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| 1 | Current Concepts of Integrated Coronary Physiology in the Catheterization Laboratory显示文摘 | Morton J. Kern Habib Samady | 2010 | Journal of the American College of Cardiology2010，，3： | 3 |
| 2 | Myocardial Bridging: Contemporary Understanding of Pathophysiology with Implications for Diagnostic and Therapeutic Strategies显示文摘 | Michel T. Corban Olivia Y. Hung Parham Eshtehardi Emad Rasoul-Arzrumly Michael McDaniel Girum Mekonnen Lucas H. Timmins Jerre Lutz Robert A. Guyton Habib Samady | 2014 | Journal of the American College of Cardiology2014，，： | 2 |
| 3 | Association of Coronary Wall Shear Stress With Atherosclerotic Plaque Burden, Composition, and Distribution in Patients With Coronary Artery Disease显示文摘 | Parham Eshtehardi Michael C. McDaniel Jin Suo Saurabh S. Dhawan Lucas H. Timmins José Nilo G. Binongo Lucas J. Golub Michel T. Corban Aloke V. Finn John N. Oshinski Arshed A. Quyyumi Don P. Giddens Habib Samady | 2012 | Journal of the American Heart Association2012，，4： | 1 |
| 4 | Current Concepts of Integrated Coronary Physiology in the Catheterization Laboratory显示文摘 | Morton J. Kern Habib Samady | 2010 | Journal of the American College of Cardiology2010，，3： | 1 |
| 5 | Fractional flow reserve: critical review of an important physiologic adjunct to angiography显示文摘 | Andrew H. Bishop Habib Samady | 2004 | American Heart Journal2004，，5： | 1 |
| 6 | Incremental value of combined perfusion and function over perfusion alone by gated SPECT myocardial perfusion imaging for detection of severe three-vessel coronary artery disease显示文摘 | Ronaldo S.L Lima Denny D Watson Allen R Goode Mir S Siadaty Michael Ragosta George A Beller Habib Samady | 2003 | Journal of the American College of Cardiology2003，，1： | 1 |
| 7 | Comparison between visual assessment and quantitative angiography versus fractional flow reserve for native coronary narrowings of moderate severity显示文摘 | Joshua J Fischer Habib Samady John A McPherson Ian J Sarembock Eric R Powers Lawrence W Gimple Michael Ragosta | 2002 | The American Journal of Cardiology2002，，3： | 1 |
| 8 | Automatic Segmentation for Intracoronary OCT Image Based on Convolutional Neural Network and Support Vector Machine Methods显示文摘Background Cardiovascular diseases are closely associated with atherosclerotic plaque development and rupture.Traditional medical imaging techniques such as magnetic resonance imaging(MRI)and intravascular ultrasound(IVUS)were unable to identify vulnerable plaques due to their limited resolution.Fortunately,optical coherence tomography(OCT)is an advanced intravascular imaging technique developed in recent years which has high resolution approximately 10 microns and could provide more accurate morphology of coronary plaque.In particular,it has the ability to identify plaques with fibrous cap thickness<65μm,an accepted threshold value for vulnerable plaques.However,segmentation of OCT images in clinic is still mainly performed manually by physicians which is time consuming and subjective.To overcome time consumption,several methodologies have been proposed for automatic segmentation of OCT images but most of these methods were still limited by intricate image preprocessing and expensive computation.In this research,two automatic segmentation methods for intracoronary OCT image based on support vector machine(SVM)and convolutional neural network(CNN)were performed to identify the plaque region and characterize plaque components.Methods In vivo IVUS and OCT coronary plaque data from 5 patients were acquired at Emory University with patient’s consent obtained.OCT were obtained from ILUMIEN OPTIS System(St.Jude,Minnesota,MN).The OCT catheter was traversed to the segment of interest and the catheter pullback was limited at a rate of 20 mm/sec.Following the OCT image acquisition,the IVUS catheter was traversed distally though the artery to the same coronary segment(Volcano Therapeutics,Rancho Cordova)and the catheter pullback speed was at a standard rate of 0.5 mm/sec.Seventy-seven matched IVUS and OCT slices with good image quality and lipid cores were selected for our segmentation study.Manual OCT segmentation was performed by experts and used as gold standard in the automatic segmentations.VH-IVUS was used as references and guide by the experts in the manual segmentation process.Three plaque component tissue classes were identified from OCT images in this work:lipid tissue(LT),fibrous tissue(FT)and background(BG).Procedures using two machine learning methods(CNN and SVM)were developed to segment OCT images,respectively.For CNN method,the U-Net architecture was selected due to its good performance in very different biomedical segmentation and very few annotated images.For SVM method,local binary patterns(LBPs),gray level co-occurrence matrices(GLCMs)which contains contrast,correlation,energy and homogeneity,entropy and mean value were calculated as features and assembled to feed SVM classifier.The accuracies of two segmentation methods were evaluated and compared using the OCT dataset.Segmentation accuracy is defined as the ratio of the number of pixels correctly classified over the total number of pixels.Results The overall classification accuracy based CNN method reached 95.8%,and the accuracies for LT,FT and BG were 86.8%,83.4%,and 98.2%,respectively.The overall classification accuracy based SVM was 71.9%,and per-class accuracy for LT,FT and BG was 75.4%,78.3%,and67.0%,respectively.Conclusions The two methods proposed can automatically identify plaque region and characterize plaque compositions for OCT images and potentially reduce the time spent by doctors in segmenting and evaluating coronary plaque OCT images.CNN provided better segmentation accuracies compared to those achieved by SVM. | Caining Zhang Huaguang Li Xiaoya Guo David Molony Xiaopeng Guo Habib Samady Don PGiddens Lambros Athanasiou Rencan Nie Jinde Cao Dalin Tang | 2019 | 医用生物力学2019，34，A01： | 0 |
| 9 | Machine Learning Model Comparison for Automatic Segmentation of Intracoronary Optical Coherence Tomography and Plaque Cap Thickness Quantification显示文摘Optical coherence tomography(OCT)is a new intravascular imaging technique with high resolution and could provide accurate morphological information for plaques in coronary arteries.However,its segmentation is still commonly performed manually by experts which is time-consuming.The aim of this study was to develop automatic techniques to characterize plaque components and quantify plaque cap thickness using 3 machine learning methods including convolutional neural network(CNN)with U-Net architecture,CNN with Fully convolutional DenseNet(FC-DenseNet)architecture and support vector machine(SVM).In vivo OCT and intravascular ultrasound(IVUS)images were acquired from two patients at Emory University with informed consent obtained.Eighteen OCT image slices which included lipid core and with acceptable image quality were selected for our study.Manual segmentation from imaging experts was used as the gold standard for model training and validation.Since OCT has limited penetration,virtual histology IVUS was combined with OCT data to improve reliability.A 3-fold cross-validation method was used for model training and validation.The overall tissue classification accuracy for the 18 slices studied(total classification database sample size was 8580096 pixels)was 96.36%and 92.72%for U-Net and FC-DenseNet,respectively.The best average prediction accuracy for lipid was 91.29%based on SVM,compared to 82.84%and 78.91%from U-Net and FC-DenseNet,respectively.The overall average accuracy(Acc)differentiating lipid and fibrous tissue were 95.58%,92.33%and 81.84%for U-Net,FC-DenseNet and SVM,respectively.The average errors of U-Net,FC-DenseNet and SVM from the 18 slices for cap thickness quantification were 8.83%,10.71%and 15.85%.The average relative errors of minimum cap thickness from 18 slices of U-Net,FC-DenseNet and SVM were 17.46%,13.06%and 22.20%,respectively.To conclude,CNN-based segmentation methods can better characterize plaque compositions and quantify plaque cap thickness on OCT images and are more likely to be used in the clinical arena.Large-scale studies are needed to further develop the methods and validate our findings. | Caining Zhang Xiaopeng Guo Xiaoya Guo David Molony Huaguang Li Habib Samady Don PGiddens Lambros Athanasiou Dalin Tang Rencan Nie Jinde Cao | 2020 | Computer Modeling in Engineering & Sciences2020，，5： | 0 |
| 10 | Convolution Neural Networks and Support Vector Machines for Automatic Segmentation of Intracoronary Optical Coherence Tomography显示文摘Cardiovascular diseases are closely associated with deteriorating atherosclerotic plaques.Optical coherence tomography(OCT)is a recently developed intravascular imaging technique with high resolution approximately 10 microns and could provide accurate quantification of coronary plaque morphology.However,tissue segmentation of OCT images in clinic is still mainly performed manually by physicians which is time consuming and subjective.To overcome these limitations,two automatic segmentation methods for intracoronary OCT image based on support vector machine(SVM)and convolutional neural network(CNN)were performed to identify the plaque region and characterize plaque components.In vivo IVUS and OCT coronary plaque data from 5 patients were acquired at Emory University with patient’s consent obtained.Seventy-seven matched IVUS and OCT slices with good image quality and lipid cores were selected for this study.Manual OCT segmentation was performed by experts using virtual histology IVUS as guidance,and used as gold standard in the automatic segmentations.The overall classification accuracy based on CNN method achieved 95.8%,and the accuracy based on SVM was 71.9%.The CNN-based segmentation method can better characterize plaque compositions on OCT images and greatly reduce the time spent by doctors in segmenting and identifying plaques. | Caining Zhang Huaguang Li Xiaoya Guo David Molony Xiaopeng Guo Habib Samady Don P.Giddens Lambros Athanasiou Rencan Nie Jinde Cao Dalin Tang | 2019 | Molecular & Cellular Biomechanics2019，16，2： | 0 |
| 11 | Predicting Coronary Plaque Morphology Changes Based on Multimodality FSI Models Using Follow-Up IVUS and OCT Data显示文摘Background Current bottleneck of patient-specific coronary plaque model construction is the resolution of in vivo medical imaging.The threshold of cap thickness of vulnerable coronary plaques is 65 microns,while the resolution of in vivo coronary intravascular ultrasound(IVUS)images is 150-200 microns,which is not enough to identify vulnerable plaques with thin caps and construct accurate biomechanical plaque models.Optical coherence tomography(OCT)with a 15-20μm resolution has the capacity to identify thin fibrous cap.IVUS and OCT images could complement each other and provide for more accurate plaque morphology,especially,fibrous cap thickness measurements.A modeling approach combining IVUS and OCT was introduced in our previous publication for cap thickness quantification and more accurate cap stress/strain calculations.In this paper,patient baseline and follow-up IVUS and OCT data were acquired and multimodality image-based Fluidstructure interaction(FSI)models combining 3D IVUS,OCT,angiography were constructed to better quantify human coronary atherosclerotic plaque morphology and plaque stress/strain conditions and investigate the relationship of plaque vulnerability and morphological and mechanical factors.Methods Baseline and 10-Month follow-up in vivo IVUS and OCT coronary plaque data were acquired from one patient with informed consent obtained.Co-registration and segmentation of baseline and follow-up IVUS and OCT images were performed for modeling use.Baseline and follow-up 3D FSI models based on IVUS and OCT were constructed to simulate the mechanical factors which integrating plaque morphology were employed to predict plaque vulnerability.These 3D models were solved by ADINA(ADINA R&D,Watertown,MA,USA).The quantitative indices of cap thickness,lipid percentage were classified according to histological literatures and denoted as Cap Index and Lipid Index.Cap Index,Lipid Index and Morphological Plaque Vulnerability Index(MPVI)were chosen to quantify plaque vulnerability,respectively.Random forest(RF)which was based 13 extracted features including morphological and mechanical factors was used for plaque vulnerability classification and prediction.Over sampling scheme and a 5-fold crossvalidation procedure was employed in all 45 slices for training and testing sets.Single and all different combinations of morphological and mechanical risk factors were used for plaque progression prediction.Results When Cap Index was used as the measurement,minimum cap thickness(MCT)was the best single predictor which area under curve(AUC)is 0.782 0;the combination of MCT,critical plaque wall strain(CPWSn),critical wall shear stress(CWSS)and cap wall shear stress(CapWSS)was the best predictor with ACU=0.868 6.When Lipid Index was used as the measurement,the lipid percentage(LP)was the best single predictor which AUC value is 0.857 8;the combination of Mean cap thickness(MeanCT),LP,CWSS and cap plaque wall stress(CapPWS)and was the best predictor with ACU=0.9821.When MPVI was used as the measurement,MCT was the best single predictor which AUC value is 0.782 9;the combination of MCT,LP,plaque area(PA),CPWSn and CapWSS was the best predictor with ACU=0.872 9.Conclusions Combinations of morphological and mechanical risk factors had higher prediction accuracy,compared to the prediction of single factors and other combination of morphological factors. | Xiaoya Guo Don PGiddens David Molony Habib Samady Chun Yang Jie Zheng Mitsuaki Matsumura Gary SMintz Akiko Maehara Liang Wang Dalin Tang | 2019 | 医用生物力学2019，34，A01： | 0 |
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