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 r...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.展开更多
基金supported in part by a Jiangsu Province Science and Technology Agency grant ( BE2016785)
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.
