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Heart attack diagnosis from DE-MRI images

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Automatic Quantification of Myocardial Infarction from Delayed Enhancement MRI …

Automatic Quantification of Myocardial Infarction from Delayed Enhancement MRI
(My master thesis research work)

Published in: Health & Medicine, Technology

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  • Let me begin with the introduction of … What is MI?Myocardial Infarction (MI), is commonly known as heart attack. Myocardial came from the termmyocardium means heart muscle, “Infarction” refers to the tissue death caused by the blocking of the coronary artery by the plaque that will prevent supplies of blood and oxygen. Based on medical studies, infarct area may contain heterogeneous zones, we define as HIA, which may have different recovery. These areas consist of infarct core isfully infarcted tissueperi-infarct, or the border zone;is a mixture of healthy and diseased myocardium. And microvascular obstruction (or calledno-reflow phenomena) is area with permanent absence of tissue perfusion.
  • Particularly, DE-MRI can be considered as the gold standard for the assessment of myocardial viability.The name is delay-enhancement MRI because the images are taken on around 10 – 15 minutes after the gadolonium-based contrast agent injection. The acquisition of these MRI slices is consecutively performed from basal to apex part of the heart. An example of a DE-MR image is shown here.. This is myocardium which surrounds the LV, with the myocardium border consists of endocardium and epicardium. Infarct region is indicated with this hyper-enhanced area.
  • Particularly, DE-MRI can be considered as the gold standard for the assessment of myocardial viability.The name is delay-enhancement MRI because the images are taken on around 10 – 15 minutes after the gadolonium-based contrast agent injection. The acquisition of these MRI slices is consecutively performed from basal to apex part of the heart. An example of a DE-MR image is shown here.. This is myocardium which surrounds the LV, with the myocardium border consists of endocardium and epicardium. Infarct region is indicated with this hyper-enhanced area.
  • Particularly, DE-MRI can be considered as the gold standard for the assessment of myocardial viability.The name is delay-enhancement MRI because the images are taken on around 10 – 15 minutes after the gadolonium-based contrast agent injection. The acquisition of these MRI slices is consecutively performed from basal to apex part of the heart. An example of a DE-MR image is shown here.. This is myocardium which surrounds the LV, with the myocardium border consists of endocardium and epicardium. Infarct region is indicated with this hyper-enhanced area.
  • Particularly, DE-MRI can be considered as the gold standard for the assessment of myocardial viability.The name is delay-enhancement MRI because the images are taken on around 10 – 15 minutes after the gadolonium-based contrast agent injection. The acquisition of these MRI slices is consecutively performed from basal to apex part of the heart. An example of a DE-MR image is shown here.. This is myocardium which surrounds the LV, with the myocardium border consists of endocardium and epicardium. Infarct region is indicated with this hyper-enhanced area.
  • Currently, the main interest for the cardiologists is this HIA which must be clearly defined to validate its correlation to the patient’s arrhythmia and mortality after heart attack.However, problem arose since HIA is hard to be distinguished accurately by human eye.  Moreover, no automatic solution available.As a consequence, an automatic quantification of infarct is becoming very important . Thus, our project goals are to:Develop automatic segmentation and quantification methods, by…AndImplement the method into a clinical software to help the cardiologistssince doing the manual segmentation is time consuming and sometimes impossibleIn order to be able to solve these problem, a bibliographic survey was done and reviewed in the following slides
  • Currently, the main interest for the cardiologists is this HIA which must be clearly defined to validate its correlation to the patient’s arrhythmia and mortality after heart attack.However, problem arose since HIA is hard to be distinguished accurately by human eye.  Moreover, no automatic solution available.As a consequence, an automatic quantification of infarct is becoming very important . Thus, our project goals are to:Develop automatic segmentation and quantification methods, by…AndImplement the method into a clinical software to help the cardiologistssince doing the manual segmentation is time consuming and sometimes impossibleIn order to be able to solve these problem, a bibliographic survey was done and reviewed in the following slides
  • There are several approaches for determining the infarct area.First approach is standard deviation based intensity thresholding, which is the most popular method. Commonly, threshold for infarct is 2 SD above the mean of the normal myocardium. Full width half maximum threshold then was proposed as the half of the maximum signal intensity inside the infarct region. But, these two methods are lack of spatial coherence. Thus, a more sophisticated results were obtained by combining Standard deviation and FWHM-based thresholding with regional feature analysis. Apart from simple intensity thresholding, other methods implement watershed and clustering technique such as k-means, or mixture model and fuzzy membership.
  • There are several approaches for determining the infarct area.First approach is standard deviation based intensity thresholding, which is the most popular method. Commonly, threshold for infarct is 2 SD above the mean of the normal myocardium. Full width half maximum threshold then was proposed as the half of the maximum signal intensity inside the infarct region. But, these two methods are lack of spatial coherence. Thus, a more sophisticated results were obtained by combining Standard deviation and FWHM-based thresholding with regional feature analysis. Apart from simple intensity thresholding, other methods implement watershed and clustering technique such as k-means, or mixture model and fuzzy membership.
  • There are several approaches for determining the infarct area.First approach is standard deviation based intensity thresholding, which is the most popular method. Commonly, threshold for infarct is 2 SD above the mean of the normal myocardium. Full width half maximum threshold then was proposed as the half of the maximum signal intensity inside the infarct region. But, these two methods are lack of spatial coherence. Thus, a more sophisticated results were obtained by combining Standard deviation and FWHM-based thresholding with regional feature analysis. Apart from simple intensity thresholding, other methods implement watershed and clustering technique such as k-means, or mixture model and fuzzy membership.
  • There are several approaches for determining the infarct area.First approach is standard deviation based intensity thresholding, which is the most popular method. Commonly, threshold for infarct is 2 SD above the mean of the normal myocardium. Full width half maximum threshold then was proposed as the half of the maximum signal intensity inside the infarct region. But, these two methods are lack of spatial coherence. Thus, a more sophisticated results were obtained by combining Standard deviation and FWHM-based thresholding with regional feature analysis. Apart from simple intensity thresholding, other methods implement watershed and clustering technique such as k-means, or mixture model and fuzzy membership.
  • There are several approaches for determining the infarct area.First approach is standard deviation based intensity thresholding, which is the most popular method. Commonly, threshold for infarct is 2 SD above the mean of the normal myocardium. Full width half maximum threshold then was proposed as the half of the maximum signal intensity inside the infarct region. But, these two methods are lack of spatial coherence. Thus, a more sophisticated results were obtained by combining Standard deviation and FWHM-based thresholding with regional feature analysis. Apart from simple intensity thresholding, other methods implement watershed and clustering technique such as k-means, or mixture model and fuzzy membership.
  • Concerning the HIA segmentation, since peri-infarct consist of healthy and infarcted tissues, the appearance is gray, As shown in this image, infarct core is red whereas peri-infarct is in yelow.Yet, only a few papers have tackled segmentation of HIA and all of them are only based on simple intensity thresholding. According to the clinical validation, Schmidt method based on FWHM criteria gave the best definition.Meanwhile, no-reflow area is very severe infarct, defined as black region within the infarct core. But, from all reviews, none of them had tried to segment or quantify this region so that there is no exact threshold definition.
  • Concerning the HIA segmentation, since peri-infarct consist of healthy and infarcted tissues, the appearance is gray, As shown in this image, infarct core is red whereas peri-infarct is in yelow.Yet, only a few papers have tackled segmentation of HIA and all of them are only based on simple intensity thresholding. According to the clinical validation, Schmidt method based on FWHM criteria gave the best definition.Meanwhile, no-reflow area is very severe infarct, defined as black region within the infarct core. But, from all reviews, none of them had tried to segment or quantify this region so that there is no exact threshold definition.
  • Concerning the HIA segmentation, since peri-infarct consist of healthy and infarcted tissues, the appearance is gray, As shown in this image, infarct core is red whereas peri-infarct is in yelow.Yet, only a few papers have tackled segmentation of HIA and all of them are only based on simple intensity thresholding. According to the clinical validation, Schmidt method based on FWHM criteria gave the best definition.Meanwhile, no-reflow area is very severe infarct, defined as black region within the infarct core. But, from all reviews, none of them had tried to segment or quantify this region so that there is no exact threshold definition.
  • Regarding the quantification.The most common method for volumetric quantification is contiguous short-axis model, which is the sum of corresponding area on all contiguous slices and multiplied by the slice thickness. Then, for representation, it is strongly recommended to use bull's eye plot with this standard 17 segment model. Before, the left ventricle must be divided into equal thirds in the long axis view to generates basal, mid-cavity, and apical sections. Then, basal and mid-cavity are divided into six segments, whereas apical part only have four segments. This figure shows the 17-segment model with each color is associated with the perfusion of a specific coronary…So far, we’ve seen the reviews of state-of-the art In the next slide, I’ll explain the solution wit our proposed method..
  • Regarding the quantification.The most common method for volumetric quantification is contiguous short-axis model, which is the sum of corresponding area on all contiguous slices and multiplied by the slice thickness. Then, for representation, it is strongly recommended to use bull's eye plot with this standard 17 segment model. Before, the left ventricle must be divided into equal thirds to generates basal, mid-cavity, and apical sections. Then, basal and mid-cavity are divided into six segments, whereas apical part only have four segments. This figure shows the 17-segment model with each color is associated with the perfusion of a specific coronary…So far, we’ve seen the reviews of state-of-the art In the next slide, I’ll explain the solution wit our proposed method..
  • In our study, images were acquired from 20 patients with acute MI who conducted MRI examination less than two weeks after experienced heart attack. The patients were examined with 3T MRI and we used the phase images from PSIR sequenceHere is the block diagram of the complete process Since it is automatic, the only input required from the user is the correct myocardium borders; means epicardium and endocardium.Then, images are ready to be pre-processed before the infarct segmentation. After, HIA segmentation is performed and the results will be used for quantification and representation
  • The first step is pre-processing..Initially, we increased the image resolution by bicubic interpolation to reduce the risk for inaccurate quantification. Then, contrast enhancement was performed to make the normal myocardium as dark as possible but keep the infarct area bright.besides ,position of a patient’s breath-hold is not the same from one acquisition to another, leading to displacement of heart location between image slices. Hence, rigid registration to the center of myocardium must be performed to ensure that myocardium propagates correctly through entire 3D image stack. Then, we obtained the aligned image slices..After that, we applied image filtering to smooth the histogram space.
  • The first step is pre-processing..Initially, we increased the image resolution by bicubic interpolation to reduce the risk for inaccurate quantification. Then, contrast enhancement was performed to make the normal myocardium as dark as possible but keep the infarct area bright.besides ,position of a patient’s breath-hold is not the same from one acquisition to another, leading to displacement of heart location between image slices. Hence, rigid registration to the center of myocardium must be performed to ensure that myocardium propagates correctly through entire 3D image stack. Then, we obtained the aligned image slices..After that, we applied image filtering to smooth the histogram space.
  • The first step is pre-processing..Initially, we increased the image resolution by bicubic interpolation to reduce the risk for inaccurate quantification. Then, contrast enhancement was performed to make the normal myocardium as dark as possible but keep the infarct area bright.besides ,position of a patient’s breath-hold is not the same from one acquisition to another, leading to displacement of heart location between image slices. Hence, rigid registration to the center of myocardium must be performed to ensure that myocardium propagates correctly through entire 3D image stack. Then, we obtained the aligned image slices..After that, we applied image filtering to smooth the histogram space.
  • The first step is pre-processing..Initially, we increased the image resolution by bicubic interpolation to reduce the risk for inaccurate quantification. Then, contrast enhancement was performed to make the normal myocardium as dark as possible but keep the infarct area bright.besides ,position of a patient’s breath-hold is not the same from one acquisition to another, leading to displacement of heart location between image slices. Hence, rigid registration to the center of myocardium must be performed to ensure that myocardium propagates correctly through entire 3D image stack. Then, we obtained the aligned image slices..After that, we applied image filtering to smooth the histogram space.
  • Pre-processed myocardium is the input for infarct segmentation stage. Based on our findings, the distribution shape of myocardium is bimodal histogram with two Gaussian. Hence, we applied a Gaussian mixture model to separate between normal and infarct region. Myocardium intensity can be modeled by weighted Gaussian distributions, given the voxel intensity values x, The unknown Gaussian mixture parameters (means, variances and proportion) are tuned using an iterative expectation maximization EM algorithm. In E-step, the responsibility is calculated to be used to estimate new means, covariance and weights in the M-step, iteratively. When it is converged, we stop the and the optimal hyper-enhanced threshold is the average of these means…The output for this step is region having intensities above this threshold.
  • Pre-processed myocardium is the input for infarct segmentation stage. Based on our findings, the distribution shape of myocardium is bimodal histogram with two Gaussian. Hence, we applied a Gaussian mixture model to separate between normal and infarct region. Myocardium intensity can be modeled by weighted Gaussian distributions, given the voxel intensity values x, The unknown Gaussian mixture parameters (means, variances and proportion) are tuned using an iterative expectation maximization EM algorithm. In E-step, the responsibility is calculated to be used to estimate new means, covariance and weights in the M-step, iteratively. When it is converged, we stop the and the optimal hyper-enhanced threshold is the average of these means…The output for this step is region having intensities above this threshold.
  • Pre-processed myocardium is the input for infarct segmentation stage. Based on our findings, the distribution shape of myocardium is bimodal histogram with two Gaussian. Hence, we applied a Gaussian mixture model to separate between normal and infarct region. Myocardium intensity can be modeled by weighted Gaussian distributions, given the voxel intensity values x, The unknown Gaussian mixture parameters (means, variances and proportion) are tuned using an iterative expectation maximization EM algorithm. In E-step, the responsibility is calculated to be used to estimate new means, covariance and weights in the M-step, iteratively. When it is converged, we stop the and the optimal hyper-enhanced threshold is the average of these means…The output for this step is region having intensities above this threshold.
  • To refine the result, we must perform the following steps..First is the 2-way 3D connectivity analysis for false positive compensation due to noisy acquisition, blood pool artifact, or PVE.. PVE is a case when a single voxel contains a mixture of multiple tissues, which can lead to a segmentation error. As our general assumption is that the infarct region must be continuous in 3D image stacks, we applied Forward connectivity from basal to apical, followed by reverse connectivity to remove unconnected region to neighboring slices.Then, in feature analysis, we remove any bright regions less than a minimum sizeYet, if the distance of a region is far from endocardial, it is discarded.Lastly, solidity must be checked because the infarct region is compact.Output for infarct region is shown here…
  • To refine the result, we must perform the following steps..First is the 2-way 3D connectivity analysis for false positive compensation due to noisy acquisition, blood pool artifact, or PVE.. PVE is a case when a single voxel contains a mixture of multiple tissues, which can lead to a segmentation error. As our general assumption is that the infarct region must be continuous in 3D image stacks, we applied Forward connectivity from basal to apical, followed by reverse connectivity to remove unconnected region to neighboring slices.Then, in feature analysis, we remove any bright regions less than a minimum sizeYet, if the distance of a region is far from endocardial, it is discarded.Lastly, solidity must be checked because the infarct region is compact.Output for infarct region is shown here…
  • The result of infarct segmentation in the previous step is the input for the infarct core segmentation. Based on the consensus of the doctors in the hospital, we defined the core-infarct threshold with FWHM criterion.After, the no-reflow area is included by morphological filling and closing operation with a bar shape kernel in four orientations. Moreover, we performed the connectivity analysis and set the minimum size of infarct core.
  • The result of infarct segmentation in the previous step is the input for the infarct core segmentation. Based on the consensus of the doctors in the hospital, we defined the core-infarct threshold with FWHM criterion.After, the no-reflow area is included by morphological filling and closing operation with a bar shape kernel in four orientations. Moreover, we performed the connectivity analysis and set the minimum size of infarct core.
  • After infarct core region is obtained, we solve the segmentation for peri-infarct. Given that the intensity of peri-infarct is between the normal tissue and infarct core, and the location is surrounding the infarct core;fuzzy membership associated with spatial and intensity is the best to define peri-infarct, as it may be regarded as 'fuzzy' area. For this reason, we applied the spatial-weighted fuzzy clustering.So, the Input to this step is the myocardium pixels having the intensities above the lower mean and below infarct core threshold.Then, optimization of an objective function must be solved for the optimal cluster center and degree of membership. By incorporating the spatial information, this fuzzy membership u*ik is can be extended as:where pik is the spatial weight, calculated as the Euq. distance of myocardium pixels to the infarct core regions Consequently, a pixel gets higher membership degree when it has a high signal intensity uik and close to infarct core pik.Once this clustering algorithm has converged, a defuzzication process was carried on to obtain the segmentation like this.
  • After infarct core region is obtained, we solve the segmentation for peri-infarct. Given that the intensity of peri-infarct is between the normal tissue and infarct core, and the location is surrounding the infarct core;fuzzy membership associated with spatial and intensity is the best to define peri-infarct, as it may be regarded as 'fuzzy' area. For this reason, we applied the spatial-weighted fuzzy clustering.So, the Input to this step is the myocardium pixels having the intensities above the lower mean and below infarct core threshold.Then, optimization of an objective function must be solved for the optimal cluster center and degree of membership. By incorporating the spatial information, this fuzzy membership u*ik is can be extended as:where pik is the spatial weight, calculated as the Euq. distance of myocardium pixels to the infarct core regions Consequently, a pixel gets higher membership degree when it has a high signal intensity uik and close to infarct core pik.Once this clustering algorithm has converged, a defuzzication process was carried on to obtain the segmentation like this.
  • After infarct core region is obtained, we solve the segmentation for peri-infarct. Given that the intensity of peri-infarct is between the normal tissue and infarct core, and the location is surrounding the infarct core;fuzzy membership associated with spatial and intensity is the best to define peri-infarct, as it may be regarded as 'fuzzy' area. For this reason, we applied the spatial-weighted fuzzy clustering.So, the Input to this step is the myocardium pixels having the intensities above the lower mean and below infarct core threshold.Then, optimization of an objective function must be solved for the optimal cluster center and degree of membership. By incorporating the spatial information, this fuzzy membership u*ik is can be extended as:where pik is the spatial weight, calculated as the Euq. distance of myocardium pixels to the infarct core regions Consequently, a pixel gets higher membership degree when it has a high signal intensity uik and close to infarct core pik.Once this clustering algorithm has converged, a defuzzication process was carried on to obtain the segmentation like this.
  • The last infarct zone to be segmented is the no-reflow area. Input for this step is the infarct core region.There are 3 assumptions applied for this segmentation. First, since no-reflow is dark pixels inside the infarct core, which may have the same intensity as normal regions. No-reflow region is defined as region having intensity lower then this HE threshold.Second, no-reflow region must be adjacent to the endocardiumThird, to limit the allowed extent of no-reflow region, we build a spatial constraint, calculated as the normalized relative distance of myocardium pixels to endo., taken in each angle of radial chord.. Thus, we set the limit to be less than 0.5 since no-reflow is rarely extent near to epicardium. And this is the output..
  • The last infarct zone to be segmented is the no-reflow area. Input for this step is the infarct core region.There are 3 assumptions applied for this segmentation. First, since no-reflow is dark pixels inside the infarct core, which may have the same intensity as normal regions. No-reflow region is defined as region having intensity lower then this HE threshold.Second, no-reflow region must be adjacent to the endocardiumThird, to limit the allowed extent of no-reflow region, we build a spatial constraint, calculated as the normalized relative distance of myocardium pixels to endo., taken in each angle of radial chord.. Thus, we set the limit to be less than 0.5 since no-reflow is rarely extent near to epicardium. And this is the output..
  • The last infarct zone to be segmented is the no-reflow area. Input for this step is the infarct core region.There are 3 assumptions applied for this segmentation. First, since no-reflow is dark pixels inside the infarct core, which may have the same intensity as normal regions. No-reflow region is defined as region having intensity lower then this HE threshold.Second, no-reflow region must be adjacent to the endocardiumThird, to limit the allowed extent of no-reflow region, we build a spatial constraint, calculated as the normalized relative distance of myocardium pixels to endo., taken in each angle of radial chord.. Thus, we set the limit to be less than 0.5 since no-reflow is rarely extent near to epicardium. And this is the output..
  • The last infarct zone to be segmented is the no-reflow area. Input for this step is the infarct core region.There are 3 assumptions applied for this segmentation. First, since no-reflow is dark pixels inside the infarct core, which may have the same intensity as normal regions. No-reflow region is defined as region having intensity lower then this HE threshold.Second, no-reflow region must be adjacent to the endocardiumThird, to limit the allowed extent of no-reflow region, we build a spatial constraint, calculated as the normalized relative distance of myocardium pixels to endo., taken in each angle of radial chord.. Thus, we set the limit to be less than 0.5 since no-reflow is rarely extent near to epicardium. And this is the output..
  • subsequently, segmentation results then are used for quantification step. First we did quantification of areas in each slice…Quantified area in true values is the multiplication of area in pixels by the pixel spacing obtained from DICOM information. Since no-reflow is included in infarct core, total infarct area is calculated as core plus peri-infarct.The volume for the whole myocardium is the summation of the area multiplied by d which is the difference between consecutive slice.The volume is expressed in mm3 and as a percentage of the total myocardium, to know how severe and heterogenic the infarct is
  • subsequently, segmentation results then are used for quantification step. First we did quantification of areas in each slice…Quantified area in true values is the multiplication of area in pixels by the pixel spacing obtained from DICOM information. Since no-reflow is included in infarct core, total infarct area is calculated as core plus peri-infarct.The volume for the whole myocardium is the summation of the area multiplied by d which is the difference between consecutive slice.The volume is expressed in mm3 and as a percentage of the total myocardium, to know how severe and heterogenic the infarct is
  • Afterwards, to see the result qualitatively, the segmented infarct regions are represented visually in Label and contour. We use different color for each; red for core-infarct, yellow for peri-infarct and green for no-reflow region.For quantitative representation, Bull’s eye plot is used. Unfortunately in the real data, apex slice (17th segment) was not available, so that we implement 16-segment model. In this model, sector is color coded according to its severity, which depends on the level of infarct percentage. Exact percentage values are also indicated in each sector.Now, that we’ve discussed the proposed solution, it is time to seeour clinical software in MATLAB GUI
  • I will explain the function of each panel..Menu toolbar, to browse for patient folder (F) and choose the slices, load the saved data (L), save, zoom, n reset the GUI (R)2. Display the current processed image3. Patient information panel4. Navigation to the next/previous slice.5. Status GUI to display the ongoing process6. Manual contouring made by user for endocardium, epicardium, and manual infarct. The user can trace point-by-point the contour and the confirmed drawing will be displayed as blue borders.7. Analysis panel to perform the automatic segmentation for all infarct zones, to assist the semi-automatic segmentation when the user is not satisfied with the automatic result so that the threshold can be specified manually in the myocardium histogram..next is to calculate the quantification parameters, and to represent the global infarct analysis in Bull's eye view.8. Display automatic infarct segmentation results in label or contour. 9. Display all quantification values in area and volumeWhen all of the process have been performed, the user can press 'P' to generate a report for the patient in Excel file containing the patient info, quantification values and the Bull's eye representation.
  • I will explain the function of each panel..Menu toolbar, to browse for patient folder (F) and choose the slices, load the saved data (L), save, zoom, n reset the GUI (R)2. Display the current processed image3. Patient information panel4. Navigation to the next/previous slice.5. Status GUI to display the ongoing process6. Manual contouring made by user for endocardium, epicardium, and manual infarct. The user can trace point-by-point the contour and the confirmed drawing will be displayed as blue borders.7. Analysis panel to perform the automatic segmentation for all infarct zones, to assist the semi-automatic segmentation when the user is not satisfied with the automatic result so that the threshold can be specified manually in the myocardium histogram..next is to calculate the quantification parameters, and to represent the global infarct analysis in Bull's eye view.8. Display automatic infarct segmentation results in label or contour. 9. Display all quantification values in area and volumeWhen all of the process have been performed, the user can press 'P' to generate a report for the patient in Excel file containing the patient info, quantification values and the Bull's eye representation.
  • I will explain the function of each panel..Menu toolbar, to browse for patient folder (F) and choose the slices, load the saved data (L), save, zoom, n reset the GUI (R)2. Display the current processed image3. Patient information panel4. Navigation to the next/previous slice.5. Status GUI to display the ongoing process6. Manual contouring made by user for endocardium, epicardium, and manual infarct. The user can trace point-by-point the contour and the confirmed drawing will be displayed as blue borders.7. Analysis panel to perform the automatic segmentation for all infarct zones, to assist the semi-automatic segmentation when the user is not satisfied with the automatic result so that the threshold can be specified manually in the myocardium histogram..next is to calculate the quantification parameters, and to represent the global infarct analysis in Bull's eye view.8. Display automatic infarct segmentation results in label or contour. 9. Display all quantification values in area and volumeWhen all of the process have been performed, the user can press 'P' to generate a report for the patient in Excel file containing the patient info, quantification values and the Bull's eye representation.
  • I will explain the function of each panel..Menu toolbar, to browse for patient folder (F) and choose the slices, load the saved data (L), save, zoom, n reset the GUI (R)2. Display the current processed image3. Patient information panel4. Navigation to the next/previous slice.5. Status GUI to display the ongoing process6. Manual contouring made by user for endocardium, epicardium, and manual infarct. The user can trace point-by-point the contour and the confirmed drawing will be displayed as blue borders.7. Analysis panel to perform the automatic segmentation for all infarct zones, to assist the semi-automatic segmentation when the user is not satisfied with the automatic result so that the threshold can be specified manually in the myocardium histogram..next is to calculate the quantification parameters, and to represent the global infarct analysis in Bull's eye view.8. Display automatic infarct segmentation results in label or contour. 9. Display all quantification values in area and volumeWhen all of the process have been performed, the user can press 'P' to generate a report for the patient in Excel file containing the patient info, quantification values and the Bull's eye representation.
  • I will explain the function of each panel..Menu toolbar, to browse for patient folder (F) and choose the slices, load the saved data (L), save, zoom, n reset the GUI (R)2. Display the current processed image3. Patient information panel4. Navigation to the next/previous slice.5. Status GUI to display the ongoing process6. Manual contouring made by user for endocardium, epicardium, and manual infarct. The user can trace point-by-point the contour and the confirmed drawing will be displayed as blue borders.7. Analysis panel to perform the automatic segmentation for all infarct zones, to assist the semi-automatic segmentation when the user is not satisfied with the automatic result so that the threshold can be specified manually in the myocardium histogram..next is to calculate the quantification parameters, and to represent the global infarct analysis in Bull's eye view.8. Display automatic infarct segmentation results in label or contour. 9. Display all quantification values in area and volumeWhen all of the process have been performed, the user can press 'P' to generate a report for the patient in Excel file containing the patient info, quantification values and the Bull's eye representation.
  • I will explain the function of each panel..Menu toolbar, to browse for patient folder (F) and choose the slices, load the saved data (L), save, zoom, n reset the GUI (R)2. Display the current processed image3. Patient information panel4. Navigation to the next/previous slice.5. Status GUI to display the ongoing process6. Manual contouring made by user for endocardium, epicardium, and manual infarct. The user can trace point-by-point the contour and the confirmed drawing will be displayed as blue borders.7. Analysis panel to perform the automatic segmentation for all infarct zones, to assist the semi-automatic segmentation when the user is not satisfied with the automatic result so that the threshold can be specified manually in the myocardium histogram..next is to calculate the quantification parameters, and to represent the global infarct analysis in Bull's eye view.8. Display automatic infarct segmentation results in label or contour. 9. Display all quantification values in area and volumeWhen all of the process have been performed, the user can press 'P' to generate a report for the patient in Excel file containing the patient info, quantification values and the Bull's eye representation.
  • I will explain the function of each panel..Menu toolbar, to browse for patient folder (F) and choose the slices, load the saved data (L), save, zoom, n reset the GUI (R)2. Display the current processed image3. Patient information panel4. Navigation to the next/previous slice.5. Status GUI to display the ongoing process6. Manual contouring made by user for endocardium, epicardium, and manual infarct. The user can trace point-by-point the contour and the confirmed drawing will be displayed as blue borders.7. Analysis panel to perform the automatic segmentation for all infarct zones, to assist the semi-automatic segmentation when the user is not satisfied with the automatic result so that the threshold can be specified manually in the myocardium histogram..next is to calculate the quantification parameters, and to represent the global infarct analysis in Bull's eye view.8. Display automatic infarct segmentation results in label or contour. 9. Display all quantification values in area and volumeWhen all of the process have been performed, the user can press 'P' to generate a report for the patient in Excel file containing the patient info, quantification values and the Bull's eye representation.
  • In order to evaluate the performance of our method, the results of infarct quantification will be compared with the ground truth or manual tracing of total infarct region by two observers. The Evaluation for volumetric quantification showed good results, with high correlation between automatic and manual volume and low dispersion in Bland-Altman plot. Errors in volumetric quantification were reported in Table, with the comparison with other availablemethods. These two last methods gave almost equivalent and the lowest average error.
  • In addition, the evaluation for area quantification showed a tight correlation between the automatic and the manual quantification, with high agreement in Bland-Altman analysis. Average errors in infarct area quantification is compared in this Table.We can see that the proposed method gives the smallest average error amongst all methods.
  • Also, ourinfarct segmentation had been evaluated by Kappa coefficient.Indeed, from all the methods, ourproposed method produced the highest kappa coef., showing the most accurate infarct segmentation.
  • Furthermore, visual evaluation of HIA segmentation had been carried on, with the rating score from 1 (very poor) to 5 (perfect). A total of 116 images from the 20 patients were taken for visual evaluation. This table shows that the score for peri-infarct is the lowest since it is very hard to be seen precisely by human eye.
  • However, based on the visual evaluation… these examples show some issues in HIA segmentation.First image represented the difficulty to segment the infarct core in apical slicelikewise,, peri-infarct region was less detected here.This image shows the false positive detection for no-reflow region. The last one is an extreme case as it does not follow our assumption, where there are more than one infarct regions,appearing only in that slice, unconnected to the infarct in neighbouring slices, so that this region was failed to be detected.
  • Nevertheless, let’s take a look at the good examples in HIA segmentation.These three images gave excellent result with appropriate proportion of HIA. Our method also works in the no-infarct case or healthy myocardium.two regions of infarct could be detected here,, Additionaly, the advantages of applying forward and backward 3D connectivity are illustrated by these two examples, where the correct infarct region can be segmented, since other bright regions are unconnected with the detected infarct in neighboring slicesLast image show the reliability of our method in detecting the infarct when there is small difference in signal intensity.
  • Finally, to sum up…..in GUI software and is capable to help the clinical work of phyiscian1…to achieve more reliable results2… by implementing the forward and reverse 3D connectivity analysis. 3… indeed,Some suggestions are…1… which need to be done by the hospital2… representation of infarct segmentation3… since MATLAB have some issues with limited display resolution
  • Finally, to sum up…..in GUI software and is capable to help the clinical work of phyiscian1…to achieve more reliable results2… by implementing the forward and reverse 3D connectivity analysis. 3… indeed,Some suggestions are…1… which need to be done by the hospital2… representation of infarct segmentation3… since MATLAB have some issues with limited display resolution
  • Transcript

    • 1. Supervised by: Alain Lalande, PhDGirona, 15 June 2011
    • 2. What is MI? Introduction What is DE-MRI? Problem definitionWhat is myocardial infarction (MI)?Heart attack, caused by coronary arthrosclerosisMyocardium: heart muscleInfarction: tissue death, due to lack of oxygenHeterogeneous infarct zones (HIA): Infarct core Peri-infarct Microvascular obstruction (no-reflow)
    • 3. What is MI? What is DE-MRI?Introduction Problem definitionDelay-enhancement MRI (DE-MRI)• Gold standard for MI viability• 10 – 15 minutes after contrast agent injection• Infarct area is shown as hyper-enhancement Acquisition of DE-MRI slices
    • 4. What is MI? What is DE-MRI?Introduction Problem definitionDelay-enhancement MRI (DE-MRI)• Gold standard for MI viability• 10 – 15 minutes after contrast agent injection• Infarct area is shown as hyper-enhancement Endocardium Acquisition of DE-MRI slices
    • 5. What is MI? What is DE-MRI?Introduction Problem definitionDelay-enhancement MRI (DE-MRI)• Gold standard for MI viability• 10 – 15 minutes after contrast agent injection Epicardium• Infarct area is shown as hyper-enhancement Endocardium Acquisition of DE-MRI slices
    • 6. What is MI? What is DE-MRI?Introduction Problem definitionDelay-enhancement MRI (DE-MRI)• Gold standard for MI viability• 10 – 15 minutes after contrast agent injection Epicardium• Infarct area is shown as hyper-enhancement Endocardium Infarct in DE-MRI Acquisition of DE-MRI slices
    • 7. What is MI? What is DE-MRI? Introduction Problem definition Problem Definition HIA is hard to be distinguished visually No automatic solution available
    • 8. What is MI? What is DE-MRI? Introduction Problem definition Problem Definition HIA is hard to be distinguished visually No automatic solution available Project goals ! Develop automatic segmentation and quantification methods, by taking into account HIA. ! Implement clinical software for automatic quantification of MI from DE-MR images
    • 9. Infarct segmentation HIA segmentation State of the Art Quantification & Representation • Infarct segmentation Diagram + picturesKim, et al (1999)Beek, et al (2005, 2009)Heiberg, et al (2005, 2008)
    • 10. Infarct segmentation HIA segmentation State of the Art Quantification & Representation • Infarct segmentation Diagram + picturesKim, et al (1999) Amado, et al (2004)Beek, et al (2005, 2009)Heiberg, et al (2005, 2008)
    • 11. Infarct segmentation HIA segmentation State of the Art Quantification & Representation • Infarct segmentation Diagram + picturesKim, et al (1999) Hsu, et al (2006) Amado, et al (2004)Beek, et al (2005, 2009)Heiberg, et al (2005, 2008)
    • 12. Infarct segmentation HIA segmentation State of the Art Quantification & Representation • Infarct segmentation Diagram + picturesKim, et al (1999) Hsu, et al (2006) Amado, et al (2004)Beek, et al (2005, 2009)Heiberg, et al (2005, 2008) Friman, et al (2008) Doublier, et al (2003) Metwally, et al (2010) Hennemuth, et al (2008) Elagoumi, et al (2010)
    • 13. Infarct segmentation HIA segmentation State of the Art Quantification & Representation • Infarct segmentation Diagram + picturesKim, et al (1999) Hsu, et al (2006) Amado, et al (2004)Beek, et al (2005, 2009)Heiberg, et al (2005, 2008) Friman, et al (2008) Doublier, et al (2003) Metwally, et al (2010) Hennemuth, et al (2008) Elagoumi, et al (2010)
    • 14. Infarct segmentation HIA segmentation State of the Art Quantification & Representation HIA SegmentationSimple intensity thresholding Microvascular obstruction (MO)Yan, et al (2006) – SD basedSchmidt, et al (2007) – FWHM based NO exact threshold definitionHundley, et al (2010) – SD based
    • 15. Infarct segmentation HIA segmentation State of the Art Quantification & Representation HIA Segmentation Infarct core Peri-InfarctSimple intensity thresholding Microvascular obstruction (MO)Yan, et al (2006) – SD basedSchmidt, et al (2007) – FWHM based NO exact threshold definitionHundley, et al (2010) – SD based
    • 16. Infarct segmentation HIA segmentation State of the Art Quantification & Representation HIA Segmentation No-reflow Infarct core Peri-InfarctSimple intensity thresholding Microvascular obstruction (MO)Yan, et al (2006) – SD basedSchmidt, et al (2007) – FWHM based NO exact threshold definitionHundley, et al (2010) – SD based
    • 17. Infarct segmentation HIA segmentation State of the Art Quantification & Representation Quantification Representation Bull’s eye plot inContiguous short-axis slices 17-segment model Slice thickness
    • 18. Infarct segmentation HIA segmentation State of the Art Quantification & Representation Quantification Representation Bull’s eye plot inContiguous short-axis slices 17-segment model basal mid-cavity apical Slice thickness apex
    • 19. Material Pre-processing Infarct segmentation Methodology Infarct core segmentation Peri-infarct segmentation No-reflow segmentation Quantification Representations MaterialPopulation study20 patientsAges: 53 +10 yearsAcute MI (<2 weeksafter heart attack)MRI Protocol3 T MR MagnetPSIR sequence
    • 20. Material Pre-processing Infarct segmentation Methodology Infarct core segmentation Peri-infarct segmentation No-reflow segmentation Quantification Representations Pre-processing Increase resolution Input: Original image 256x216+ Myocardial contours
    • 21. Material Pre-processing Infarct segmentation Methodology Infarct core segmentation Peri-infarct segmentation No-reflow segmentation Quantification Representations Pre-processing Increase Contrast resolution Enhancement Input: Original image 256x216+ Myocardial contours
    • 22. Material Pre-processing Infarct segmentation Methodology Infarct core segmentation Peri-infarct segmentation No-reflow segmentation Quantification Representations Pre-processing Increase Contrast resolution Enhancement Input: Original image 256x216+ Myocardial contours Unregistered 3D MRI stack Registered image slices, same center myocardium ROI location Rigid registration Motion compensation
    • 23. Material Pre-processing Infarct segmentation Methodology Infarct core segmentation Peri-infarct segmentation No-reflow segmentation Quantification Representations Pre-processing Increase Contrast resolution Enhancement Input: Original image 256x216+ Myocardial contours Unregistered 3D MRI stack Myocardium ROI Image filtering Registered image slices, same center myocardium ROI location Rigid registration Motion compensation
    • 24. Material Pre-processing Infarct segmentation Methodology Infarct core segmentation Peri-infarct segmentation No-reflow segmentation Quantification Representations Infarct segmentation Input: Gaussian mixture model (GMM) Pre-processed myocardium Gaussian parametersMixture of Gaussian distribution: Estimate θ by iterative M-step: expectation-maximization (EM) algorithm Gaussian distribution: E-step: Voxel intensity
    • 25. Material Pre-processing Infarct segmentation Methodology Infarct core segmentation Peri-infarct segmentation No-reflow segmentation Quantification Representations Infarct segmentation Input: Gaussian mixture model (GMM) Pre-processed myocardium Gaussian parametersMixture of Gaussian distribution: Estimate θ by iterative M-step: expectation-maximization (EM) algorithm Gaussian distribution: E-step: Voxel intensity
    • 26. Material Pre-processing Infarct segmentation Methodology Infarct core segmentation Peri-infarct segmentation No-reflow segmentation Quantification Representations Output: Infarct segmentation Hyper-enhanced (HE) region Input: Gaussian mixture model (GMM) Pre-processed myocardium Gaussian parametersMixture of Gaussian distribution: Estimate θ by iterative M-step: expectation-maximization (EM) algorithm Gaussian distribution: E-step: Voxel intensity
    • 27. Material Pre-processing Infarct segmentationMethodology Infarct core segmentation Peri-infarct segmentation No-reflow segmentation Quantification RepresentationsInfarct segmentationTwo-way 3D connectivity analysisFalse positive compensation  noisy acquisition, blood poolartifact, or partial volume effect (PVE)Detected infarct  continuous in 3D image stack
    • 28. Material Pre-processing Infarct segmentationMethodology Infarct core segmentation Peri-infarct segmentation No-reflow segmentation Quantification RepresentationsInfarct segmentationTwo-way 3D connectivity analysisFalse positive compensation  noisy acquisition, blood poolartifact, or partial volume effect (PVE)Detected infarct  continuous in 3D image stack Feature analysis • Minimum size • Sub-endocardial distance • Solidity
    • 29. Material Pre-processing Infarct segmentation Methodology Infarct core segmentation Peri-infarct segmentation No-reflow segmentation Quantification RepresentationsInfarct core segmentationFWHM ThresholdingFeature analysis Inclusion of no-reflow areaMorphological filling and closing with endocardium 3D connectivity analysis Minimum size
    • 30. Material Pre-processing Infarct segmentation Methodology Infarct core segmentation Peri-infarct segmentation No-reflow segmentation Quantification RepresentationsInfarct core segmentationFWHM ThresholdingFeature analysis Inclusion of no-reflow areaMorphological filling and closing with endocardium 3D connectivity analysis Minimum size
    • 31. Material Pre-processing Infarct segmentation Methodology Infarct core segmentation Peri-infarct segmentation No-reflow segmentation Quantification Representations Peri-infarct segmentation Spatial-weighted Fuzzy clustering Optimization of objective function O for the optimal cluster center cInput and degree of membership u: Cluster: K = 2  normal and peri-infarct Spatial-weighted fuzzy membership: Euclidean distance of myocardium pixels to the infarct core
    • 32. Material Pre-processing Infarct segmentation Methodology Infarct core segmentation Peri-infarct segmentation No-reflow segmentation Quantification Representations Peri-infarct segmentation Spatial-weighted Fuzzy clustering Optimization of objective function O for the optimal cluster center cInput and degree of membership u: Cluster: K = 2  normal and peri-infarct Spatial-weighted fuzzy membership: Spatial weight pik Euclidean distance of myocardium pixels to the infarct core
    • 33. Material Pre-processing Infarct segmentation Methodology Infarct core segmentation Peri-infarct segmentation No-reflow segmentation Quantification Representations Peri-infarct segmentation Spatial-weighted Fuzzy clustering Optimization of objective function O for the optimal cluster center cInput and degree of membership u: Cluster: K = 2  normal and peri-infarct Spatial-weighted fuzzy membership:Output Spatial weight pik Defuzzification Euclidean distance of myocardium pixels to the infarct core
    • 34. Material Pre-processing Infarct segmentation Methodology Infarct core segmentation Peri-infarct segmentation No-reflow segmentation Quantification RepresentationsNo-reflow segmentation 1 Dark region surrounded by infarct coreInput: Infarct core
    • 35. Material Pre-processing Infarct segmentation Methodology Infarct core segmentation Peri-infarct segmentation No-reflow segmentation Quantification RepresentationsNo-reflow segmentation 1 Dark region surrounded by infarct coreInput: Infarct core 2 Adjacent to endocardium
    • 36. Material Pre-processing Infarct segmentation Methodology Infarct core segmentation Peri-infarct segmentation No-reflow segmentation Quantification RepresentationsNo-reflow segmentation 1 Dark region surrounded by infarct coreInput: Infarct core 2 Adjacent to endocardium 3 Extent of MO ≠transmural Spatial constraint Dist myo : normalized relative distance of myocardium pixels to endocardium Limit for no-reflow region:
    • 37. Material Pre-processing Infarct segmentation Methodology Infarct core segmentation Peri-infarct segmentation No-reflow segmentation Quantification RepresentationsNo-reflow segmentation 1 Dark region surrounded by infarct coreInput: Infarct core 2 Adjacent to endocardium 3 Extent of MO ≠transmural Spatial constraint Dist myo : normalized relative distance of myocardium pixels to endocardium Limit for no-reflow region: Output
    • 38. Material Pre-processing Infarct segmentation Methodology Infarct core segmentation Peri-infarct segmentation No-reflow segmentation Quantification RepresentationsQuantification Area in
    • 39. Material Pre-processing Infarct segmentation Methodology Infarct core segmentation Peri-infarct segmentation No-reflow segmentation Quantification Representations Quantification Area inVolumetric quantification for the whole myocardiumwhere,i = the current slice from N image slices in MRI stack. Volume in Also, in % of myocardium
    • 40. Material Pre-processing Infarct segmentation Methodology Infarct core segmentation Peri-infarct segmentation No-reflow segmentation Quantification RepresentationsRepresentation QuantitativeQ Bull’s eye plot in 16-segment modelu Infarct Coreal Peri-i infarcttat No-i reflowve All
    • 41. GUI Implementation Evaluation of Infarct Size Quantification Results Evaluation of the Infarct Area Segmentation Visual Evaluation of HIA Segmentation Navigation Manual contouringMenu toolbar Status GUIImage display Patient Display information Analysis panel segmentation Display quantification
    • 42. GUI Implementation Evaluation of Infarct Size Quantification Results Evaluation of the Infarct Area Segmentation Visual Evaluation of HIA Segmentation Navigation Manual contouringMenu toolbar Status GUIImage display Patient Display information Analysis panel segmentation Display quantification
    • 43. GUI Implementation Evaluation of Infarct Size Quantification Results Evaluation of the Infarct Area Segmentation Visual Evaluation of HIA Segmentation Navigation Manual contouringMenu toolbar Status GUIImage display Patient Display information Analysis panel segmentation Display quantification
    • 44. GUI Implementation Evaluation of Infarct Size Quantification Results Evaluation of the Infarct Area Segmentation Visual Evaluation of HIA Segmentation Navigation Manual contouringMenu toolbar Status GUIImage display Patient Display information Analysis panel segmentation Display quantification
    • 45. GUI Implementation Evaluation of Infarct Size Quantification Results Evaluation of the Infarct Area Segmentation Visual Evaluation of HIA Segmentation Navigation Manual contouringMenu toolbar Status GUIImage display Patient Display information Analysis panel segmentation Display quantification
    • 46. GUI Implementation Evaluation of Infarct Size Quantification Results Evaluation of the Infarct Area Segmentation Visual Evaluation of HIA Segmentation Navigation Manual contouringMenu toolbar Status GUIImage display Patient Display information Analysis panel segmentation Display quantification
    • 47. GUI Implementation Evaluation of Infarct Size Quantification Results Evaluation of the Infarct Area Segmentation Visual Evaluation of HIA Segmentation Navigation Manual contouringMenu toolbar Status GUIImage display Patient Display information Analysis panel segmentation Display quantification
    • 48. GUI Implementation Evaluation of Infarct Size Quantification Results Evaluation of the Infarct Area Segmentation Visual Evaluation of HIA SegmentationEvaluation of Infarct Size QuantificationComparison of our automatic method & ground truth (manual total-infarct tracing from 2 observers) Vo l u m e mean of the differences = 2.78 cm3 SD of the differences = 3.27 cm3
    • 49. GUI Implementation Evaluation of Infarct Size QuantificationResults Evaluation of the Infarct Area Segmentation Visual Evaluation of HIA SegmentationEvaluation of Infarct Size Quantification Area mean of the differences = 0.45 cm2 SD of the differences = 0.75 cm2
    • 50. GUI Implementation Evaluation of Infarct Size Quantification Results Evaluation of the Infarct Area Segmentation Visual Evaluation of HIA SegmentationEvaluation of the Infarct SegmentationBy Kappa coefficient, between the automatic segmentation results and ground-truths(manual total-infarct tracing from 2 observers) Kappa statistics for infarct-segmentation comparison of automatic methods and observers agreement
    • 51. GUI Implementation Evaluation of Infarct Size QuantificationResults Evaluation of the Infarct Area Segmentation Visual Evaluation of HIA SegmentationVisual Evaluation of HIA SegmentationRating score for subjective-visual evaluation of HIA segmentationVisual evaluation result for HIA segmentationfrom 116 images from 20 patients
    • 52. GUI Implementation Evaluation of Infarct Size Quantification Results Evaluation of the Infarct Area Segmentation Visual Evaluation of HIA Segmentation Visual Evaluation of HIA Segmentation Issues in HIA segmentation (errors are indicated with white arrow)Error in the infarct Error in the peri-infact Error in the no-reflow Unconnected infarctcore segmentation segmentation segmentation regions
    • 53. GUI Implementation Evaluation of Infarct Size QuantificationResults Evaluation of the Infarct Area Segmentation Visual Evaluation of HIA SegmentationVisual Evaluation of HIA SegmentationExample of correct infarct segmentation results with our method Robustness of the infarct segmentation method Image with narrow range neighboring slices by using two-way 3D connectivity of signal intensity
    • 54. Conclusions1 A fully automatic system for myocardial infarction quantification has been implemented2 The automatic quantification and segmentation results had been evaluated and gave the best performance with fast computational timeImprovements made in this thesis work: Clustering method used rather than strict threshold determination Elimination of false positive cases were tackled The definitions for peri-infarct and no-reflow segmentation give promising result
    • 55. Conclusions1 A fully automatic system for myocardial infarction quantification has been implemented2 The automatic quantification and segmentation results had been evaluated and gave the best performance with fast computational timeImprovements made in this thesis work: Clustering method used rather than strict threshold determination Elimination of false positive cases were tackled The definitions for peri-infarct and no-reflow segmentation give promising result Future works Validation with histopathology Extend the representation in 3D model Implementation in C++
    • 56. Thank you! Gracias! Merci! Terima kasih!
    • 57. References[1] V.L.Roger, A.S.Go, D.M Jones, J.D.Berry, and R.J. Adams, Heart Disease and Stroke Statistics 2011 Update,"Circulation, vol. 123, pp.e18 - e 209, 2011.[2] W. Kevin Tsai, K.M. Holohan, and K.A. Williams, Myocardial Perfusion Imaging from Echocardiography to SPECT, PET, CT, and MRI|Recent Advances and Applications," US Cardiology, vol. 7, no.1, pp.12 - 6, 2010.[3] P. Hunold, T. Schosser, and J.Barkhausen. Magnetic resonance cardiac perfusion imaging{a clinical perspective,"Journal of European Radiology, vol. 16, pp. 1779{1788, 2006.[4] National Heart Lung and Blood Institute. What is a Heart Attack?," Heart and Vascular Diseases and Condition Index. http://www.nhlbi.nih.gov/health/dci/Diseases/HeartAttack.html, 2008.[5] Jay S. Detsky, Cardiac Tissue Characterization Following Myocardial Infarction using Magnetic Resonance Imaging," Thesis book from Graduate Departement of Medical Biophysics, University of Toronto, 2008.[6] Y. Mikami, H. Sakuma, M. Nagata, and M.Ishida, Relation Between Signal Intensity on T2-Weighted MR Images and Presence of Microvascular Obstruction in Patients with Acute Myocardial Infarction,"American Journal of Radiology, vol. 192, pp.321-326, 2009.[7] R.Ja e, T.Charron, and G.Puley, Microvascular Obstruction and the No-Reow Phenomenon after Percutaneous Coronary Intervention,"Circulation, vol. 117, pp. 3152 - 3156, 2008.[8] Hundley, et al, Expert Consensus on Cardiovascular Magnetic Resonance," Circulation, vol.121, pp. 2462 - 2508, 2010.[9] E.C.Lin, Cardiac MRI - Technical Aspects Primer," eMedicine Clinical Reference, http://emedicine.medscape.com, December 2008.
    • 58. References[10] G.S.Slavin, S.D. Wol , S.N.Gupta, and T.K.Foo, First-Pass Myocardial Perfusion MR Imagingwith Interleaved Notched Saturation: Feasibility Study," Radiology, vol.219, no.1, pp. 259 { 263, 2001.[11] P.Hunold, T. Schlosser, and F.M.Vogt, Myocardial Late Enhancement in ContrastEnhanced Cardiac MRI: Distinction Between Infarction Scar and Non{Infarction-Related Disease," American Journal of Radiology, vol.184, pp. 1420 - 1426, 2005.[12] Kim RJ, Albert TS, Wible JH, Elliott MD, Performance of delayed-enhancement magnetic resonance imaging with gadoversetamide contrast for the detection and assessment of myocardial infarction,"Circulation, vol.117, pp.629-637, 2008.[13] H.Abdel-Aty and C.Tillmanns, The Use of Cardiovascular Magnetic Resonance in Acute Myocardial Infarction,"Springer: Current Cardiology Journal, vol.12, pp.76 - 81, 2001.[14] W. G. Hundley, D.A.Bluemke, J.P.Finn, S.D.Flamm, et al,2010 Expert Consensus Document on Cardiovascular Magnetic Resonance: A Report of the American College of Cardiology Foundation Task Force on Expert Consensus Documents," Journal of the American College of Cardiology, vol.55, no.23, pp.2614-2664, 2010.[15] R.J. Kim, D.S. Fieno, T.B. Parrish,Relationship of MRI Delayed Contrast Enhancement to Irreversible Injury, Infarct Age, and Contractile Function," Circulation, vol.100, pp. 1992 - 2002, 1999.[16] A.M. Beek, O. Bondarenko,Quanti cation of Late Gadolonium Enhanced CMR in Viability Assessment in Chronic Ischemic Heart Disease: A Comparison to Functional Outcome," Journal of Cardiovascular Magnetic Resonance, vol.11, pp.1-7, 2009.[17] A.M. Beek, O. Bondarenko,Standardizing the De nition of Hyperenhancement in Quantitative Assessment of Infarct Size and Myocardial Viability using Delayed Contrastenhanced CMR," Journal of Cardiovascular Magnetic Resonance, vol.7, pp. 481 - 485, 2005.[18] L.C. Amado, B.L. Gerber, and S.N. Gupta, Accurate and objective infarct sizing by contrast-enhanced magnetic resonance imaging in a canine myocardial infarction model," Journal of American College of Cardiology, vol.44, pp. 2383{2389, 2004.[19] Li-Yueh Hsu, A. Natanzon, P. Kellman and G.A. Hirsch,Quantitative Myocardial Infarction on Delayed Enhancement MRI. Part I: Animal Validation of an Automated Feature Analysis and Combined Thresholding Infarct Sizing Algorithm,"Journal of Magnetic Resonance Imaging, vol. 23, pp. 298-308, 2006.[20] Li-Yueh Hsu, A. Natanzon, P. Kellman and G.A. Hirsch,Quantitative Myocardial Infarction on Delayed Enhancement MRI. Part II: Clinical Application of an Automated Feature Analysis and Combined Thresholding Infarct Sizing Algorithm,"Journal of Magnetic Resonance Imaging, vol. 23, pp. 309-314, 2006.[21] C.Doublier, M.Couprie, J.Garot, and Y. Hamam,Computer Assited Segmentation, Quanti cation and Visualtization of an Infarcted Myocardium from MRI Images," Proceedings Biomedsim, 2003.[22] O.Friman, A.Hennemuth, and H.O.Peitgmen,A Rician-Gaussian Mixture Model for Segmenting Delayed Enhancement MRI Images,"Proceeding of International Society for Magnetic Resonance in Medicine, vol.16, 2008.[23] K.Elagouni, Ciofolo-Veit, C.Mory, Philips Med. System, Automatic segmentation of pathological tissues in cardiac MRI,"Biomedial Imaging: IEEE International Symposium, pp. 472 - 475, 2010.[24] A.K. Attili, A.Schuster, and E.Nagel, Quanti cation in cardiac MRI: advances in image acquisition and processing,"International Journal of Cardiovascular Imaging, vol. 26, pp. 27 -40, 2010.[25] M. J. Herold, Quanti cation of myocardial perfusion by cardiovascular magnetic resonance,"Journal of Cardiovascular Magnetic Resonance, vol.12, pp.1-16, 2010.
    • 59. References[26] R.M.Setse, D.G.Bexell, T.P.O’Donnel and A.R. Stillman,Quantitative Assessment of Myocardial Scar in Delayed Enhancement Magnetic Resonance Imaging,"Journal of Magnetic Resonance Imaging, vol.18, pp.434-441, 2003.[27] L. Rosendhl, P. Blomstrand, E. Heiberg, and J. Ohlsoon, Computer-assisted Calculation of Myocardial Infarct Size Shortens the Evaluation Time of Contrast-enhanced Cardiac MRI,"Clinical Physiology and Functional Imaging, vol. 28, no.1, pp.1-7, 2008.[28] E. Heiberg, H. Engblom, J. Engvall, E. Hedstrom, M. Ugander, and H. Arheden, Semiautomatic quanti cation of myocardial infarction from delayed contrast enhanced magnetic resonance imaging,"Scandinavian Cardiovascular Journal, vol. 39, no.5, pp. 267- 275, 2005.[29] E. Heiberg, M. Ugander, H. Engblom, M. G otberg, G. K. Olivecrona, D. Erlinge, and H. Arheden,Automated quanti cation of myocardial infarction from MR images by accounting for partial volume e ects: animal, phantom, and human study," Radiology, vol. 246, no. 2, pp. 581-558, 2008.[30] Andrew.E. Arai, Myocardial Infarction and Viability with an Emphasis on Imaging Delayed Enhancement," Contemporary Cardiology: Cardiovascular Magnetic Resonance Imaging, Humana Press, pp.351 - 353, 2008.[31] B. Sievers, M.D. Elliott, L.M. Hurwitz, Rapid Detection of Myocardial Infarction by Subsecond, Free-Breathing Delayed Contrast- Enhancement Cardiovascular Magnetic Resonance,"Circulation, vol.115, pp.236 - 244, 2007.[32] A. Hennemuth, A.Seeger, O.Firman, S.Miller, B.Klumpp, S.Oeltze, and H.O.Peitgnen,A Comprehensive Approach to the Analysis of Contrast Enhanced Cardiac MR Images," IEEE Transaction of Medical Imaging, vol. 27, No. 11, pp.1592 - 1601, 2008.[33] A.Hennemuth, A.Seeger, O.Friman, S.Miller, and H.O.Peitgen,Automatic Detection and Quanti cation of Non-Viable Myocardium in Late Enhancement Images," Proceeding of International Society for Magnetic Resonance in Medicine, vol.16, pp.1039, 2008.[34] M.K.Metwally, N. El-Gayar, and N.F.Osma,Improved Technique to Detect the Infarction in Delayed Enhancement Image Using K- Mean Method," Image Analysis and Recognition International Conference Proceeding, pp. 108 - 119, 2010.[35] S. Roy, A. Carass, P. Bazzin, and J.L Prince, "A Rician Mixture Model Classi cation Algorithm for Magnetic Resonance Images,"Proceeding of IEEE International Symposium on Biomedical Imaging, 2009.[36] N. Kachenoura, A. Redheuil, A. Herment, E. Mousseaux, F. Frouin, Robust assessment of the transmural extent of myocardial infarction in late gadolinium-enhanced MRI studies using appropriate angular and circumferential subdivision of the myocardium," European Radiology, vol.18, pp.2140 - 2147, 2008.[37] J.C.Rubenstein, J.T.Ortiz and E.Wu, The Use of Peri-infarct Contrast-enhanced Cardiac Magnetic Resonance Imaging for the Prediction of Late Post-myocardial Infarction Ventricular Dysfunction,"American Heart Journal, vo. 156, no.3, pp.498 - 505, 2008.[38] M. Saeed, G. Lund, and M.F.Wenland,Magnetic Resonance Characterization of the PeriInfarction Zone of Reperfused Myocardial Infarction With Necrosis-Speci c and Extracellular Nonspeci c Contrast Media,"Circulation, vol.103, pp. 871 - 876, 2001.[39] H. Engblom, E. Hedstrom, and E. Heiberg, Rapid Initial Reduction of Hyperenhanced Myocardium after Reperfused First Myocardial Infarction Suggest Recovery of the PeriInfarction Zone: One-Year Follow-Up by MRI,"Circulation, vol.2, pp.47-55, 2009.
    • 60. References[40] J. Bogaert, M. Kalantzi, and F.E. Rademakers,Determinants and Impact of Microvascular Obstruction in Successfully Reperfused ST-segment Elevation Myocardial Infarction: Assessment by Magnetic Resonance Imaging."Journal of European Radiology, vol. 17, pp. 2572 - 2580, 2007.[41] A.T. Yan, A. J. Shayne, K.A.Brown, and S.N.Gupta,Characterization of the Peri-Infarct Zone by Contrast-Enhanced Cardiac Magnetic Resonance Imaging Is a Powerful Predictor of Post{Myocardial Infarction Mortality,"Circulation, vol. 114, pp. 32-39, 2006.[42] A.Schmidt, C.F Azevedo, A. Cheng, and S.N. Supta, Infarct Tissue Heterogeneity by Magnetic Resonance Imaging Identi es Enhanced Cardiac Arrhythmia Susceptibility in Patients With Left Ventricular Dysfunction,"Circulation, vol.115, pp. 2006-2014, 2007.[43] S. Heidary, H. Patel, J. Chung, H. Yokota,Quantitative Tissue Characterization of Infarct Core and Border Zone in Patients with Ischemic Cardiomyopathy by Magnetic Resonance is Associated with Future Cardiovascular Events," Journal of the American College of Cardiology, vol.55, no.24, 2010.[44] A.Stork, G.K. Lunc, and K. Muellerleille, Characterization of the peri-infarction zone using T2-weighted MRI and delayed-enhancement MRI in patients with acute myocardial infarction,"Journal of European Radiology, vol.16, pp. 2350 - 2357, 2006.[45] R.Nijveldt, A.M. Beek, and A.Hirsch,No-reow After Acute Myocardial Infarction: Direct Visualisation of Microvascular Obstruction by Gadolinium- enhanced CMR," Netherlands Heart Journal, vol. 16, no.5, pp.179-181, 2008.[46] S.M.Bekkers, W.H.Backes, R.J.Kim, and G.Snoep, Detection and characteristics of microvascular obstruction in reperfused acute myocardial infarction using an optimized protocol for contrast-enhanced cardiovascular magnetic resonance imaging,"Journal of European Radiology, vol.19, pp. 2904 - 2912, 2009.[47] C.B. Ducci, F. Siong, K. Symmonds,The Complex Pathophysiology of Acute Myocardial Infarction Imaged by Cardiovascular magnetic Resonance: Infarction, Edema, Microvascular Obstruction, and Inducible Ischemia,"Circulation, vol. 118, pp. 89 - 92, 2008.[48] G. L. Ra , W.W.O’Neil, and R.E. Gentry, Microvascular Obstruction and Myocardial Function after Acute Myocardial Infarction: Assessment by Using Contrast -enhanced Cine MR Imaging,"Radiology, vol. 240, no.2, 529 - 536, 2006.[49] R. Nijveldt, M.B. Hofman, and A. Hirsch, Assessment of Microvascular Obstruction and Prediction of Short-term Remodeling after Acute Myocardial Infarction: Cardiac MR Imaging Study,"Radiology, vol. 250, no.2, 363- 370, 2009.[50] N.G.Bellenger, M.I.Burgess, and S.G.Ray, Comparison of left ventricular ejection fraction and volumes in heart failure by echocardiography, radionuclide ventriculography and cardiovascular magnetic resonance: Are they interchangeable?," European Heart Journal, vol.21, pp.1387 - 1396, 2000.[51] .H. Thiele, I. Paetsch, and B. Schnackenburg,Improved Accuracy of Quantitative Assessment of Left Ventricular Volume and Ejection Fraction by Geometric Models with SteadyState Free Precession,"Journal of Cardiovascular Magnetic Resonance, vol.4, pp.327 - 339, 2002.[52] Anil K.Attili, A. schuster, E.Nagel, Quanti cation in cardiac MRI: advances in image acquisition and processing,"International Journal of Cardiovascular Imaging, vol.26, pp.27 - 40, 2010.
    • 61. References[53] Kelly.M.Choi,R.J.Kim, G.Guberniko .Transmural Extent of Acute Myocardial Infarction Predicts Long-Term Improvement in Contractile Function," Circulation, vol.104, pp.1101 - 1107, 2011.[54] M.D.Cerqueira, N.J.Weissman, V. Dilsizian, and A.K.Jacobs, Standardized Myocardial Segmentation and Nomenclature for Tomographic Imaging of the Heart: A Statemenet for Healthcare Professionals from the Cardiac Imaging Comitted of the Council on Clinical Cardiology of the American Heart Association,"Circulation, vol.115, pp.539-542, 2002.[55] E. Heiberg, H. Engblom, M. Ugander, and H. Arheden. Automated Calculation of Infarct Transmurality.IEEE Computers in Cardiology,"vol.34, pp.165-168, 2007.[56] Rafael C. Gonzales, Richard E. Woods,Digital Image Processing, second edition,"Prentice Hall, 2002.[57] R.J.Kim, D.J. Shah, and R.M.Judd, How We Perform Delayed Enhancement Imaging," Journal of Cardiovascular Magnetic Resonance, vol.5, no.3, pp. 505 - 514, 2003.[58] O. Demirkaya, M.H. Asyali, and P.K.Sahoo, Image Processing with MATLAB: Application in Medicine and Biology,"Taylor & Francis Group, New York, 2009.[59] S. Lakare, 3D Segmentation Techniques for Medical Volumes," State University of New York: Research Prociency Exam, 2000.[60] Zhi-Kai Huang and De-Hui Lui,Unsupervised Image Segmentation Using EM Algorithm by Histogram," Proceedings of the Intelligent Computing, 3rd International Conference on Advanced Intelligent Computing Theories and Applications, 2007.[61] Zhi-Kai Huang, Kwok-Wing Chau,A New Image Thresholding Method Based on Gaussian Mixture Model,"Applied Mathematics and Vomputation, vol.205, No.2, pp. 899 - 907, 2008.[62] Y. Yang, C. Zheng, and P.Lin, Fuzzy Clustering with Spatial Constraints for Image Thresholding,"Optica Applicata, vol. XXXV, no.4, 2005.[63] S.Z.Beevi and M.M. Sathik. An E ective Approach for Segmentation of MRI Images: Combining Spatial Information with Fuzzy C-Means Clustering,"European Journal of Scienti c Research, vol.41, no. 3, pp. 437 - 451, 2010.[64] M. Dang and G. Govaert,Spatial Fuzzy Clustering using EM and Markov Random Fields," Systems Research and Information Systems, vol. 8, pp. 183 - 202, 1998.[65] J.M.Bland and D.G.Altman, Statistical Method for Assessing Agreement between Two Methods of Clinical Measurement,"The Lancet, vol. 1, pp. 307{ 310, 1986.[66] L. Ramus and G. Malandain, Using Consensus Measures for Atlas Construction," ISBI INRIA Sophia Antipolis, 2009.[67] A. Pednekar, IA. Kakadiaris, U.Kurkure, R. Mutupillai, and S.Flamm, Intensity and Morphology-Based Energy Minimization for the Automatic Segmentation of the Myocardium, "Proceeding of International Conference on Computer Vision, no. 23, 2003.[68] Qi Wang and Zengfu Wang, A Subjective Method for Image Segmentation Evaluation," ACCV Springer - Verlag Berlin Heidelberg, pp. 53 - 64, 2010.
    • 62. Infarct Segmentation Comparison 2 SD FWHM Combined threshold- Feature Analysis Proposed method Ground truth 1 Ground truth 2
    • 63. HIA Segmentation ComparisonYan et al. Schmidt et al. Hundely et al. Proposed (2006) (2007) (2010) method
    • 64. Volume Evaluation - Regression
    • 65. Volume Evaluation – Bland Altman 2 SD FWHMFACT Proposed Method
    • 66. Area Evaluation - Regression
    • 67. Area Evaluation – Bland Altman 2 SD FWHMFACT Proposed Method
    • 68. Threshold management
    • 69. HIA Segmentation
    • 70. Bull’s Eye Calculation Weight  according to the location of the overlapped slice
    • 71. Volumetric Quantification
    • 72. Infarct segmentationCharacteristics:• The distribution of myocardium SI according to Gaussian• Infarct always starts from endocardial• Infarct regions are compact-shape of certain size A = area of the region H = the convex hull area of the polygon approximating the region shape
    • 73. MRF Segmentation