(DIFF(ii,labelbounds)));
tailbottom = min(real(DIFF(ii,labelbounds)));
text(0.6, min(resid),'residual', 'HorizontalAlignment', 'left');
text(0.8, tailtop + metabmax/20, 'data', 'Color', [0 0 1]);
text(0.8, tailbottom - metabmax/20, 'model', 'Color', [1 0 0]);
text(1.4, max(residPlot2) + 0.5*abs(max(residPlot2)), 'down-weighted', 'Color', [255 160 64]/255, 'HorizontalAlignment', 'center');
end
title('Difference spectrum and model fit');
xlabel('ppm');
set(gca,'XDir','reverse','TickDir','out','Box','off');
set(get(gca,'YAxis'),'Visible','off');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 2. Water Fit
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if strcmp(MRS_struct.p.reference,'H2O')
% Estimate height and baseline from data
[maxinWater, watermaxindex] = max(real(WaterData(ii,:)),[],2);
waterbase = mean(real(WaterData(ii,freq <= 4 & freq >= 3.8)));
% Philips data do not phase well based on first point, so do a preliminary
% fit, then adjust phase of WaterData accordingly
% Do this only if the eddy current correction is not performed on water
if strcmp(MRS_struct.p.vendor,'Philips') && ~MRS_struct.p.water_ECC
% Run preliminary fit of data
LGModelInit = [maxinWater 20 freq(watermaxindex) 0 waterbase -50];
freqbounds = freq <= 5.6 & freq >= 3.8;
% Do the water fit (Lorentz-Gauss)
LGModelParam = nlinfit(freq(freqbounds), real(WaterData(ii,freqbounds)), @LorentzGaussModel, LGModelInit, nlinopts);
Eerror = zeros([120 1]);
for ll = 1:120
Data = WaterData(ii,freqbounds)*exp(1i*pi/180*ll*3);
Model = LorentzGaussModel(LGModelParam,freq(freqbounds));
Eerror(ll) = sum((real(Data)-Model).^2);
end
[~,index] = min(Eerror);
WaterData(ii,:) = WaterData(ii,:) * exp(1i*pi/180*index*3);
end
LGPModelInit = [maxinWater 20 freq(watermaxindex) 0 waterbase -50 0];
lb = [0.01*maxinWater 1 4.6 0 0 -50 -pi];
ub = [40*maxinWater 100 4.8 1e-6 1 0 pi];
freqbounds = freq <= 5.6 & freq >= 3.8;
% Least-squares model fitting
LGPModelInit = lsqcurvefit(@LorentzGaussModelP, LGPModelInit, freq(freqbounds), real(WaterData(ii,freqbounds)), lb, ub, lsqopts);
[LGPModelParam, residw] = nlinfit(freq(freqbounds), real(WaterData(ii,freqbounds)), @LorentzGaussModelP, LGPModelInit, nlinopts);
WaterArea = sum(real(LorentzGaussModel(LGPModelParam(1:end-1), freq(freqbounds))) - BaselineModel(LGPModelParam(3:5), freq(freqbounds)),2);
MRS_struct.out.(vox{kk}).water.Area(ii) = WaterArea * abs(freq(1) - freq(2));
waterheight = LGPModelParam(1);
MRS_struct.out.(vox{kk}).water.FitError(ii) = 100 * std(residw) / waterheight;
LG = real(LorentzGaussModel(LGPModelParam(1:end-1), freq(freqbounds))) - BaselineModel(LGPModelParam(3:5), freq(freqbounds));
LG = LG/max(LG);
ind = find(LG >= 0.5);
f = freq(freqbounds);
w = abs(f(ind(1)) - f(ind(end)));
MRS_struct.out.(vox{kk}).water.FWHM(ii) = w * MRS_struct.p.LarmorFreq(ii);
MRS_struct.out.(vox{kk}).water.ModelParam(ii,:) = LGPModelParam;
MRS_struct.out.(vox{kk}).water.Resid(ii,:) = residw;
% Calculate SNR of water signal
noiseSigma_Water = CalcNoise(freq, WaterData(ii,:));
MRS_struct.out.(vox{kk}).water.SNR(ii) = abs(waterheight) / noiseSigma_Water;
% Water spectrum plot
hb = subplot(2,2,3);
watmin = min(real(WaterData(ii,:)));
watmax = max(real(WaterData(ii,:)));
residw = residw + watmin - max(residw);
plot(freq(freqbounds), real(WaterData(ii,freqbounds)), 'b', ...
freq(freqbounds), real(LorentzGaussModelP(LGPModelParam,freq(freqbounds))), 'r', ...
freq(freqbounds), residw, 'k');
set(gca,'XDir','reverse','TickDir','out','Box','off','XTick',4.2:0.2:5.2);
xlim([4.2 5.2]);
set(get(gca,'YAxis'),'Visible','off');
% Add on some labels
text(4.8, watmax/2, 'Water', 'HorizontalAlignment', 'right');
labelfreq = freq(freqbounds);
rlabelbounds = labelfreq <= 4.4 & labelfreq >= 4.25;
axis_bottom = axis;
text(4.4, max(min(residw(rlabelbounds)) - 0.05 * watmax, axis_bottom(3)), 'residual', 'HorizontalAlignment', 'left');
% Root sum square fit error and concentration in institutional units
switch target{jj}
case 'GABA'
MRS_struct.out.(vox{kk}).GABA.FitError_W(ii) = sqrt(MRS_struct.out.(vox{kk}).GABA.FitError(ii).^2 + MRS_struct.out.(vox{kk}).water.FitError(ii).^2);
MRS_struct = CalcIU(MRS_struct, vox{kk}, 'GABA', ii);
case 'Glx'
MRS_struct.out.(vox{kk}).Glx.FitError_W(ii) = sqrt(MRS_struct.out.(vox{kk}).Glx.FitError(ii).^2 + MRS_struct.out.(vox{kk}).water.FitError(ii).^2);
MRS_struct = CalcIU(MRS_struct, vox{kk}, 'Glx', ii);
case 'GABAGlx'
MRS_struct.out.(vox{kk}).GABA.FitError_W(ii) = sqrt(MRS_struct.out.(vox{kk}).GABA.FitError(ii).^2 + MRS_struct.out.(vox{kk}).water.FitError(ii).^2);
MRS_struct.out.(vox{kk}).Glx.FitError_W(ii) = sqrt(MRS_struct.out.(vox{kk}).Glx.FitError(ii).^2 + MRS_struct.out.(vox{kk}).water.FitError(ii).^2);
MRS_struct = CalcIU(MRS_struct, vox{kk}, 'GABA', ii);
MRS_struct = CalcIU(MRS_struct, vox{kk}, 'Glx', ii);
case 'GSH'
MRS_struct.out.(vox{kk}).GSH.FitError_W(ii) = sqrt(MRS_struct.out.(vox{kk}).GSH.FitError(ii).^2 + MRS_struct.out.(vox{kk}).water.FitError(ii).^2);
MRS_struct = CalcIU(MRS_struct, vox{kk}, (target{jj}), ii);
case 'Lac'
MRS_struct.out.(vox{kk}).Lac.FitError_W(ii) = sqrt(MRS_struct.out.(vox{kk}).Lac.FitError(ii).^2 + MRS_struct.out.(vox{kk}).water.FitError(ii).^2);
MRS_struct = CalcIU(MRS_struct, vox{kk}, (target{jj}), ii);
case 'EtOH'
MRS_struct.out.(vox{kk}).EtOH.FitError_W(ii) = sqrt(MRS_struct.out.(vox{kk}).EtOH.FitError(ii).^2 + MRS_struct.out.(vox{kk}).water.FitError(ii).^2);
MRS_struct = CalcIU(MRS_struct, vox{kk}, (target{jj}), ii);
end
% Generate scaled spectra (for plotting)
MRS_struct.spec.(vox{kk}).(target{jj}).off_scaled(ii,:) = ...
MRS_struct.spec.(vox{kk}).(target{jj}).off(ii,:) .* (1/MRS_struct.out.(vox{kk}).water.ModelParam(ii,1));
if MRS_struct.p.HERMES
MRS_struct.spec.(vox{kk}).(target{jj}).off_off_scaled(ii,:) = ...
MRS_struct.spec.(vox{kk}).(target{jj}).off_off(ii,:) .* (1/MRS_struct.out.(vox{kk}).water.ModelParam(ii,1));
end
MRS_struct.spec.(vox{kk}).(target{jj}).on_scaled(ii,:) = ...
MRS_struct.spec.(vox{kk}).(target{jj}).on(ii,:) .* (1/MRS_struct.out.(vox{kk}).water.ModelParam(ii,1));
MRS_struct.spec.(vox{kk}).(target{jj}).diff_scaled(ii,:) = ...
MRS_struct.spec.(vox{kk}).(target{jj}).diff(ii,:) .* (1/MRS_struct.out.(vox{kk}).water.ModelParam(ii,1));
MRS_struct.spec.(vox{kk}).(target{jj}).sum_scaled(ii,:) = ...
MRS_struct.spec.(vox{kk}).(target{jj}).sum(ii,:) .* (1/MRS_struct.out.(vox{kk}).water.ModelParam(ii,1));
% Reorder structure fields
MRS_struct.out.(vox{kk}).water = orderfields(MRS_struct.out.(vox{kk}).water, {'ModelParam', 'Resid', 'Area', 'FWHM', 'SNR', 'FitError'});
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 2a. Residual Water Fit
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if MRS_struct.p.fit_resid_water
residWater = OFF(ii,:);
freqWater = freq <= 4.68 + 0.4 & freq >= 4.68 - 0.4;
residWater = residWater(freqWater);
freqWater = freq(freqWater);
[maxResidWater, maxInd] = max(abs(real(residWater)));
s = sign(real(residWater(maxInd)));
maxResidWater = s * maxResidWater;
offset = real(residWater(1));
LGPModelInit = [maxResidWater 25 freqWater(maxInd) 0 offset 0.001 0];
LGPModelInit([1 5]) = LGPModelInit([1 5]) / maxResidWater; % Scale initial conditions to avoid warnings about numerical underflow
%lb = [maxResidWater-abs(2*maxResidWater) 1 freqWaterOFF(maxInd)-0.2 0 0 -200 -pi];
%ub = [maxResidWater+abs(2*maxResidWater) 100 freqWaterOFF(maxInd)+0.2 0.000001 1 0 pi];
%lb([1 4 5]) = lb([1 4 5]) / maxResidWater;
%ub([1 4 5]) = ub([1 4 5]) / maxResidWater;
% Least-squares model fitting
%LGPModelInit = lsqcurvefit(@LorentzGaussModelP, LGPModelInit, freqWaterOFF, real(water_OFF) / maxResidWater, lb, ub, lsqopts);
[MRS_struct.out.(vox{kk}).ResidWater.ModelParam(ii,:), residRW] = nlinfit(freqWater, real(residWater) / maxResidWater, @LorentzGaussModelP, LGPModelInit, nlinopts);
% Rescale fit parameters and residuals
MRS_struct.out.(vox{kk}).ResidWater.ModelParam(ii,[1 4 5]) = MRS_struct.out.(vox{kk}).ResidWater.ModelParam(ii,[1 4 5]) * maxResidWater;
residRW = residRW * maxResidWater;
MRS_struct.out.(vox{kk}).ResidWater.FitError(ii) = 100*std(residRW) / MRS_struct.out.(vox{kk}).ResidWater.ModelParam(ii,1);
MRS_struct.out.(vox{kk}).ResidWater.SuppressionFactor(ii) = ...
(MRS_struct.out.(vox{kk}).water.ModelParam(ii,1) - abs(MRS_struct.out.(vox{kk}).ResidWater.ModelParam(ii,1))) ...
/ MRS_struct.out.(vox{kk}).water.ModelParam(ii,1);
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 3. Cr Fit
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Cr_OFF = OFF(ii,:);
freqboundsChoCr = freq <= 3.6 & freq >= 2.6;
ChoCrMeanSpec = Cr_OFF(freqboundsChoCr).';
Baseline_offset = real(ChoCrMeanSpec(1) + ChoCrMeanSpec(end)) / 2;
Width_estimate = 0.05;
Area_estimate = (max(real(ChoCrMeanSpec)) - min(real(ChoCrMeanSpec))) * Width_estimate * 4;
ChoCr_initx = [Area_estimate Width_estimate 3.02 0 Baseline_offset 0 1] ...
.* [1 2*MRS_struct.p.LarmorFreq(ii) MRS_struct.p.LarmorFreq(ii) 180/pi 1 1 1];
[ChoCrModelParam, ~, residChoCr] = FitChoCr(freq(freqboundsChoCr), ChoCrMeanSpec, ChoCr_initx, MRS_struct.p.LarmorFreq(ii));
MRS_struct.out.(vox{kk}).ChoCr.ModelParam(ii,:) = ChoCrModelParam ./ [1 2*MRS_struct.p.LarmorFreq(ii) MRS_struct.p.LarmorFreq(ii) 180/pi 1 1 1];
% Initialise fitting pars
freqboundsCr = freq <= 3.12 & freq >= 2.72;
LorentzModelInit = [max(real(Cr_OFF(freqboundsCr))) 0.05 3.0 0 0 0];
% Least-squares model fitting
LorentzModelInit = lsqcurvefit(@LorentzModel, LorentzModelInit, freq(freqboundsCr), real(Cr_OFF(freqboundsCr)), [], [], lsqopts);
[LorentzModelParam, residCr] = nlinfit(freq(freqboundsCr), real(Cr_OFF(freqboundsCr)), @LorentzModel, LorentzModelInit, nlinopts);
MRS_struct.out.(vox{kk}).Cr.ModelParam(ii,:) = LorentzModelParam;
CrHeight = LorentzModelParam(1) / (2*pi*LorentzModelParam(2));
MRS_struct.out.(vox{kk}).Cr.FitError(ii) = 100 * std(residCr) / CrHeight;
MRS_struct.out.(vox{kk}).Cr.Resid(ii,:) = residCr;
MRS_struct.out.(vox{kk}).Cho.ModelParam(ii,:) = MRS_struct.out.(vox{kk}).ChoCr.ModelParam(ii,:);
ChoHeight = (MRS_struct.out.(vox{kk}).ChoCr.ModelParam(ii,1) * MRS_struct.out.(vox{kk}).ChoCr.ModelParam(ii,7)) / (2*pi*MRS_struct.out.(vox{kk}).ChoCr.ModelParam(ii,2));
MRS_struct.out.(vox{kk}).Cho.FitError(ii) = 100 * std(residChoCr) / ChoHeight;
MRS_struct.out.(vox{kk}).Cho.Resid(ii,:) = residChoCr;
MRS_struct.out.(vox{kk}).Cr.Area(ii) = sum(real(TwoLorentzModel([MRS_struct.out.(vox{kk}).ChoCr.ModelParam(ii,1:end-1) 0], freq(freqboundsChoCr)) - ...
TwoLorentzModel([0 MRS_struct.out.(vox{kk}).ChoCr.ModelParam(ii,2:end-1) 0], freq(freqboundsChoCr)))) * abs(freq(1) - freq(2));
MRS_struct.out.(vox{kk}).Cho.Area(ii) = sum(real(TwoLorentzModel(MRS_struct.out.(vox{kk}).ChoCr.ModelParam(ii,:), freq(freqboundsChoCr)) - ...
TwoLorentzModel([MRS_struct.out.(vox{kk}).ChoCr.ModelParam(ii,1:end-1) 0], freq(freqboundsChoCr)))) * abs(freq(1) - freq(2));
MRS_struct.out.(vox{kk}).Cr.FWHM(ii) = ChoCrModelParam(2);
MRS_struct.out.(vox{kk}).Cho.FWHM(ii) = ChoCrModelParam(2);
% Calculate SNR of Cr signal
noiseSigma_OFF = CalcNoise(freq, OFF(ii,:));
MRS_struct.out.(vox{kk}).Cr.SNR(ii) = abs(CrHeight) / noiseSigma_OFF;
MRS_struct.out.(vox{kk}).Cho.SNR(ii) = abs(ChoHeight) / noiseSigma_OFF;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 4. NAA Fit
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
NAA_OFF = OFF(ii,:);
freqbounds = find(freq <= 2.25 & freq >= 1.75);
maxinNAA = max(real(NAA_OFF(freqbounds)));
grad_points = (real(NAA_OFF(freqbounds(end))) - real(NAA_OFF(freqbounds(1)))) ./ abs(freqbounds(end) - freqbounds(1)); %in points
LinearInit = grad_points ./ abs(freq(1) - freq(2));
constInit = (real(NAA_OFF(freqbounds(end))) + real(NAA_OFF(freqbounds(1)))) ./ 2;
LorentzModelInit = [maxinNAA 0.05 2.01 0 -LinearInit constInit];
lb = [0 0.01 1.97 0 -40*maxinNAA -2000*maxinNAA];
ub = [4000*maxinNAA 0.1 2.05 0.5 40*maxinNAA 1000*maxinNAA];
% Least-squares model fitting
LorentzModelInit = lsqcurvefit(@LorentzModel, LorentzModelInit, freq(freqbounds), real(NAA_OFF(freqbounds)), lb, ub, lsqopts);
[LorentzModelParam, resid] = nlinfit(freq(freqbounds), real(NAA_OFF(freqbounds)), @LorentzModel, LorentzModelInit, nlinopts);
NAAheight = LorentzModelParam(1) / (2*pi*LorentzModelParam(2));
MRS_struct.out.(vox{kk}).NAA.FitError(ii) = 100 * std(resid) / NAAheight;
NAAModelParam = LorentzModelParam;
NAAModelParam(4) = 0;
MRS_struct.out.(vox{kk}).NAA.Area(ii) = sum(LorentzModel(NAAModelParam,freq(freqbounds)) - BaselineModel(NAAModelParam([3 6 5]),freq(freqbounds)), 2) * abs(freq(1) - freq(2));
MRS_struct.out.(vox{kk}).NAA.FWHM(ii) = abs((2*MRS_struct.p.LarmorFreq(ii)) * NAAModelParam(2));
MRS_struct.out.(vox{kk}).NAA.ModelParam(ii,:) = LorentzModelParam;
MRS_struct.out.(vox{kk}).NAA.Resid(ii,:) = resid;
% Calculate SNR of NAA signal
MRS_struct.out.(vox{kk}).NAA.SNR(ii) = abs(NAAheight) / noiseSigma_OFF;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 5. Glu Fit MM (240328): This still needs testing to see if it's reliable
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%