Manual of IMTPC. By Zhaohui Wang

Transcription

1 Manual of IMTPC By Zhaohui Wang August 2005

2 i Table of Contents Table of Contents... i Intima-Media Thickness and Plaque Classification Introduction IMT Analysis Basics of Artery Intima-media Thickness Algorithm Calibration Preprocessing the Data Calculate the Far wall, Far M-Line and Far I-Line Calculate the Near wall, Near M-Line and Near I-Line Post-processing Segmentation Manual Adjustment Calculate the Diameter, Near IMT, Far IMT Cross-sectional Line Analysis Plaque Classification Some Important Evaluation Parameters GSM Adjustment Draw Plaque Area Color Classification Software Development... 12

3 ii 4.1. Classes and their Functions Installation and the Registration of the ActiveX Driver Information for Research Functional Control Panel IMT Operation Panel Plaque Operation Panel Class Color Assignment Panel Report Panel Display Panel Compliance Panel Application Contrast-Detail Study Clinical Application Appendices: Three Reports Reference... 28

4 1 Intima-Media Thickness and Plaque Classification 1. Introduction To understand the contrast-detail results of the ultrasound images, we should make clear the features of the ultrasound data, similar to the research of the classification of blood plaque components. The software, IMTPC, aims to direct quantitative assessment of the atherosclerotic plaque and provide additional understanding of the natural history and prognosis of vascular atherosclerosis disease as well as the potential of pharmaceutical interventions [1]. Severe carotid stenosis is the single most important etiological factor in focal cerebral ischemia. Carotid endarterectomy is indicated in stenosis of greater than 70% according to national trials in the United States. Since the morbimortality associated with carotid endarterectomy in patients with moderate stenosis is similar to the incidence of stroke, different criteria should be used to qualify stenosis for diagnosing patients with a moderate risk of having a stroke. Intima-media thickness and plaque classification software could analyze the intimamedia thickness (IMT), calculate grey scale median (GSM), make Lal classification, calculate Dajani heterogeneity and echolucency indices, and form thrombus characterization. IMTPC supports many image formats, such as tif, bmp, jpeg, ico, jif, jfif, koa, pcd, pcx, png, pbm, pgm, ppm, ras, targa, and

5 2 tga etc. Figure 5 is the interface of the IMT analysis, and Figure 6 is the interface of the plaque analysis. If the imported image is color image, it is converted to monochromatic image first by the weighted mean relationship [10]: ( ) ( ) ( ) ( I yx, = I yx, I yx, I yx, (1) R G B ) In the toolbar, there are two icons: (IMT analysis) and (plaque analysis), by which user could switch between two statuses. 2. IMT Analysis Atherosclerosis remains the most significant cause of morbidity and mortality in the United States and Canada. Myocardial infarction, stroke and lower extremtity ischemia are three of the major consequences of atherosclerosis. The most popular variable measured to quantitate arterial narrowing has been the percentage of diameter reduction. The narrowest lumen in the region of the plaque is compared to a selected reference diameter. The residual lumen in the carotid artery bifurcation has been compared to the normal diameter distal to the carotid bulb. This measurement is effective in selecting candidate for surgery such as carotid endarterectomy. The basic structure of artery is explained carefully, and the algorithm of positioning the wall, M-Line and I-Line, calculating the diameter, near IMT and far IMT, are also provided here. Figure 5 is the interface of the IMT analysis. There have already a few algorithms about calculating the IMT and diameter, such as [10], [12]. Consolatina only use the magnitude of the gradient value to judge the M- Line and I-Line, causing lots of confusion as the M-Line and I-Line should be the local minimum values first. So the author proposed one new algorithm that first determine the

6 3 wall position, then search M-Line, finally find the I-Line. The results are favorable, and one report of IMT analysis (Figure 14) is provided at the end of this chapter Basics of Artery Artery walls are made up of three layers (or tunicae): intima, media, and adventitia. The tunica intima is composed of a single layer of flattened epithelial cells with an underlying basement membrane. The tunica media comprises an inner layer of elastic fibers and an outer layer of circular smooth muscle. The tunica adventitia is composed of collagenous fibers. In ultrasound impulse-based analysis, blood (arterial lumen) and wall layers exhibit differences in wave reflection capability due to differences in density and elasticity. Hence, the arterial lumen and tunica media do not reflect ultrasound waves, thereby allowing the intima lumen and adventitia media interfaces to be identified [10]. The longitudinal scansion of the common carotid allows three interfaces to be highlighted for each of the two walls (near wall and far wall). Starting from the upper side of the image (Figure 1): the first line edge 1) corresponds to the external surface of the adventitia layer (also called periadventitia-adventitia layer), the second one 2) corresponds to the adventitia media interfac, and the third one 3) is the intima lumen interface. The fourth down is the far wall, coming from inside the artery instead of outside. This implies that the same interfaces of the near wall are found but in an inverse order: the first one 4) is the lumen intima interface, the second 5) is the media adventitia interface, and the third 6) is the external surface of the adventitia layer.

7 4 Figure 1. Structure of the carotid artery with interfaces: 1) periadventitia-adventitia (NW), 2) adventitia-media (NW), 3) intima-lumen (NW), 4) lumen-intima (FW), 5) mediaadventitia (FW), 6) adventitia-periadventitia (FW) Intima-media Thickness Algorithm Press button Auto Analysis, IMT analysis will be started for the image. The near I line, M line and far I line, far M-Line will be displayed on the position identified, and the data of the IMT thickness and diameter could also be provided in the chart. It can be seen from Figure 2 that the far wall has the maximum value on the left side, and the near wall has the maximum value on the right side. The detailed algorithm is explained step by step as follows.

8 5 Figure 2. One vertical cross-sectional line is captured by checking the button Cross Line in the IMT Options panel, the original data (blue) and its gradient value (red) are plotted in the chart area. The left side is far wall area, and the right side is near wall area Calibration In the toolbar, check button Calibrate should be made sure checked first, then the user could adjust the position of two circle centers, and input the actual distance in mm into the edit box Calibrate Length. The length unit in mm/pixel will be calculated and displayed in the text box. To uncheck the button Calibrate will stop this activity Preprocessing the Data First all the pixels in the captured area are mapped into the range [0,255] based on the range [minmum value, maximum value] in the area. Calculate the gradient value for pixels from 0 to Height 1. ( ) ( ) I i + 1-Ii,0 i Height 2 Gi ()=, (2) G( i 1), i = Height 1

9 6 where Hight is the length of the cross-sectional line, I ( i ) represents the intensity value of the pixel Calculate the Far wall, Far M-Line and Far I-Line The local maximum point i has the conditions ( ) G( i) The local minimum point i has the conditions G i 1 0&& < 0 (3) ( 1) 0&& ( ) 5&& ( 1) ( ) G i < G i > G i < G i (4) From the left side, search the far wall that is the highest maximum point nearest to the center on the left side, in other words, if several local maximum points have the same value, the one nearest to center is the position of far wall. After determine the position of far wall, the far M-Line can be continually searched among the local minimum points from far wall position to the center. I( FarLocalMin( j)) < 0.6 I( Farwall) or I( FarLocalMin( j)) < FarMLineValvevalue, where I( FarLocalMin( j )) is the intensity of the j th local minimum pixel, I( Farwall ) is the intensity of the far wall, F armlinevalvevalue is the valve value for far M-Line adjusted by the user, usually about 60. In the local minimum pixels, starting from the far M-Line to the center, we could find the far I-Line. I( FarLocalMin( j)) (5) < FarILineValvevalue, (6) where FarILineValvevalue is the valve value for far I-Line adjusted by the user, usually about 3.

10 Calculate the Near wall, Near M-Line and Near I-Line The local maximum point i has the conditions ( ) G( i) The local minimum point i has the conditions G i 1 > 0 and 0 (7) ( 1) 5 and ( ) 0 and ( 1) ( ) G i < G i > G i < G i (8) From the right side, search the near wall that is the highest maximum point nearest to the center on the right side, in other words, if several local maximum points have the same value, the one nearest to center is the position of near wall. After the position of near wall is determined, the near M-Line can be continually searched among the local minimum points from near wall position to the center. I( NearLocalMin( j)) < 0.6 I( Nearwall) or I( NearLocalMin( j)) < NearMLineValvevalue, where I( NearLocalMin( j )) is the intensity of the j th local minimum pixel, I( Nearwall ) is the intensity of the near wall, N earmlinevalvevalue is the valve value for near M-Line adjusted by the user, usually about 60. In the local minimum pixels, starting from the near M-Line to the center, we could find the near I-Line. I( NearLocalMin( j)) (9) < NearILineValvevalue, (10) where NearILineValvevalue is the valve value for near I-Line adjusted by the user, usually about 3 for high quality image, or 30 for low quality.

11 Post-processing Post-processing include a few tools that could improve the appearance of the M- Line and I-Line. From Figure 7, segmentation is to divide the area into several parts to interpolate or apply delta smoothing on each segment, because the vessel may change direction for several times causing heavy deviation of the mean value. Moving smoothing method is also provided to make the appearance better, and the pixel number is usually set 10. If it is increased, the M-Line and I-Line become smoother Segmentation It is suggested to choose delta smooth method, which could adjust the abrupt point to the fine position. User could define the segments manually or let it work automatically which will divide the whole region width evenly. Setting check button Manual Segment checked could start the manual segment process, and user just clicks on the position where user thinks the vessel is changing direction. The segment number will be displayed in the edit box Seg. No.. To uncheck that Manual Segment button will end up this segment definition process Manual Adjustment In the Manual adjustment frame, four buttons, Start, Undo, Redo and Finish, are applied to modify the line chosen by clicking on the radio button named after that line. Start is to start the modification process, and this will change the chosen line color into the color of modified status. Undo could return to previous modified result, redo could go ahead, finish ends up the modification process and restores chosen line color.

12 Calculate the Diameter, Near IMT, Far IMT Diameter is the distance between the near M-Line and the far M-Line. Near IMT is the distance between the near M-Line and near I-Line. Far IMT is the distance between the far M-Line and far I-Line. We can further calculate the mean, median, maximum value, 95% maximum value, minimum value, 95% minimum value for diameter, near IMT and far IMT. 95% maximum value is the value that is greater than 95% of all values. Similarly, 95% minimum value refers to the value that is smaller than 95% of all values Cross-sectional Line Analysis In the label IMT Option of Table in the right window, choose Check Button Cross Line to enable the capture of cross-sectional line. Click one point in the rectangle area, the gray magnitude of the points along the vertical direction of the rectangle will be displayed in the chart area. Figure 2 is created by this function. 3. Plaque Classification Plaque classification need a few steps, first is to capture the blood area and wall area, whose histogram distributions are displayed on the panel drawing area, then press GSM button to mapping the whole image area based on the mapping rule that blood median value is mapped into 0, the wall median value is mapped into 200. In [11], Lal provided gray value assignment for plaque classification, and defined five classes, blood (0-4), fat (8-26), muscle (41-76), fibrous tissue ( ), calcium ( ). The author further makes it easier to assign gray area with different color, and

13 10 two reports by two groups of color classification are also provide at the end of this chapter Some Important Evaluation Parameters Based on the research work of Khalil [1], a few evaluation parameters are summarized here to make statistical analysis of the plaque GSM classification. 1) The number of pixels in each region is counted and related, percent wise, to the total number of pixels. 2) Deviation square (DEVSQ) of the percent areas for each region is expressed 2 as ( x x), and calculated as the summation of the differences between each regional area and average area. Khalil summarized in p129 of [1] that the number of classes determines the value of max DEVSQ for a totally homogeneous plaque. 3) The plaque composition index or PCI is defined as (11), that compares plaque complexity between classifications in different number of regions. PCI DEVSQ = (11) Max( DEVSQ) 4) The plaque heterogeneity index or PHI is defined as 1-PCI to express maximum heterogeneity as PHI equal to 1 rather than 0. PHI DEVSQ = 1 (12) Max( DEVSQ) 5) PLI is defined as the percentage area of the n regions with lowest echo amplitudes in a N-region classification ( n N ). 6) Coefficient of variance (COVAR) is defined as standard deviation divided by average value.

14 11 7) Variability index (VI) is defined as {maximum minus minimum value} divided by average value GSM Adjustment GSM analysis needs three steps: blood area choice, wall area choice, and GSM adjustment for whole image. Each step could provide the histogram curve and some important values, especially median value. GSM adjustment will utilize the median value of blood area and wall area, and map the image gray value linearly where blood median value is 0, wall median value is Draw Plaque Area There are four buttons used for plaque polygon area definition. Start button begins the process, then user can click mouse on the image to define polygon area. You could also modify the chosen position. Undo button will return to previous step, but redo will go to next step. Finish button ends up this process and calculates the covered area Color Classification This software provides two basic gray scale classification methods: Lal method and JVC method. Lal method defines five classes, blood (0-4), fat (8-26), muscle (41-76), fibrous tissue ( ), calcium ( ). JVC method divides the whole gray range (0,255) by interval 20, and forms thirteen classes. User could see the class color definition or even modify them by click the button Color Assignment. In the Class Assignment window, user should make sure the modification in the range of total class number. If you want to change the class number,

15 12 just change the Class Number value on the top first. If you want to edit the title, gray from and to value, you first choose the cell, then input the value to the edit box, and click Edit. In the Plaque dialogue, the modified color assignment could be saved in one file by click Save, or user could load previous defined color assignment from one file by click Load. The suffix of the file name is.clr. If user wants to classify the plaque area with his own color assignment, just click Classify in the User Classification frame. 4. Software Development 4.1. Classes and their Functions 1) rtflib.h, and rtflib.cpp are used to create RTF file 2) font.h/cpp, mshflexgrid.h/cpp, picture.h/cpp, recordset.h/cpp are used to control the Flex Grid operation. 3) TabCtrlSSL.h/cpp are used for Table operation. The sub labels, including IMT operation (Figure 7), plaque operation (Figure 8), subject panel (Figure 9), display panel (Figure 10), and compliance panel (Figure 12), are controlled separately by TabIMTOptions.h/cpp, TabPlaque.h/cpp, TabSubject.h/cpp, TabCompliance.h/cpp, TabDisplay.h/cpp Installation and the Registration of the ActiveX Driver 1) Copy files in directory cimt to hard disk. The files include MFC42D.DLL, MSVCP60D.DLL, MSVCRTD.DLL, FreeImage.dll, XGRAPH10d.dll, regsvr32.exe, MSHFLXGD.OCX, and cimt.exe. 2) Install Microsoft High Flex-grid Active-X by executing command as follows.

16 13 regsvr32.exe MSHFLXGD.OCX 3) User could execute cimt.exe to start IMT and Plaque analysis Information for Research 8) The legend include the x-coordinate of the point in the rectangle, the y-coordinate could be obtained from the bottom status bar area in the main window. 9) The information in the status bar, for example 363,241,Rect[44,31], means that the point coordinate in the window is (363, 241), and its coordinate in rectangle is (44,31). 10) The coordinate direction of rectangle in which left-bottom corner is original point is different from that of window where the left-top corner is the original point Functional Control Panel The control panels on the bottom of the right frame include five sub-labels, each of which has unique functions that are important for IMT calculation and plaque component classification IMT Operation Panel The IMT operation panel (Figure 7) is used for IMT analysis. Automatic analysis could be done when user press button Auto Analysis, it applies delta smooth, interpolation, moving smooth method. The user could make modification from Manual Adjustment area. The configuration changed by user could be saved into one.cnf configure file. The user could load it later. Check button Cross Line is used to draw the gray scale curve of vertical line in the captured rectangle area at the mouse position.

17 14 There values are very important to achieve good results: the pixels length for smooth method, the valve values for near I-line and far I-line. If the noise is too high in the blood area, it is suggested to set the I-line valve value higher, such as 10, or even 30, this will let the IMT thickness become small. If image quality is very good, the I-line valve value could be set 3 just bigger than 0, the thickness is accurate Plaque Operation Panel Plaque operation panel (Figure 8) is used for plaque analysis. On the panel, there are several steps to make plaque analysis, first is to capture blood area, second is to capture the wall area, third is to make GSM adjustment, fourth is to draw polygon area to capture the plaque, finally the user could apply Lal classification, JVC classification or user defined classification methods to classify the plaque components Class Color Assignment Panel Color assignment panel (Figure 11) is used to define class number and assign the gray range and color to each class. User could save the new definition by choosing Save button in Plaque panel, or load one class definition file by pressing Load button Report Panel Report panel (Figure 9) is used to create report for IMT or Plaque analysis, and could also create RTF format file supported by Microsoft Word, and user could configure the font, character size, and image size.

18 15 This panel needs user input a few information about the patient, Name, Medical No., Location, Angle, and Body Side. You can insert or delete some help information from the combo box, all these information could be saved to configure file. User could load one of the records, and continue make other analysis on it. RTF setup panel (Figure 13) is used to configure the RTF output page arrangement. It is activated by pressing button RTF Setup Display Panel Display panel (Figure 10) is the place where user could change the line size and color in figure and chart. 11) Chart line configuration User could modify the line width and color for the curves of near IMT, far IMT and diameter in IMT analysis, histogram line in GSM analysis, cross line in cross-sectional analysis. 12) Figure line configuration User could modify the line color for near M-line, near I-line, far I-line and far M- line in IMT analysis Compliance Panel Compliance panel (Figure 12) is used to create test images according to user s choice. User could also apply analysis to the test image especially small size image that is not convenient to be processed by opening directly.

19 16 5. Application IMTPC software could not only be used for blood vessel volumetric calculation, plaque components classification, but also for experimental research. The author here applies it to the research of phantom images. This software still needs to be improved for future new requirements Contrast-Detail Study The author used the developed research tool IMTPC (see Chapter 7) to classify the pixels into 13 classes (Figure 3) or 5 classes (Figure 4) Clinical Application This program has also been used for blood vessel images obtained from the Toledo Hospital, there are three reports created by IMTPC software for the ultrasonic images. The first one is the intima-media thickness report of the blood vessel, the second and third reports are respectively the Lal classification and JVC classification for the plaque in the vessel.

20 17 Figure 3. The reconstructed phantom cone by HFR-91 is classified by 13 classes Figure 4. The reconstructed phantom cone by HFR-91 is classified by 5 classes

21 18 Figure 5. The IMT analysis interface Figure 6. The plaque analysis interface

22 19 Figure 7. IMT operation panel Figure 8. Plaque operation panel Figure 9. Report panel

23 20 Figure 10. Display panel Figure 11. Class color assignment panel

24 21 Figure 12. Compliance panel Figure 13. RTF page setup panel

25 22 6. Appendices: Three Reports The first report is about intima-media thickness analysis. Intima-Media Thickness Report Name: Zhaohui Wang MRN: 0001 DATE: 06/29/05 Location: Dist Internal Carotid Angle: none

26 23 Title Far IMT Near IMT Diameter Mean Median Deviance Min Max % Min % Max Figure 14. One IMT report

27 24 method. The second report is for blood plaque components analysis using Lal classification Blood Plaque Report Name: Zhaohui Wang MRN: 0001 DATE: 06/29/05 Location: Dist Internal Carotid Angle: none

28 25 Title Gray From To Pixel No% Color Default Blood Fat Muscle Fibrous Tissue Calcium Figure 15. One plaque report of Lal classification method

29 26 method. The third report is for blood plaque components analysis using JVC classification Blood Plaque Report Name: Zhaohui Wang MRN: 0001 DATE: 06/29/05 Location: Dist Internal Carotid Angle: none

30 27 Title Gray From To Pixel No% Color Default item item item item item item item item item item item item item Figure 16. One plaque report of JVC classification method

31 28 Reference [1] Khalil Fayad Dajani. Analysis of Carotid and Femoral Stenosis and Lesions with Three-dimensional Ultrasound. Doctor of Philosophy 2000, The University of Toledo, August 2000 [2] Richard N. Czerwinski, Douglas L. Jones, and William D. O Brien, Jr., Line and Boundary Detection in Speckle Images, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 7, NO. 12, Dec [3] Sverre Holm. Bessel and Conical Beams and Approximation with Annular Arrays. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 45(3), , May 1998 [4] P.M.Morse, Theoretical Acoustics. New Jersey Princeton University Press, ch.6, 1968 [5] Michael S. Patterson and F. Stuart Foster. Acoustic Fields of Conical Radiators. IEEE Trans. On Sonics and Ultrasonics, SU-29(2), 83-92, March 1982 [6] J. Durnin. Exact Solutions for Nondiffracting beams. I. The Scalar Theory. Journal Opt. Soc. Am. A, 4(4), , April 1987 [7] J. W. Goodman, Introduction to Fourier Optics. New York, N.Y.: McGraw-Hill, Ch. 2-4, 1968 [8] 2005 IEEE International Ultrasonics Symposium, Conference centre De Doelen, Rotterdam, The Netherlands, September (05/12/05 Submitted). [9] Yongjian Yu, Acton, S.T. Edge detection in ultrasound imagery using the instantaneous coefficient of variation. Image Processing, IEEE Transactions on, Volume 13, Issue 12, Dec Page(s):

32 29 [10] Consolatina Liguori, Alfredo Paolillo et al. An Automatic Measurement System for the Evaluation of Carotid Intima-Media Thickness. IEEE Trans. On Instrumentation and Measurement, 50(6), , Dec [11] Brajesh K. Lal et al. Pixel distribution analysis of B-mode ultrasound scan images predicts histologic features of atherosclerotic cartotid plaques.journal of Vascular Surgery, , June 2002 [12] Milan Sonka, Weidong Liang, Ronald M.Lauer. Automated Analysis of Brachial Ultrasound Image Sequences: Early Detection of Cardiovascular Disease via Surrogates of Endothelial Function. IEEE Trans. on Medical Imaging, 21(10), , Oct [13] Andrew N.Nicolaides etal. Chaper 12, Evaluation of Carotid Plaque Morphology, Cerebrovascular Disease [14] Giorgio M.Biasi etal. Carotid Plaque Echolucency Increases the Risk of Stroke in Carotid Stenting: the Imaging in Carotid Angioplasty and Risk of Stroke (ICAROS) Study. Circulation, , Aug. 10,