airtrapping threshold techniques . dias

D. S. DIAS, BME 7112, MIDTERM PROJECT, SPRING 2015

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AbstractGoal: This study intends to evaluate which Threshold criteria is the best when using only expiratory scan. Methods: In order to do this some thresholds were chosen in literature to compare it`s efficacy of identifying and quantifying AT with a radiologist analysis of a child expiratory state scan. through using segmentation and computer vision techniques in MATLAB Results: It was found that the threshold between -850 and -910 HU was the closest to the radiologist analysis and that the child could be suffering of a mild chronic obstructive pulmonary disease. Conclusion: . The methodology used showed potential to quantify AT without inspiratory scans showing potential to reduce radiation dose in patients and it`s required further studies with more subjects for statistical analysis, and the use of more segmentation techniques like vessel removal and analysis of each lung lobes separately. Significance: There is no consensus yet on which thresholds for quantifying AT is/are the best in this application, .

Index Terms Air Trapping, Threshold, Computer Tomography, Expiratory Scan.

I. INTRODUCTION

A. Background

HE evolution of Computed Tomography(CT) imaging systems is highly relevant on lungs imaging, modern CT systems are able to image 64 1-milimiter slices during a 1-

second rotation of its gantry. This evolution has came out with a new range of applications and a large amount of data for the radiologists to analyze, this situation suggested that this process of analysis could be automated, or offer help to the radiologists with computer vision tools [1]. The CT imaging resolution can be acquired at a submilimiter level, allowing direct visualization in changes of small airways of 2mm, and even more advanced CTs like multi-detector ones that can detect intrapulmonary and endobronchial structures of 0.2mm3[1-3]. One thing that these higher resolution CT scans could detect and quantify is air trapping(AT) that is defined as retention of air during expiration state of the lungs[1,2]. particularly at the level of small bronchial tubes. Expiratory CT scans by themselves are able to detect AT according to Gaeta et al (2013)[4].

Basically the procedure of acquiring the expiratory CT scans is typically obtained at the end of a forced expiration. During the scan the patients are advised to : Take a deep breath, blow out hard, and do not breathe in again for 10 seconds. It's required that each patient practice it before the scanning begins[4]. The majority of quantitative CT techniques to measure AT are density based ones, like: comparing expiratory to expiratory ratio of mean lung density; and volume change of voxels between two threshold of Hounsfield units(HU)[3-9]) and percentage of voxels below-856 HU in a expiratory scan[9]. Some of them also compare or associate the CT scans with pulmonary functional tests[2,3,6,7]. B. Significance AT is related to various airways obstruction in several diseases like emphysema, bronchiolitis obliterans, bronchial asthma, Swyer-James syndrome,reactive airway disease, cystic fibrosis, bronchiectasis, sarcoidosis, hypersensitivity pneumonitis, atypical pneumonia and eosinophilic granuloma [4,10]. Early AT measurements and detection is also important for finding diseases in their early stages like chronic lung rejection[2], smoking-related lung cancer, chronic obstructive pulmonary disease(COPD)(Mets et al 2012) and small airways disease. An early finding of these diseases are essential because there could be an interventions in their early stages which could prevent their progression and their severity. According to some studies there is no consensus or standard on how to quantify AT.[5,8], But in order to standardize and universalize the protocols we need, there is a problem because there is no agreement between studies due different scanner models; reconstruction algorithms, parameters protocols(voxel size, tube voltage (kVp), and tube current, exposure time product (mAs),number ) interpatient variation in inspiratory and expiratory lung volumes, the degree of expiration at the time of the scanning and the threshold techniques during AT finding.[9,10].

Despite most of authors say that we need both expiratory and inspiratory CT scans for lung disease, this approach is questionable because the amount of radiation exposed to the patient during the acquisition mainly for patients undergoing repeated exposures and young patients[4](that is the case of this study).There is even a study[10] that discuss that using tube current time down to 20mAs it`s possible to gather AT information without imparing it.

Comparison of air trapping(AT) identification in Computed Tomography(CT) in expiration scans

using different threshold levels Diogo da Silva Dias, [email protected], UID U00777095, Processing of Medical Images, Wright

State University,Spring 2015, March 23rd, 2015, Professor: Mr. Nasser Kashou,

T


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C. Hypothesis Comparing AT finding and quantifying criteria and

techniques is a way of trying to standardize and further create consensual protocols for clinical application.

D. Purpose The purpose of this study is to compare different threshold

criteria in expiratory scan some studies for finding and quantifying AT regions and find which one have closer results to the radiologist findings on the same scans.

II. METHODS

A. DICOM headers In this project I had for analysis 4 lung scans in expiratory

state and in DICOM format, and the 4 same scans with the indication of AT regions by radiologists. Using the command dicominfo() of MATLAB I could gather some information on how the scans were acquired and it is showed on table 1, in order to help me interpret the data I used a guidance document(ACR, 2013), and this other one. Reading the dicom files of each header I realized the 4 scans were from the same person, this person was a male child.

TABLE 1 INFORMATION OF THE ACQUISITION OF THE IMAGES IN THE DICOM HEADERS

OF THE FILES

Some Dicom Files Headers information System HISPEED RP CT system

from GE Resolution 512x512 Bit Depth 16

Color Type "Grayscale" Patient`s Age 5 years old Patient`s Sex Male

Nominal Slice thickness 1mm KVP(Kilo Voltage peak)

used 120

Tube current(mA) 100 Exposure time (ms) 1000

Current exposure time 100 Reconstruction Diameter(mm)

250

[b]Rescale Intercept -1024 [m]Rescale Slope 1

B. Thresholding criteria After that reading the dicom header of the files, I`ve

followed some ideas of the procedures of some analogue studies, like the automated isolation of the lungs from the parenchyma, [1,2,5,7,8]; separate the left from right lung [8,11] and the use of threshold techniques for detecting AT[2,3,6,7,8,11]. The threshold levels of HU in some studies(....) used for expiratory state is showed on table 2, analyzing these studies, It was possible to define 5 thresholds for AT detection(A,B,C,D and E), in the studies without the Lower Level will be defined as -1024 because it is the rescale

intercept value .

TABLE 2 - THRESHOLD LEVELS IN EXPIRATORY CT FOR DETECTING AT

Threshold range

Colormap Upper level(HU)

Lower Level(HU)

Solyanik et al 2013[2]

A Spring (pink, yellow)

-750 -910

Cohen et al 2008[3] Lee et al 2008[6]

B Autumn (red,yellow)

-950 -

Schrodder et al 2013[9] Zach et al 2013[12]

C Winter (blue, green)

-856 -

Mets et al 2012[8]

D Summer (Green, yellow)

-860 -950

Bommart et al 2014[5]

E Hot (red, white)

-850 -910

Radiologist`s analysis

F Grayscale/ yellow line marker

- -

C. Converting raw data to Hounsfield Units(HU)

In order to make this a quantity analysis using HU I had to convert the raw data into HU and for doing a DICOM manual [12] gives the instructions on how to do it, there is a formula (eq. 1) using the rescale intercept[b] and slope[m] to convert the raw data[SV] into HU.

Output units = m(SV) + b; (1)

D. Image Analysis Through code

Using the previous considerations I wrote a m-file in MATLAB R2014a for measuring and displaying the AT region on a CT scan of the expiratory state, called ATmeasure(). there is a diagram (Fig.1) and a example (Fig. 2) of on how the code works in Appendix 1. The procedure of ATmeasure() has the following steps:

1- Reading the DICOM image and storing it in a raw data array(Fig 2.a) and a converting dicom image in another uint16 array(Fig 1.b).

2- Isolating automatically the lungs from other tissues in the uint16 image and each lung in different arrays(Fig 2. d)

3- Creating and applying masks to each lung with the function maskcreator() that`s going to be commented further.( (Fig 2.e)


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4- Converting the raw data into HU with eq. 1 and storing the converted data in other array

5- Applying the previously created mask for each lung in the AT array.

6- Highlighting the places in the lungs with AT within the inputted threshold by the user.(Fig.2.f)

7- Creating another mask with the AT regions for each lung.(Fig 2,g)

8- Calculating the percentage of AT and the mean attenuation in HU of each lung, and then printing these results on the command window

9- Plotting the image of the lungs with the Highlighted AT regions.

The maskcreator() is a function used by ATmeasure(), it has a diagram (Fig.3) and a example(Fig. 4) on Appendix 2 to illustrate how the code works. During this code development, I had huge help from Shoelson`s webinar videos at www.mathworks.com, on how to create masks in MATLAB and on how to use the MATLAB`s built-in function regionprops()[13,14].The procedure of maskcreator() has the following steps:

1- Loading a uint16 array (Fig 4.a) 2- Adjusting the uint16 histogram and creating a Black

and white binary image out of the grayscale one, making black areas with intensities in one peak of the histogram and white the regions on the other peak.(Fig 4.b)

3- Using regionprops() for finding "areas" on the Black and white image

4- Making all the areas black except the biggest one, in the case of this application the lung. (Fig 4.c)

5- Filling the "holes" in the image with imfill()(Fig 4.d) 6- Making this resultant mask as the output of the

function.

E. Comparison

In order to check the difference between the thresholds I used different colormaps(Table 2) for each threshold result and I`ve compared then with the radiologist findings, to evaluate which threshold was closer to the radiologist findings. The percentage of AT in each scan for each lung as well their mean lung attenuation were put in tables, to evaluate the quantification of the AT.

III. RESULTS

On Table 3 below the Mean Lung attenuation of the 4 available slices of the CT scan in the expiratory stage is showed below, the left and right lung are observed separately

TABLE 3 MEAN LUNG ATTENUATION(HU) IN EACH LUNG IN 4 SLICES

Slice Left Lung(HU) Right Lung(HU) 1 -739.03 -737.35 2 -726.2 -725.39 3 -779.94 -781.64 4 -746.33 -748.03

Average -747.8822.94 -748.124.19

There is not a huge difference in the mean lung attenuation of the lungs according table 3.

Comparing the Mean Lung attenuation in expiratory state in table 3 to Schrodder et al (2013)[8] results we could say that this subject can have a mild(-730.4) to moderate(-758.8) COPD. However we should consider that the age group was different from our subject, maybe his case could have a different severity.

On Table 4 In Appendix 3 shows the percentage of the AT in relation to the lung area is showed, for every slice, for each Table 2`s thresholding criteria, for each lung separately. In order to compliment this table Figures 5,6,7 and 8 in Appendix 3 show the visual differences between the techniques and the radiologist analysis of where the AT is.

Observing Table 4 and figures 5-8 in appendix 3 it looks that the thresholds A and B, are not good for this application, A for detecting more AT than the previewed by the Radiologists and B for almost not detecting AT at all. Comparing C,D and E percentages of AT in expiratory state to the Schrodder et al 2013 [8] results, it`s reinforced the idea that the subject has a a mild COPD.

Despites the radiologist points out the regions on the CTs where there is AT. Looking at the table, thresholds, C, D and E had very close AT percentage measurements and looking at the figures 5-8 the threshold E is the closest one to shows the AT detection similar to the Radiologist analysis, we could say that maybe this threshold is better to evaluate the existence of AT in young children, or Males.

Between thresholding criteria C,D and E, closer criteria to the radiologist analysis, it was not seen a big difference of left and right lung of as in table 3

IV. CONCLUSION

It was possible to create a MATLAB code to detect AT quantifications and position similar to the radiologist findings mainly in using a threshold of -950 and -850 HU as Bommart et al(2014)[5] for detecting AT, as I had only one subject no statistical analysis could be made like[2,6,8] this is study was also limited having only the expiratory state, but as it was possible to quantify AT without Inspiratory state it`s possible to say that it was not a major problem of this study and besides it, this result confirms the idea that it`s questionable the use of Inspiration state for AT quantification and identification and we could also reduce patient`s dose for this applications if we keep developing a protocol for quantifying AT only with Expiratory CT scans.


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Other improvements that further studies could have besides more subjects and consequently statistical analysis, is analyzing lung lobes segmentation, lung vessels removal, having a better interface in the software to the user as suggested by Summer et al. (2006)[1].One other possibility is testing the technique of this study using even of lower radiation dose in acquiring the images as said by Bankier et al (2007)[10].

REFERENCES [1] I. Sluimer, A. Schilham, M. Prokop and B. van Ginneken.

Computer analysis of computed tomography scans of the lung: A survey. Medical Imaging, IEEE Transactions on 25(4), pp. 385-405. 2006.

[2] O. Solyanik, S. Dettmer, T. Kaireit, F. Wacker, H.-O. Shin; Detection of pathologic air trapping in patients after lung transplantation: comparison of three methods using Multi Detector Row CT.ECR 2013,2013.

[3] J. Cohen, W. R. Douma, P. M. van Ooijen, T. P. Willems, V. Dicken, J. M. Kuhnigk, N. H. ten Hacken, D. S. Postma and M. Oudkerk. Localization and quantification of regional and segmental air trapping in asthma. J. Comput. Assist. Tomogr. 32(4), pp. 562-569. 2008.

[4] M. Gaeta, F. Minutoli, G. Girbino, A. Murabito, C. Benedetto, R. Contiguglia, P. Ruggeri and S. Privitera. Expiratory CT scan in patients with normal inspiratory CT scan: A finding of obliterative bronchiolitis and other causes of bronchiolar obstruction. Multidiscip Respir Med 8pp. 1-86. 2013.

[5] S. Bommart, G. Marin, A. Bourdin, N. Molinari, F. Klein, M. Hayot, I. Vachier, P. Chanez, J. Mercier and H. Vernhet-Kovacsik. Relationship between CT air trapping criteria and lung function in small airway impairment quantification. BMC Pulmonary Medicine 14(1), pp. 29. 2014.

[6] Y. K. Lee, Y. Oh, J. Lee, E. K. Kim, J. H. Lee, N. Kim, J. B. Seo, S. Do Lee and KOLD Study Group. Quantitative assessment of emphysema, air trapping, and airway thickening on computed tomography. Lung 186(3), pp. 157-165. 2008.

[7] O. M. Mets, P. Zanen, J. J. Lammers, I. Isgum, H. A. Gietema, B. van Ginneken, M. Prokop and P. A. de Jong. Early identification of small airways disease on lung cancer screening CT: Comparison of current air trapping measures. Lung 190(6), pp. 629-633. 2012.

[8] J. D. Schroeder, A. S. McKenzie, J. A. Zach, C. G. Wilson, D. Curran-Everett, D. S. Stinson, J. D. Newell Jr and D. A. Lynch. Relationships between airflow obstruction and quantitative CT measurements of emphysema, air trapping, and airways in subjects with and without chronic obstructive pulmonary disease. AJR Am. J. Roentgenol. 201(3), pp. W460-70. 2013.

[9] M. L. Goris, H. J. Zhu, F. Blankenberg, F. Chan and T. E. Robinson. An automated approach to quantitative air trapping measurements in mild cystic fibrosis. CHEST Journal 123(5), pp. 1655-1663. 2003.

[10] A. A. Bankier, C. Schaefer-Prokop, V. De Maertelaer, D. Tack, P. Jaksch, W. Klepetko and P. A. Gevenois. Air trapping: Comparison of standard-dose and simulated low-dose thin-section CT techniques 1. Radiology 242(3), pp. 898-906. 2007.

[11] J. A. Zach, J. D. Newell Jr, J. Schroeder, J. R. Murphy, D. Curran-Everett, E. A. Hoffman, P. M. Westgate, M. K. Han, E. K. Silverman, J. D. Crapo, D. A. Lynch and COPDGene Investigators. Quantitative computed tomography of the lungs and airways in healthy nonsmoking adults. Invest. Radiol. 47(10), pp. 596-602. 2012.

[12] NEMA.National Electrical Manufacturers Association. Digital Imaging and Communications in Medicine (DICOM). Part 3: Information Object Definitions . Virginia, 2011.

[13] B. Shoelson.Webinar:Medical Imaging Workflows with MATLAB.Mathworks.2011. Available at : http://www.mathworks.com/videos/medical-imaging-workflows-with-matlab-81850.html

[14] B. Shoelson.Webinar:Medical Image Processing with MATLAB .Mathworks.2011. Available at: http://www.mathworks.com/videos/medical-image-processing-with-matlab-81890.html


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Appendix 1

Fig.1 - The Diagram of the ATmeasure() function


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Appendix 1

Fig.2 - The Steps of the ATmeasure() function.a-) is the raw data, b-) is the adjusted imagee, c-) is the image without other structures near the lungs, d-) is the left lung separeted, e-) is the lung after using the mask, f-) is the AT mask without being

filtered, g-) is the left lung with the AT measuremenst highlighted.

Code 1 - ATmeasure().m

%% ATmeasure() % Diogo Dias UID# U00777095 % BME 7112 Processing of Medical Images % Spring 2015 % Midterm Project % %% --- help for ATmeasure() --- % % ATmeasure() is a function that reads a Dicom CT Image of a Segment of the lungs % in a expiration state, then calculates the percentage of air trapping(AT) % in each lung and the mean attenuation in Hounsfiled units(HU) in each % lung as well, it also plot a grayscale image of the lungs with the AT. % The procedure follows % 1- It reads the image % 2- It creates and apply masks to each lung % 3- It highlights the places in the original image with a threshold % inputted by the user % 4- It creates another mask with the AT regions % 5- It calculates the percentage of AT and the mean attenuation in HU of % each lung, and then print it in the command window % 6- It plots the image of each

%% Function Start


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% PATleft and PATright are the percentage of AT in each lung % meanHousleft and meanHousright is the mean attenuation in housfield in % the lungs % lower ATT is the lower threshold for finding the AT % upper ATT is the upper threshold for finding the AT % A2 is the matrix with the highlighted

function[PATleft, PATright, meanHousleft, meanHousright, A2] = ATmeasure(lowerATT, upperATT) %% Start a clean state close all;clc;%close all; %set(0,'DefaultFigureWindowStyle','docked');

%% Open image, change its format and contrast

%cd ['C:\Users\Diogo\Documents\backup hd externo\WSU\Processing of Medical ... %'Imaging\Midterm project']; %B = dicomread('IM1');

%I use the built-in function dicomread() B = dicomread(uigetfile({'IM*'},'Pick a CT file')); %imshow(B) %procedure check 1 A = uint16(B); %imshow(A);%procedure check 2 A1 = imadjust(A); %imshow(A1)%procedure check 3 I`ve used this image to check out what coordinates I would use %to remove the regions above and below the lung from the image in %the next cell

%% Remove image parts below and above the lung %I would do it to make it easier to create a mask for the lungs A1(1:110,:)= 0 ; A1(350:end,:)= 0 ; A1(A1 >= 35000) = 0;

%imshow(A1);%procedure check 4

%% Divide the lungs into two arrays Aleft = A1; Aright = A1; Aleft(:,267:end) = 0;%make right lung black Aright(:,1:267) = 0;%make leftt lung black

%imshow(Aleft);%procedure check 5 %% Create masks for each lung %I use here a function called maskcreator that I`ve wrote myself to create %the masks, the comments about is done in this function itself.

leftmask = maskcreator(Aleft); rightmask = maskcreator(Aright);

%imshow(leftmask);%procedure check 6 %% Apply Masks %The following code will make everything outside the masks with intensity 0 %i.e black Aleft(~leftmask) = 0; Aright(~rightmask) = 0;


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%imshow(Aleft);%procedure check 7

%% Checkout Air Trapping regions % to come up with this cell of code I`ve read the header of the dicom files % to check the rescale slope and intercept to convert the raw data of the % dicom file into HU

m = 1; %rescale slope RI = -1024; %rescale intercept %lowerATT = -910; %Air Trapping Threshhold %upperATT = -750;%According to cohen et al 2008;

B(B == -32768) = -300; B = B.*m + RI; %slope for conversion meanB = B; B(B > lowerATT & B < upperATT) = 10000; %make the regions with Air Trapping Whiter %imshow(B)%procedure check 8 B1 = uint16(B);%turn the values into uint16 bwb1 = ~im2bw(B1


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clear all; end% function end

Appendix 2

Fig.3 - The Diagram of the maskcreator() function


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Fig.4 - The Steps of the maskcreator() function.a-) is the segmented left lung, b-) is the image in black and white, c-) is the biggest area of the image obtained through regionprops , d-) is the resultant mask.

Code 2 - maskcreator().m

%% maskcreator() % Diogo Dias UID# U00777095 % BME 7112 Processing of Medical Images % Spring 2015 % Midterm Project % %% --- help for maskcreator() --- % % maskcreator() is a function that gets grayscale image and creates a mask % only for the biggest object in it.It uses regionprops built-in MATLAB`s function % and its Area feature. The 'image' is the grayscale image and [mask] % is the resultant mask % In this code I would like to cite and thank Brett Shoelson`s webinar videos % at www.mathworks.com("Medical Imaging Workflows with MATLAB" and % "Medical Image Processing with MATLAB" both recorded on April 2012) that % gave me ideas for creating masks, and helped a lot in this midterm project.

%% The code itself function[mask] = maskcreator(image)


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%according to graythresh`s help it uses the Otsu's method, which chooses %the threshold to minimize the intraclass variance of the thresholded %black and white pixels.

G = graythresh(image);

%im2bw converts the intensity values into black for values that are below % the threshhold G and white to values above G BW = im2bw(image,G); %imshow(BW);%procedure check 10 %bwconncomp is a function that stores the properties of a black and white %matrix, it`s going to be used further in the code cc = bwconncomp(BW);

%regionprops with the Area flag return All the areas that the region props %could find in the Black and White image stats = regionprops(BW,'Area'); S = [stats.Area]; %the maximum area in the figure is selected in this code to be used as a %theshold to make it 0 all the other areas out of the bigger one. [~,tresh] = max(S); BW(labelmatrix(cc)~= tresh)= 0; %imshow(BW);%procedure check 11 %imfill () built-in function that makes the mask smoothier without holes on %it BW = imfill(BW,'holes'); mask = BW; %imshow(BW);%procedure check 12 end %function end

Appendix 3

TABLE 4 - PERCENTAGE OF DETECTED AT IN THE RIGHT AND LEFT LUNG, WITH THE DIFFERENT THRESHOLD CRITERIA

Threshold Range

Slice 1 Slice 2 Slice 3 Slice 4 Left

Lung(%) Right

Lung(%) Left

Lung(%) Right

Lung(%) Left

Lung(%) Right

Lung(%) Left

Lung(%) Right

Lung(%) A 54 68 46 30 74 44 63 42 B 1 1 0 0 0 0 0 0 C 19 19 5 4 9 6 8 5 D 16 16 4 3 8 5 7 4 E 16 15 5 4 8 5 9 5


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Fig.5 - The Different Thresholds for detecting AT and the Radiologist analysis ,analyzing the slice 1,.a-)Threshold between -750 HU -910 b-) Threshold between -950 HU -1024 c-) Threshold between -856 HU -1024

d-) Threshold between -860 HU -950e-) Threshold between -850 HU -910.f-) radiologist analysis.




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