Zhifan Gao

Automated Framework for Detecting Lumen and Media-Adventitia Borders in Intravascular Ultrasound Images.

An automated framework for detecting lumen and media-adventitia borders in intravascular ultrasound images was developed on the basis of an adaptive region-growing method and an unsupervised clustering method. To demonstrate the capability of the framework, linear regression, Bland-Altman analysis and distance analysis were used to quantitatively investigate the correlation, agreement and spatial distance, respectively, between our detected borders and manually traced borders in 337 intravascular ultrasound images in vivo acquired from six patients. The results of these investigations revealed good correlation (r = 0.99), good agreement (>96.82% of results within the 95% confidence interval) and small average distance errors (lumen border: 0.08 mm, media-adventitia border: 0.10 mm) between the borders generated by the automated framework and the manual tracing method. The proposed framework was found to be effective in detecting lumen and media-adventitia borders in intravascular ultrasound images, indicating its potential for use in routine studies of vascular disease.

Robust estimation of carotid artery wall motion using the elasticity-based state-space approach

HighlightsWe propose an approach to track the motion of the carotid artery wall in the ultrasound images.We propose an evaluation function to measure the tracking error.Results on 280 sequences from 140 subjects show good performance compared with manual tracing. Graphical abstract Figure. No Caption available. Abstract The dynamics of the carotid artery wall has been recognized as a valuable indicator to evaluate the status of atherosclerotic disease in the preclinical stage. However, it is still a challenge to accurately measure this dynamics from ultrasound images. This paper aims at developing an elasticity‐based state‐space approach for accurately measuring the two‐dimensional motion of the carotid artery wall from the ultrasound imaging sequences. In our approach, we have employed a linear elasticity model of the carotid artery wall, and converted it into the state space equation. Then, the two‐dimensional motion of carotid artery wall is computed by solving this state‐space approach using the H∞ filter and the block matching method. In addition, a parameter training strategy is proposed in this study for dealing with the parameter initialization problem. In our experiment, we have also developed an evaluation function to measure the tracking accuracy of the motion of the carotid artery wall by considering the influence of the sizes of the two blocks (acquired by our approach and the manual tracing) containing the same carotid wall tissue and their overlapping degree. Then, we have compared the performance of our approach with the manual traced results drawn by three medical physicians on 37 healthy subjects and 103 unhealthy subjects. The results have showed that our approach was highly correlated (Pearson’s correlation coefficient equals 0.9897 for the radial motion and 0.9536 for the longitudinal motion), and agreed well (width the 95% confidence interval is 89.62 &mgr;m for the radial motion and 387.26 &mgr;m for the longitudinal motion) with the manual tracing method. We also compared our approach to the three kinds of previous methods, including conventional block matching methods, Kalman‐based block matching methods and the optical flow. Altogether, we have been able to successfully demonstrate the efficacy of our elasticity‐model based state‐space approach (EBS) for more accurate tracking of the 2‐dimensional motion of the carotid artery wall, towards more effective assessment of the status of atherosclerotic disease in the preclinical stage.

An artificial neural network method for lumen and media-adventitia border detection in IVUS

Intravascular ultrasound (IVUS) has been well recognized as one powerful imaging technique to evaluate the stenosis inside the coronary arteries. The detection of lumen border and media-adventitia (MA) border in IVUS images is the key procedure to determine the plaque burden inside the coronary arteries, but this detection could be burdensome to the doctor because of large volume of the IVUS images. In this paper, we use the artificial neural network (ANN) method as the feature learning algorithm for the detection of the lumen and MA borders in IVUS images. Two types of imaging information including spatial, neighboring features were used as the input data to the ANN method, and then the different vascular layers were distinguished accordingly through two sparse auto-encoders and one softmax classifier. Another ANN was used to optimize the result of the first network. In the end, the active contour model was applied to smooth the lumen and MA borders detected by the ANN method. The performance of our approach was compared with the manual drawing method performed by two IVUS experts on 461 IVUS images from four subjects. Results showed that our approach had a high correlation and good agreement with the manual drawing results. The detection error of the ANN method close to the error between two groups of manual drawing result. All these results indicated that our proposed approach could efficiently and accurately handle the detection of lumen and MA borders in the IVUS images.

Beat-to-Beat Blood Pressure and Two-dimensional (axial and radial) Motion of the Carotid Artery Wall: Physiological Evaluation of Arterial Stiffness

The physiological relationship between local arterial displacement and blood pressure (BP) plays an integral role in assess- ment of the mechanical properties of arteries. In this study, we used more advanced methods to obtain reliable continuous BP and the displacement of the common carotid artery (CCA) simultaneously. We propose a novel evaluation method for arterial stiffness that relies on determining the physiological relationship between the axial and radial displacements of the CCA wall and beat-to-beat BP. Patients (total of 138) were divided into groups according to the following three criteria: essential hyper- tension (EH) and normotension, male and female, elderly and younger. The Pearson correlation test and canonical correlation analysis showed that the CCA indices were significantly correlated with BP indices (r = 0:787; p < 0:05). The slope of the CCA displacement/pressure curve showed a progressive reduction with increasing age and EH disease occurrence (EH: 0.496 vs. normotension: 0.822; age <= 60:0.585 vs. age > 60:0.783). Our method provides an explicit reference value and relationship for the manner in which the CCA wall responds to changes in BP. Short-term and continuous BP were significantly correlated with CCA displacement and exhibited a close inverse relationship with each subject’s BP and EH, age, and systolic blood pressure.

Direct Detection of Pixel-Level Myocardial Infarction Areas via a Deep-Learning Algorithm

Accurate detection of the myocardial infarction (MI) area is crucial for early diagnosis planning and follow-up management. In this study, we propose an end-to-end deep-learning algorithm framework (OF-RNN) to accurately detect the MI area at the pixel level. Our OF-RNN consists of three different function layers: the heart localization layers, which can accurately and automatically crop the region-of-interest (ROI) sequences, including the left ventricle, using the whole cardiac magnetic resonance image sequences; the motion statistical layers, which are used to build a time-series architecture to capture two types of motion features (at the pixel-level) by integrating the local motion features generated by long short-term memory-recurrent neural networks and the global motion features generated by deep optical flows from the whole ROI sequence, which can effectively characterize myocardial physiologic function; and the fully connected discriminate layers, which use stacked auto-encoders to further learn these features, and they use a softmax classifier to build the correspondences from the motion features to the tissue identities (infarction or not) for each pixel. Through the seamless connection of each layer, our OF-RNN can obtain the area, position, and shape of the MI for each patient. Our proposed framework yielded an overall classification accuracy of 94.35% at the pixel level, from 114 clinical subjects. These results indicate the potential of our proposed method in aiding standardized MI assessments.

Functional assessment of the stenotic carotid artery by CFD-based pressure gradient evaluation

The functional assessment of a hemodynamic significant stenosis base on blood pressure variation has been applied for evaluation of the myocardial ischemic event. This functional assessment shows great potential for improving the accuracy of the classification of the severity of carotid stenosis. To explore the value of grading the stenosis using a pressure gradient (PG)-we had reconstructed patient-specific carotid geometries based on MRI images-computational fluid dynamics were performed to analyze the PG in their stenotic arteries. Doppler ultrasound image data and the corresponding MRI image data of 19 patients with carotid stenosis were collected. Based on these, 31 stenotic carotid arterial geometries were reconstructed. A combinatorial boundary condition method was implemented for steady-state computer fluid dynamics simulations. Anatomic parameters, including tortuosity (T), the angle of bifurcation, and the cross-sectional area of the remaining lumen, were collected to investigate the effect on the pressure distribution. The PG is highly correlated with the severe stenosis (r = 0.902), whereas generally, the T and the angle of the bifurcation negatively correlate to the pressure drop of the internal carotid artery stenosis. The calculation required <10 min/case, which made it prepared for the fast diagnosis of the severe stenosis. According to the results, we had proposed a potential threshold value for distinguishing severe stenosis from mild-moderate stenosis (PG = 0.88). In conclusion, the PG could serve as the additional factor for improving the accuracy of grading the severity of the stenosis.

Automated detection framework of the calcified plaque with acoustic shadowing in IVUS images.

Intravascular Ultrasound (IVUS) is one ultrasonic imaging technology to acquire vascular cross-sectional images for the visualization of the inner vessel structure. This technique has been widely used for the diagnosis and treatment of coronary artery diseases. The detection of the calcified plaque with acoustic shadowing in IVUS images plays a vital role in the quantitative analysis of atheromatous plaques. The conventional method of the calcium detection is manual drawing by the doctors. However, it is very time-consuming, and with high inter-observer and intra-observer variability between different doctors. Therefore, the computer-aided detection of the calcified plaque is highly desired. In this paper, an automated method is proposed to detect the calcified plaque with acoustic shadowing in IVUS images by the Rayleigh mixture model, the Markov random field, the graph searching method and the prior knowledge about the calcified plaque. The performance of our method was evaluated over 996 in-vivo IVUS images acquired from eight patients, and the detected calcified plaques are compared with manually detected calcified plaques by one cardiology doctor. The experimental results are quantitatively analyzed separately by three evaluation methods, the test of the sensitivity and specificity, the linear regression and the Bland-Altman analysis. The first method is used to evaluate the ability to distinguish between IVUS images with and without the calcified plaque, and the latter two methods can respectively measure the correlation and the agreement between our results and manual drawing results for locating the calcified plaque in the IVUS image. High sensitivity (94.68%) and specificity (95.82%), good correlation and agreement (>96.82% results fall within the 95% confidence interval in the Student t-test) demonstrate the effectiveness of the proposed method in the detection of the calcified plaque with acoustic shadowing in IVUS images.

Hemodynamics analysis of the serial stenotic coronary arteries

Coronary arterial stenoses, particularly serial stenoses in a single branch, are responsible for complex hemodynamic properties of the coronary arterial trees, and the uncertain prognosis of invasive intervention. Critical information of the blood flow redistribution in the stenotic arterial segments is required for the adequate treatment planning. Therefore, in this study, an image based non-invasive functional assessment is performed to investigate the hemodynamic significances of serial stenoses. Twenty patient-specific coronary arterial trees with different combinations of stenoses were reconstructed from the computer tomography angiography for the evaluation of the hemodynamics. Our results showed that the computed FFR based on CTA images (FFRCT) pullback curves with wall shear stress (WSS) distribution could provide more effectively examine the physiological significance of the locations of the segmental narrowing and the curvature of the coronary arterial segments. The paper thus provides the diagnostic efficacy of FFRCT pullback curve for noninvasive quantification of the hemodynamics of stenotic coronary arteries with serial lesions, compared to the gold standard invasive FFR, to provide a reliable physiological assessment of significant amount of coronary artery stenosis. Further, we were also able to demonstrate the potential of carrying out virtual revascularization, to enable more precise PCI procedures and improve their outcomes.

Association between beat-to-beat blood pressure variability and vascular elasticity in normal young adults during the cold pressor test

Abstract The beat-to-beat blood pressure (BP) monitoring parameters, such as average beat-to-beat BP, BP variability (BPV), could have an influence on the vascular elasticity. This study hypothesized that the elevated beat-to-beat BPV could evoke the reduction of the vascular elasticity independent of BP levels. We measured the beat-to-beat BP recordings and total arterial compliance (TAC), which was used to assess the vascular elasticity, in 80 young healthy adults during the cold pressor test (CPT). The CPT included 3 phases: baseline phase, cold stimulus phase, recovery phase. Six parameters were used to estimate BPV. In bivariate correlation analysis, TAC showed a significant correlation with systolic BP (SBP) and diastolic BP (DBP) in the cold stimulus phase; and 4 indices of SBP variability (SBPV) were associated with TAC (r = 0.271∼0.331, P ⩽ 0.015) in the recovery phase; similarly, 2 indices of DBP variability (DBPV) were also correlated with TAC (r = 0.221∼0.285, P ⩽ 0.048) in the recovery phase. In multivariate regression analysis, DBPV (&bgr; = 0.229, P = 0.001) was a determinant of TAC independent of average DBP, sex, and weight. In addition, both beat-to-beat BP and BPV values increased in the cold stimulus phase (P < 0.01); whereas, the TAC decreased in the cold stimulus phase (P < 0.01). In conclusion, these data suggest that the beat-to-beat DBPV shows an independent association with the vascular elasticity in young normal adults during the CPT.

Robust recovery of myocardial kinematics using dual

Accurate estimation of myocardial motion can help to better understand the pathophysiological processes of ischemic heart diseases. However, because of partial and noisy image-derived measurements on the cardiac kinematics, the performance of model-based motion estimation relies heavily on the assumption of noise distribution on the measurement data. While existing studies of model-based motion estimation have often adopted the ℋ2