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EIDORS: Electrical Impedance Tomography and Diffuse Optical Tomography Reconstruction Software

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EIT-based regional lung mechanics in the acute respiratory distress syndrome during lung recruitment (human)

Methods and Data

The EIT data were collected at Children's Hospital Boston in 2009 during a stepwise lung recruitment manoeuver and positive end-expiratory pressure (PEEP) titration of a patient with the acute respiratory distress syndrome as part of a clinical study described in the following two papers:

  • C Gómez-Laberge, JH Arnold, GK Wolf. A Unified Approach for EIT Imaging of Regional Overdistension and Atelectasis in Acute Lung Injury IEEE Trans Med Imag, In Press 2012
  • GK Wolf, C Gómez-Laberge, JN Kheir, D Zurakowski, BK Walsh, A Adler, JH Arnold. Reversal of Dependent Lung Collapse Predicts Response to Lung Recruitment in Children with Early Acute Lung Injury Pediatr Crit Care Med, In Press 2012

EIDORS Analysis

  1. Go to the Data Contrib section, and download the data and save it to your working directory.
  2. Go to the Data Contrib section, and download the code and save it to your working directory.

    At this point, you should have 27 *.m files in the directory, and a directory structure 'DATA/STUDYNAME/SUBJECT_1/YYYYMMDD/Eit/Viasys' containing 9 *.get files.

  3. Reconstruct images for each step of the protocol and save images
    % PROCESS EACH STEP OF THE PROTOCOL
    basename = './DATA';
    filename = 'STUDYNAME/SUBJECT_1/YYYYMMDD/Eit/Viasys/1001_b.get'; 
    range =[]; maneuver='increment'; PEEP=14; dP=5;
    process_and_save(basename, filename, range, maneuver, PEEP, dP);
        
    filename = 'STUDYNAME/SUBJECT_1/YYYYMMDD/Eit/Viasys/1001_c1.get'; 
    range =[]; maneuver='increment'; PEEP=15; dP=15;
    process_and_save(basename, filename, range, maneuver, PEEP, dP);
        
    filename = 'STUDYNAME/SUBJECT_1/YYYYMMDD/Eit/Viasys/1001_c2.get'; 
    range =[]; maneuver='increment'; PEEP=20; dP=15;
    process_and_save(basename, filename, range, maneuver, PEEP, dP);
        
    filename = 'STUDYNAME/SUBJECT_1/YYYYMMDD/Eit/Viasys/1001_c3.get'; 
    range =[]; maneuver='increment'; PEEP=25; dP=15;
    process_and_save(basename, filename, range, maneuver, PEEP, dP);
        
    filename = 'STUDYNAME/SUBJECT_1/YYYYMMDD/Eit/Viasys/1001_c4.get'; 
    range =[]; maneuver='increment'; PEEP=30; dP=15;
    process_and_save(basename, filename, range, maneuver, PEEP, dP);
        
    filename = 'STUDYNAME/SUBJECT_1/YYYYMMDD/Eit/Viasys/1001_d1.get'; 
    range =[1,765]; maneuver='decrement'; PEEP=20; dP=6;
    process_and_save(basename, filename, range, maneuver, PEEP, dP);
        
    filename = 'STUDYNAME/SUBJECT_1/YYYYMMDD/Eit/Viasys/1001_d2.get'; 
    range =[]; maneuver='decrement'; PEEP=18; dP=6;
    process_and_save(basename, filename, range, maneuver, PEEP, dP);
        
    filename = 'STUDYNAME/SUBJECT_1/YYYYMMDD/Eit/Viasys/1001_d3.get'; 
    range =[368,780]; maneuver='decrement';  PEEP=16; dP=5;
    process_and_save(basename, filename, range, maneuver, PEEP, dP);
        
    filename = 'STUDYNAME/SUBJECT_1/YYYYMMDD/Eit/Viasys/1001_d4.get'; 
    range =[411,780]; maneuver='decrement'; PEEP=14; dP=4;
    process_and_save(basename, filename, range, maneuver, PEEP, dP);
    
    
  4. Display time signal and images from one step
    %% SHOW EXAMPLE RESULTS FROM ONE STEP
    load SUBJECT_1-c1.mat
    EITCalcTimeSignal(db_c1.eitdata);
    EITCalcFrequencySpectrum(db_c1.eitdata);
    EITDisplayImages(db_c1.eitimages);
    EITDisplayImages(db_c1.eitimages,1,'mtd');
    % :clear
    
    

    Figure 1: Output of "EITDisplayImages(db_c1.eitimages,1,'mtd');". An estimate regional tidal ventilation.
  5. Aggregate data from all recruitment maneuver steps
     
    close all
    load SUBJECT_1-b.mat
    load SUBJECT_1-c1.mat
    load SUBJECT_1-c2.mat
    load SUBJECT_1-c3.mat
    load SUBJECT_1-c4.mat
    load SUBJECT_1-d1.mat
    load SUBJECT_1-d2.mat
    load SUBJECT_1-d3.mat
    load SUBJECT_1-d4.mat
    clear matname
    vars = who('db_*');
    aggLungROI = eval([vars{1} '.eitimages.lungROI;']);
    for i = 2:length(vars)
        aggLungROI = aggLungROI | eval([vars{i} '.eitimages.lungROI;']);
    end
    for i = 1:length(vars)
        eval([vars{i} '.eitimages.lungROI = aggLungROI;']);
        eval([vars{i} ...
            '.eitimages = EITCalcComplianceImage(' vars{i} '.eitimages);']);
    end
    clear i
    limb_index = {'b','c1','c2','c3','c4'};
    
    eitimages = eval(['db_' limb_index{1} '.eitimages']);
    image_mask = eitimages.image_mask;
    lungROI = eitimages.lungROI;
    lindex = length(limb_index);
    [row,col] = size(image_mask);
    C = zeros(row,col,lindex);
    pressure = zeros(lindex,1);
    for i = 1:lindex
        C(:,:,i) = eval(['db_' limb_index{i} '.eitimages.complianceimage']);
        pressure(i) = eval(['db_' limb_index{i} '.eitimages.peep;']);
    end
    Ctab = TabulateImageData(C,image_mask);
    lungROItab = TabulateImageData(lungROI,image_mask);
    Ctab(lungROItab==0,:) = [];
    
    Cmax = zeros(row,col);
    Ctab = TabulateImageData(C,image_mask);
    [Cmaxtab,index] = TabulateImageData(Cmax,image_mask);
    Pstartab = Cmaxtab;
    for i = 1:size(Ctab,1)
        if isinf(Ctab(i,1))
            Cmaxtab(i) = -Inf;
            Pstartab(i) = -Inf;
        else
            tempc = Ctab(i,:);
            cmax = max(tempc);
            pstaridx = find(tempc==cmax,1);
            pstar = eval(['db_' limb_index{pstaridx} '.eitimages.peep']);
            Cmaxtab(i)=cmax;
            Pstartab(i)=pstar;
        end
    end
    Cmax = ImageTableData(Cmaxtab,[row col],index);
    Cmax(~image_mask) = -Inf;
    Pstar = ImageTableData(Pstartab,[row col],index);
    Pstar(~image_mask) = -Inf;
    eitimages.Cmax  = Cmax;
    eitimages.Pstar = Pstar;
    for i = 1:lindex
        eval(['db_' limb_index{i} '.eitimages = EITCalcLungState(db_' ...
            limb_index{i} '.eitimages,Cmax,Pstar);']);
    end
    
    
  6. Display C_MAX and P_STAR maps
    EITDisplayImages(eitimages,1,'Cmax')
    EITDisplayImages(eitimages,1,'Pstar')
    
    

    Figure 2: C_MAX map represents each pixel's maximum compliance during the recruitment manoeuvre. Grey pixels represent extrapulmonary tissue excluded during ROI analysis in step 4.

    Figure 3: P_STAR map indicates the pressure at which maximum compliance was achieved during lung recruitment. Black pixels were most compliant at 14 cm of water, while brightest pixels were most compliant at 30 cm of water. Grey pixels represent extrapulmonary tissue.
  7. Display lung overdistension (blue) and collapse (red) maps
    close all
    for i = 1:lindex
        EITDisplayImages(eval(['db_' limb_index{lindex-i+1} '.eitimages']),1,'collapseOD');
    end
    
    

    Figure 4: Regional overdistension (blue) and collapse (red) maps during lung recruitment. Grey pixels represent extrapulmonary tissue.
  8. Repeat steps 4-6 for PEEP titration manoeuvre: aggregate data from titration steps
    close all
    clear 
    load SUBJECT_1-b.mat
    load SUBJECT_1-c1.mat
    load SUBJECT_1-c2.mat
    load SUBJECT_1-c3.mat
    load SUBJECT_1-c4.mat
    load SUBJECT_1-d1.mat
    load SUBJECT_1-d2.mat
    load SUBJECT_1-d3.mat
    load SUBJECT_1-d4.mat
    clear matname
    vars = who;
    aggLungROI = eval([vars{1} '.eitimages.lungROI;']);
    for i = 2:length(vars)
        aggLungROI = aggLungROI | eval([vars{i} '.eitimages.lungROI;']);
    end
    for i = 1:length(vars)
        eval([vars{i} '.eitimages.lungROI = aggLungROI;']);
        eval([vars{i} ...
            '.eitimages = EITCalcComplianceImage(' vars{i} '.eitimages);']);
    end
    clear i
    
    limb_index = {'d1','d2','d3','d4'};
    
    eitimages = eval(['db_' limb_index{1} '.eitimages']);
    image_mask = eitimages.image_mask;
    lungROI = eitimages.lungROI;
    lindex = length(limb_index);
    [row,col] = size(image_mask);
    C = zeros(row,col,lindex);
    pressure = zeros(lindex,1);
    for i = 1:lindex
        C(:,:,i) = eval(['db_' limb_index{i} '.eitimages.complianceimage']);
        pressure(i) = eval(['db_' limb_index{i} '.eitimages.peep;']);
    end
    Ctab = TabulateImageData(C,image_mask);
    lungROItab = TabulateImageData(lungROI,image_mask);
    Ctab(lungROItab==0,:) = [];
    
    Cmax = zeros(row,col);
    Ctab = TabulateImageData(C,image_mask);
    [Cmaxtab,index] = TabulateImageData(Cmax,image_mask);
    Pstartab = Cmaxtab;
    for i = 1:size(Ctab,1)
        if isinf(Ctab(i,1))
            Cmaxtab(i) = -Inf;
            Pstartab(i) = -Inf;
        else
            tempc = Ctab(i,:);
            cmax = max(tempc);
            pstaridx = find(tempc==cmax,1);
            pstar = eval(['db_' limb_index{pstaridx} '.eitimages.peep']);
            Cmaxtab(i)=cmax;
            Pstartab(i)=pstar;
        end
    end
    Cmax = ImageTableData(Cmaxtab,[row col],index);
    Cmax(~image_mask) = -Inf;
    Pstar = ImageTableData(Pstartab,[row col],index);
    Pstar(~image_mask) = -Inf;
    eitimages.Cmax  = Cmax;
    eitimages.Pstar = Pstar;
    for i = 1:lindex
        eval(['db_' limb_index{i} '.eitimages = EITCalcLungState(db_' ...
            limb_index{i} '.eitimages,Cmax,Pstar);']);
    end
    
    
  9. Display C_MAX and P_STAR maps
    EITDisplayImages(eitimages,1,'Cmax')
    EITDisplayImages(eitimages,1,'Pstar')
    
    

    Figure 5: C_MAX map represents each pixel's maximum compliance during the PEEP titration. Grey pixels represent extrapulmonary tissue.

    Figure 6: P_STAR map indicates the pressure at which maximum compliance was achieved during PEEP titration. Black pixels were most compliant at 14 cmH2O, while brightest pixels were most compliant at 20 cmH2O. Grey pixels represent extrapulmonary tissue.
  10. Display lung overdistension (blue) and collapse (red) maps
    close all
    for i = 1:lindex
        EITDisplayImages(eval(['db_' limb_index{lindex-i+1} '.eitimages']),1,'collapseOD');
    end
    
    

    Figure 7: Regional overdistension (blue) and collapse (red) maps during PEEP titration. Grey pixels represent extrapulmonary tissue.

Last Modified: $Date: 2017-02-28 13:12:08 -0500 (Tue, 28 Feb 2017) $ by $Author: aadler $