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EIT_EIDORS_Model_FER.m
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function EIT_EIDORS_Model_FER()
%% Make folder and set filePath
if ~exist('.\EIT_EIDORS_Model_FER','dir')
mkdir('.\EIT_EIDORS_Model_FER');
end
EIT_FER_Filepath = '.\EIT_EIDORS_Model_FER';
if ~exist('.\EIT_EIDORS_Model_FER_Voltage','dir')
mkdir('.\EIT_EIDORS_Model_FER_Voltage');
end
EIT_V_Filepath = '.\EIT_EIDORS_Model_FER_Voltage';
if ~exist('.\EIT_EIDORS_Model_FER_CP','dir')
mkdir('.\EIT_EIDORS_Model_FER_CP');
end
EIT_CP_Filepath = '.\EIT_EIDORS_Model_FER_CP';
%% Get Contours
thorax = shape_library('get','adult_male','boundary');
rlung = shape_library('get','adult_male','right_lung');
llung = shape_library('get','adult_male','left_lung');
% one could also run:
% shape_library('get','adult_male');
% to get all the info at once in a struct
% show the library image
% figure; shape_library('show','adult_male');
% print_convert thoraxmdl01a.jpg '-density 100'
shape = { 0, % height
{thorax, rlung, llung}, % contours
[4,50], % perform smoothing with 50 points
0.04}; % small maxh (fine mesh)
elec_pos = [ 16, % number of elecs per plane
1, % equidistant spacing
0]'; % a single z-plane
elec_shape = [0.05, % radius
0, % circular electrode
0.01 ]'; % maxh (electrode refinement)
fmdl = ng_mk_extruded_model(shape, elec_pos, elec_shape);
% this similar model is also available as:
% fmdl = mk_library_model('adult_male_16el_lungs');
% show_fem(fmdl,[0 1]);
fmdl.nodes(:,1) = fmdl.nodes(:,1) - 0.5*(max(fmdl.nodes(:,1)) + min(fmdl.nodes(:,1)));
fmdl.nodes(:,2) = fmdl.nodes(:,2) - 0.5*(max(fmdl.nodes(:,2)) + min(fmdl.nodes(:,2)));
% figure; show_fem(fmdl); axis tight off
[fmdl.stimulation,fmdl.meas_sel] = mk_stim_patterns(16,1,[0,1],[0,1],{'no_meas_current'}, 5);
img = mk_image(fmdl,1);
vh = fwd_solve(img);
vRef = vh.meas;
numElement = length(img.fwd_model.elems);
element = img.fwd_model.elems;
node = img.fwd_model.nodes;
% -- Save img at compareImg to calculate S
SImg = img;
% -- Set lung conductivity
img.elem_data(fmdl.mat_idx{1}) = 1;
lungElem = [fmdl.mat_idx{2}; fmdl.mat_idx{3}]; % 2 : Right, 3 : Left
sigmaLen = 1000;
sigmaX = linspace(0,4*pi,sigmaLen);
amp = linspace(1,1.3,sigmaLen);
sigma = 0.1*amp.*sin(sigmaX)+0.25;
S = calc_jacobian(SImg);
S_normal = zeros(size(S));
for kk = 1:size(S,2)
S_normal(:,kk) = S(:,kk)/norm(S(:,kk));
end
W = diag(1./sum(abs(S'*S_normal)));
reconSolver = W*S_normal';
%% Detect Motion Artifact & Calculate VErr
boundaryNode = unique(img.fwd_model.boundary(:));
element = img.fwd_model.elems;
node = img.fwd_model.nodes;
boundaryLambda = 1e-10;
boundaryElement = [];
for iter = 1:length(element)
for innerIter = 1:length(boundaryNode)
if element(iter,1) == boundaryNode(innerIter) || element(iter,2) ...
== boundaryNode(innerIter) || element(iter,3) == boundaryNode(innerIter)
boundaryElement = [boundaryElement; iter];
end
end
end
boundaryElement = sort(unique(boundaryElement));
numboundaryElement = length(boundaryElement);
% Pick S columnn and subtract artifact
realIndexLength = length(vRef);
boundaryS = zeros(realIndexLength, numboundaryElement);
% for iter = 1:numboundaryElement
% boundaryS(:,iter) = S(:,boundaryElement(iter));
% end
boundaryS = S(:,boundaryElement);
% innerTerm = (boundaryS'*boundaryS + boundaryLambda*eye(numboundaryElement))\boundaryS';
% innerTerm = boundaryS * innerTerm;
innerTerm = eye(size(S,1)) - ((boundaryS*boundaryS'+boundaryLambda*eye(size(S,1)))\(boundaryS*boundaryS'));
%% Change triangle mesh to grid mesh
for i = 1:size(element,1)
xy(i,:) = mean(node(element(i,1:3),:));
end
nPixel = 128;
margin_FOV = 1.05;
meshsize = max(max(abs(node)))*margin_FOV;
ti = -meshsize:(2*meshsize)/(nPixel-1):meshsize;
[qx,qy] = meshgrid(ti,ti);
% -- No use area
% This is for training data
% img.elem_data(lungElem) = sigma(1);
% This is for showing data
% img.elem_data(lungElem) = sigma(350);
% vkRef = fwd_solve(img);
% vCRef = vkRef.meas;
% bodyShpae128 : makes outer body pixels zero (Need for post processing)
bodyShape = zeros(numElement,1);
bodyShape(boundaryElement) = 0.7;
bodyShape128 = scatteredInterpolant(xy(:,1),xy(:,2),bodyShape);
bodyShape128 = flipud(bodyShape128(qx,qy));
bodyShape128(find(bodyShape128>0)) = 1;
bodyShape128 = imcomplement(bodyShape128);
%% Change the collapse area
for collapseCase = 1:19%19:19
tempImg = SImg;
switch collapseCase
% Do nothing, which is normal lung
case 1
targetLungElem = lungElem;
collapseP = 0;
% Right lung collapse 5%
case 2
collapseArea = inline('((x+0.34)/0.32).^2 + ((y+0.49)/0.16).^2 < 1','x','y','z');
[row, ~] = find(elem_select(tempImg.fwd_model, collapseArea));
targetLungElem = setdiff(lungElem,row);
collapseP = 1 - length(targetLungElem) / length(lungElem);
disp(collapseP);
% Right lung collapse 10%
case 3
collapseArea = inline('((x+0.34)/0.32).^2 + ((y+0.39)/0.16).^2 < 1','x','y','z');
[row, ~] = find(elem_select(tempImg.fwd_model, collapseArea));
targetLungElem = setdiff(lungElem,row);
collapseP = 1 - length(targetLungElem) / length(lungElem);
disp(collapseP);
% Right lung collapse 15%
case 4
collapseArea = inline('((x+0.34)/0.32).^2 + ((y+0.3)/0.16).^2 < 1','x','y','z');
[row, ~] = find(elem_select(tempImg.fwd_model, collapseArea));
targetLungElem = setdiff(lungElem,row);
collapseP = 1 - length(targetLungElem) / length(lungElem);
disp(collapseP);
% Right lung collapse 20%
case 5
collapseArea = inline('((x+0.34)/0.32).^2 + ((y+0.25)/0.2).^2 < 1','x','y','z');
[row, ~] = find(elem_select(tempImg.fwd_model, collapseArea));
targetLungElem = setdiff(lungElem,row);
collapseP = 1 - length(targetLungElem) / length(lungElem);
disp(collapseP);
% Right lung collapse 25%
case 6
collapseArea = inline('((x+0.34)/0.35).^2 + ((y+0.21)/0.24).^2 < 1','x','y','z');
[row, ~] = find(elem_select(tempImg.fwd_model, collapseArea));
targetLungElem = setdiff(lungElem,row);
collapseP = 1 - length(targetLungElem) / length(lungElem);
disp(collapseP);
% Left lung collapse 5%
case 7
collapseArea = inline('((x-0.34)/0.32).^2 + ((y+0.52)/0.16).^2 < 1','x','y','z');
[row, ~] = find(elem_select(tempImg.fwd_model, collapseArea));
targetLungElem = setdiff(lungElem,row);
collapseP = 1 - length(targetLungElem) / length(lungElem);
disp(collapseP);
% Left lung collapse 10%
case 8
collapseArea = inline('((x-0.34)/0.32).^2 + ((y+0.42)/0.16).^2 < 1','x','y','z');
[row, ~] = find(elem_select(tempImg.fwd_model, collapseArea));
targetLungElem = setdiff(lungElem,row);
collapseP = 1 - length(targetLungElem) / length(lungElem);
disp(collapseP);
% Left lung collapse 15%
case 9
collapseArea = inline('((x-0.4)/0.32).^2 + ((y+0.34)/0.16).^2 < 1','x','y','z');
[row, ~] = find(elem_select(tempImg.fwd_model, collapseArea));
targetLungElem = setdiff(lungElem,row);
collapseP = 1 - length(targetLungElem) / length(lungElem);
disp(collapseP);
% Left lung collapse 20%
case 10
collapseArea = inline('((x-0.4)/0.32).^2 + ((y+0.29)/0.2).^2 < 1','x','y','z');
[row, ~] = find(elem_select(tempImg.fwd_model, collapseArea));
targetLungElem = setdiff(lungElem,row);
collapseP = 1 - length(targetLungElem) / length(lungElem);
disp(collapseP);
% Left lung collapse 25%
case 11
collapseArea = inline('((x-0.44)/0.34).^2 + ((y+0.25)/0.23).^2 < 1','x','y','z');
[row, ~] = find(elem_select(tempImg.fwd_model, collapseArea));
targetLungElem = setdiff(lungElem,row);
collapseP = 1 - length(targetLungElem) / length(lungElem);
disp(collapseP);
% Both lung collapse 5%
case 12
collapseArea = inline('(x/0.8).^2 + ((y+0.59)/0.2).^2 < 1','x','y','z');
[row, ~] = find(elem_select(tempImg.fwd_model, collapseArea));
targetLungElem = setdiff(lungElem,row);
collapseP = 1 - length(targetLungElem) / length(lungElem);
disp(collapseP);
% Both lung collapse 10%
case 13
collapseArea = inline('(x/0.8).^2 + ((y+0.53)/0.2).^2 < 1','x','y','z');
[row, ~] = find(elem_select(tempImg.fwd_model, collapseArea));
targetLungElem = setdiff(lungElem,row);
collapseP = 1 - length(targetLungElem) / length(lungElem);
disp(collapseP);
% Both lung collapse 15%
case 14
collapseArea = inline('(x/0.8).^2 + ((y+0.485)/0.2).^2 < 1','x','y','z');
[row, ~] = find(elem_select(tempImg.fwd_model, collapseArea));
targetLungElem = setdiff(lungElem,row);
collapseP = 1 - length(targetLungElem) / length(lungElem);
disp(collapseP);
% Both lung collapse 20%
case 15
collapseArea = inline('(x/0.8).^2 + ((y+0.435)/0.2).^2 < 1','x','y','z');
[row, ~] = find(elem_select(tempImg.fwd_model, collapseArea));
targetLungElem = setdiff(lungElem,row);
collapseP = 1 - length(targetLungElem) / length(lungElem);
disp(collapseP);
% Both lung collapse 25%
case 16
collapseArea = inline('(x/0.8).^2 + ((y+0.391)/0.2).^2 < 1','x','y','z');
[row, ~] = find(elem_select(tempImg.fwd_model, collapseArea));
targetLungElem = setdiff(lungElem,row);
collapseP = 1 - length(targetLungElem) / length(lungElem);
disp(collapseP);
% Both lung collapse 30%
case 17
collapseArea = inline('(x/0.8).^2 + ((y+0.35)/0.2).^2 < 1','x','y','z');
[row, ~] = find(elem_select(tempImg.fwd_model, collapseArea));
targetLungElem = setdiff(lungElem,row);
collapseP = 1 - length(targetLungElem) / length(lungElem);
disp(collapseP);
% Both lung collapse 35%
case 18
collapseArea = inline('(x/0.8).^2 + ((y+0.31)/0.2).^2 < 1','x','y','z');
[row, ~] = find(elem_select(tempImg.fwd_model, collapseArea));
targetLungElem = setdiff(lungElem,row);
collapseP = 1 - length(targetLungElem) / length(lungElem);
disp(collapseP);
% Both lung collapse 40%
case 19
collapseArea = inline('(x/0.8).^2 + ((y+0.288)/0.22).^2 < 1','x','y','z');
[row, ~] = find(elem_select(tempImg.fwd_model, collapseArea));
targetLungElem = setdiff(lungElem,row);
collapseP = 1 - length(targetLungElem) / length(lungElem);
disp(collapseP);
otherwise
error('Wrong collapseCase!')
end
RLungCollapseElem = intersect(fmdl.mat_idx{2},targetLungElem);
LLungCollapseElem = intersect(fmdl.mat_idx{3},targetLungElem);
RCollapseP = 1 - length(RLungCollapseElem) / length(fmdl.mat_idx{2});
LCollapseP = 1 - length(LLungCollapseElem) / length(fmdl.mat_idx{3});
disp([RCollapseP, LCollapseP])
%% Run FER and save EIT data during sine TV wave
for iter = 1:sigmaLen
tempImg.elem_data(targetLungElem) = sigma(iter);
if iter == 1
saveFig = figure; show_fem(tempImg); axis tight off % → Ok
saveas(saveFig, ['EIDORS Model Collapse shape (Collapse case ' num2str(collapseCase) ').bmp'])
close(saveFig);
% break; % For save fem model image
end
vk = fwd_solve(tempImg);
vCase = vk.meas;
% V = vCRef - vCase;
V = vRef - vCase;
% vErr = innerTerm*V;
% V = V - vErr; % -- CAUTION !!!
reconResult = reconSolver*innerTerm*V;
% -- Change image into 128*128
F = scatteredInterpolant(xy(:,1),xy(:,2),reconResult);
% Need to change the constant for each cases
gridReconResult = flipud(F(qx,qy)) .* 3;
gridReconResult(gridReconResult<0) = 0;
% This makes outer body pixel zero (post processing)
gridReconResult = gridReconResult .* bodyShape128;
figure; imagesc(gridReconResult); axis square tight off % Check
% Save 128*128 image & voltage data for training
imgPath = [EIT_FER_Filepath '\EIT_EIDORS_Model_FER_collapse_case_' num2str(collapseCase) '_' num2str(iter) '.png'];
imwrite(gridReconResult,imgPath,'PNG'); close;
VPath = [EIT_V_Filepath '\EIT_EIDORS_Model_FER_Voltage_collapse_case_' num2str(collapseCase) '_' num2str(iter) '.csv'];
writematrix(V',VPath);
CPPath = [EIT_CP_Filepath '\EIT_EIDORS_Model_FER_CP_collapse_case_' num2str(collapseCase) '_' num2str(iter) '.csv'];
writematrix([RCollapseP, LCollapseP],CPPath);
disp(['EIDORS Model FER Case ' num2str(collapseCase) ' → ' num2str(iter) ' / ' num2str(sigmaLen) ' Finished ...']);
end
end
end