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Merge pull request #17 from markmikkelsen/dev
Update to v3.3.1
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,11 +1,16 @@ | ||
| function [output MRS_struct] = AlignUsingH2O(input,MRS_struct) | ||
| %Align to water maximum (top-right plot) | ||
| A=size(input) | ||
| [number index] = max(abs(input),[],2); | ||
| index=index-A(1)/2; | ||
| for ii=1:A(2) | ||
| output(:,ii)=circshift(input(:,ii),[-A 0]); | ||
| function [output, MRS_struct] = AlignUsingH2O(input, MRS_struct) | ||
| % Align to residual water magnitude | ||
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| A = size(input); | ||
| [~, index] = max(abs(input),[],1); | ||
| index = index - A(1)/2; | ||
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| for ii = 1:A(2) | ||
| output(:,ii) = circshift(input(:,ii), -index(ii), 1); %#ok<AGROW> | ||
| end | ||
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| MRS_struct.out.reject(:,MRS_struct.ii) = zeros(A(2),1); | ||
| end | ||
| MRS_struct.out.reject{MRS_struct.ii} = zeros(1,A(2)); | ||
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| fprintf('\n'); | ||
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| end |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,4 @@ | ||
| function F = BaselineModel(x, freq) | ||
| % Function for baseline model | ||
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| F = x(2) * (freq - x(1)) + x(3); |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,105 @@ | ||
| function MRS_struct = CalcIU(MRS_struct, vox, metab, ii) | ||
| % Function for quantifying concentration in institutional units | ||
| % Convert metabolite and water areas to institutional units | ||
| % (pseudo-concentration in mmol/L) | ||
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| TR = MRS_struct.p.TR(ii)/1e3; | ||
| TE = MRS_struct.p.TE(ii)/1e3; | ||
| if isfield(MRS_struct.p,'TR_water') | ||
| TR_water = MRS_struct.p.TR_water(ii)/1e3; | ||
| else | ||
| TR_water = TR; | ||
| end | ||
| if isfield(MRS_struct.p,'TE_water') | ||
| TE_water = MRS_struct.p.TE_water(ii)/1e3; | ||
| else | ||
| TE_water = TE; | ||
| end | ||
| PureWaterConc = 55.51*1e3; % mol/kg | ||
| WaterVisibility = 0.65; % this is approx the value from Ernst, Kreis, Ross (1993, JMR) | ||
| T1_Water = 1.100; % average of WM and GM, Wansapura et al. 1999 (JMRI) | ||
| T2_Water = 0.095; % average of WM and GM, Wansapura et al. 1999 (JMRI) | ||
| N_H_Water = 2; | ||
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| switch metab | ||
| case 'GABA' | ||
| EditingEfficiency = 0.5; % For TE = 68 ms | ||
| T1_Metab = 1.31; % Puts et al. 2013 (JMRI) | ||
| T2_Metab = 0.088; % Edden et al. 2012 (JMRI) | ||
| N_H_Metab = 2; | ||
| MM = 0.45; % MM correction: fraction of GABA in GABA+ peak. (In TrypDep, 30 subjects: 55% of GABA+ was MM) | ||
| % This fraction is platform- and implementation-dependent, based on length and | ||
| % shape of editing pulses and ifis Henry method | ||
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| case 'Glx' | ||
| EditingEfficiency = 0.4; % determined by FID-A simulations (for TE = 68 ms) | ||
| T1_Metab = 1.23; % Posse et al. 2007 (MRM) | ||
| T2_Metab = 0.18; % Ganji et al. 2012 (NMR Biomed) | ||
| N_H_Metab = 1; | ||
| MM = 1; | ||
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| case 'GSH' | ||
| EditingEfficiency = 0.74; % At 3T based on Quantification of Glutathione in the Human Brain by MR Spectroscopy at 3 Tesla: | ||
| % Comparison of PRESS and MEGA-PRESS | ||
| % Faezeh Sanaei Nezhad etal. DOI 10.1002/mrm.26532, 2016 | ||
| T1_Metab = 0.40; % At 3T based on Doubly selective multiple quantum chemical shift imaging and | ||
| % T1 relaxation time measurement of glutathione (GSH) in the human brain in vivo | ||
| % In-Young Choi et al. NMR Biomed. 2013; 26: 28-34 | ||
| T2_Metab = 0.12; % At 3T based on the ISMRM abstract | ||
| % T2 relaxation times of 18 brain metabolites determined in 83 healthy volunteers in vivo | ||
| % Milan Scheidegger et al. Proc. Intl. Soc. Mag. Reson. Med. 22 (2014) | ||
| N_H_Metab = 2; | ||
| MM = 1; | ||
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| case 'Lac' | ||
| EditingEfficiency = 0.94; % determined by FID-A simulations (for TE = 140 ms) | ||
| T1_Metab = 1.50; % Wijnen et al. 2015 (NMR Biomed) | ||
| T2_Metab = 0.24; % Madan et al. 2015 (MRM) (NB: this was estimated in brain tumors) | ||
| N_H_Metab = 3; | ||
| MM = 1; | ||
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| case 'EtOH' | ||
| EditingEfficiency = 0.5; % assuming same as GABA for now | ||
| T1_Metab = 1.31; % assuming same as GABA | ||
| T2_Metab = 0.088; % assuming same as GABA | ||
| N_H_Metab = 3; | ||
| MM = 1; | ||
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| case 'Cr' % 3 ppm moiety | ||
| EditingEfficiency = 1; % not edited, so 1 | ||
| T1_Metab = (1.46 + 1.24)/2; % Mlynárik et al. 2001 (NMR in Biomed) | ||
| T2_Metab = (166 + 144 + 148)/3/1e3; % Wyss et al. 2018 (MRM) | ||
| N_H_Metab = 3; | ||
| MM = 1; | ||
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| case 'Cho' % 3.2 ppm moiety | ||
| EditingEfficiency = 1; % not edited, so 1 | ||
| T1_Metab = (1.30 + 1.08)/2; % Mlynárik et al. 2001 (NMR in Biomed) | ||
| T2_Metab = (218 + 222 + 274)/3/1e3; % Wyss et al. 2018 (MRM) | ||
| N_H_Metab = 9; | ||
| MM = 1; | ||
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| case 'NAA' % 2 ppm moiety | ||
| EditingEfficiency = 1; % not edited, so 1 | ||
| T1_Metab = (1.47 + 1.35)/2; % Mlynárik et al. 2001 (NMR in Biomed) | ||
| T2_Metab = (343 + 263 + 253)/3/1e3; % Wyss et al. 2018 (MRM) | ||
| N_H_Metab = 3; | ||
| MM = 1; | ||
| end | ||
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| T1_Factor = (1 - exp(-TR_water./T1_Water)) ./ (1 - exp(-TR./T1_Metab)); | ||
| T2_Factor = exp(-TE_water./T2_Water) ./ exp(-TE./T2_Metab); | ||
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| if strcmp(MRS_struct.p.vendor, 'Siemens_rda') | ||
| % Factor of 2 is appropriate for averaged Siemens data (read in separately as ON and OFF) | ||
| MRS_struct.out.(vox).(metab).ConcIU(ii) = (MRS_struct.out.(vox).(metab).Area(ii) ./ MRS_struct.out.(vox).water.Area(ii)) ... | ||
| .* PureWaterConc .* WaterVisibility .* T1_Factor .* T2_Factor .* (N_H_Water ./ N_H_Metab) ... | ||
| .* MM ./ 2 ./ EditingEfficiency; | ||
| else | ||
| MRS_struct.out.(vox).(metab).ConcIU(ii) = (MRS_struct.out.(vox).(metab).Area(ii) ./ MRS_struct.out.(vox).water.Area(ii)) ... | ||
| .* PureWaterConc .* WaterVisibility .* T1_Factor .* T2_Factor .* (N_H_Water ./ N_H_Metab) ... | ||
| .* MM ./ EditingEfficiency; | ||
| end | ||
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