Fully automated segmentation and tracking of the intima media thickness in ultrasound video sequences of the common carotid artery

Abstract—The robust identification and measurement of the intima media thickness (IMT) has a high clinical relevance because it represents one of the most precise predictors used in the assessment of potential future cardiovascular events. To facilitate the analysis of arterial wall thickening in serial clinical investigations, in this paper we have developed a novel fully automatic algorithm for the segmentation, measurement, and tracking of the intima media complex (IMC) in B-mode ultrasound video sequences. The proposed algorithm entails a two-stage image analysis process that initially addresses the segmentation of the IMC in the first frame of the ultrasound video sequence using a model-based approach; in the second step, a novel customized tracking procedure is applied to robustly detect the IMC in the subsequent frames. For the video tracking procedure, we introduce a spatially coherent algorithm called adaptive normalized correlation that prevents the tracking process from converging to wrong arterial interfaces. This represents the main contribution of this paper and was developed to deal with inconsistencies in the appearance of the IMC over the cardiac cycle. The quantitative evaluation has been carried out on 40 ultrasound video sequences of the common carotid artery (CCA) by comparing the results returned by the developed algorithm with respect to ground truth data that has been manually annotated by clinical experts. The measured IMTmean ± standard deviation recorded by the proposed algorithm is 0.60 mm ± 0.10, with a mean coefficient of variation (CV) of 2.05%, whereas the corresponding result obtained for the manually annotated ground truth data is 0.60 mm ± 0.11 with a mean CV equal to 5.60%. The numerical results reported in this paper indicate that the proposed algorithm is able to correctly segment and track the IMC in ultrasound CCA video sequences, and we were encouraged by the stability of our technique when applied to data captured under different imaging conditions. Future clinical studies will focus on the evaluation of patients that are affected by advanced cardiovascular conditions such as focal thickening and arterial plaques

Robust semi-automated path extraction for visualising stenosis of the coronary arteries

Computed tomography angiography (CTA) is useful for diagnosing and planning treatment of heart disease. However, contrast agent in surrounding structures (such as the aorta and left ventricle) makes 3-D visualisation of the coronary arteries difficult. This paper presents a composite method employing segmentation and volume rendering to overcome this issue. A key contribution is a novel Fast Marching minimal path cost function for vessel centreline extraction. The resultant centreline is used to compute a measure of vessel lumen, which indicates the degree of stenosis (narrowing of a vessel). Two volume visualisation techniques are presented which utilise the segmented arteries and lumen measure. The system is evaluated and demonstrated using synthetic and clinically obtained datasets

Replacing vascular corrosion casting by in-vivo micro-CT imaging for building 3D cardiovascular models in mice

The purpose of this study was to investigate if in vivo micro-computed tomography (CT) is a reliable alternative to micro-CT scanning of a vascular corrosion cast. This would allow one to study the early development of cardiovascular diseases. Datasets using both modalities were acquired, segmented, and used to generate a 3D geometrical model from nine mice. As blood pool contrast agent, Fenestra VC-131 was used. Batson's No. 17 was used as casting agent. Computational fluid dynamics simulations were performed on both datasets to quantify the difference in wall shear stress (WSS). Aortic arch diameters show 30% to 40% difference between the Fenestra VC-131 and the casted dataset. The aortic arch bifurcation angles show less than 20% difference between both datasets. Numerically computed WSS showed a 28% difference between both datasets. Our results indicate that in vivo micro-CT imaging can provide an excellent alternative for vascular corrosion casting. This enables follow-up studies

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Abnormal wave reflections and left ventricular hypertrophy late after coarctation of the aorta repair

Patients with repaired coarctation of the aorta are thought to have increased afterload due to abnormalities in vessel structure and function. We have developed a novel cardiovascular magnetic resonance protocol that allows assessment of central hemodynamics, including central aortic systolic blood pressure, resistance, total arterial compliance, pulse wave velocity, and wave reflections. The main study aims were to (1) characterize group differences in central aortic systolic blood pressure and peripheral systolic blood pressure, (2) comprehensively evaluate afterload (including wave reflections) in the 2 groups, and (3) identify possible biomarkers among covariates associated with elevated left ventricular mass (LVM). Fifty adult patients with repaired coarctation and 25 age- and sex-matched controls were recruited. Ascending aorta area and flow waveforms were obtained using a high temporal-resolution spiral phase-contrast cardiovascular magnetic resonance flow sequence. These data were used to derive central hemodynamics and to perform wave intensity analysis noninvasively. Covariates associated with LVM were assessed using multivariable linear regression analysis. There were no significant group differences (P≥0.1) in brachial systolic, mean, or diastolic BP. However central aortic systolic blood pressure was significantly higher in patients compared with controls (113 versus 107 mm Hg, P=0.002). Patients had reduced total arterial compliance, increased pulse wave velocity, and larger backward compression waves compared with controls. LVM index was significantly higher in patients than controls (72 versus 59 g/m(2), P<0.0005). The magnitude of the backward compression waves was independently associated with variation in LVM (P=0.01). Using a novel, noninvasive hemodynamic assessment, we have shown abnormal conduit vessel function after coarctation of the aorta repair, including abnormal wave reflections that are associated with elevated LVM

From 4D medical images (CT, MRI, and Ultrasound) to 4D structured mesh models of the left ventricular endocardium for patient-specific simulations

With cardiovascular disease (CVD) remaining the primary cause of death worldwide, early detection of CVDs becomes essential. The intracardiac flow is an important component of ventricular function, motion kinetics, wash-out of ventricular chambers, and ventricular energetics. Coupling between Computational Fluid Dynamics (CFD) simulations and medical images can play a fundamental role in terms of patient-specific diagnostic tools. From a technical perspective, CFD simulations with moving boundaries could easily lead to negative volumes errors and the sudden failure of the simulation. The generation of high-quality 4D meshes (3D in space + time) with 1-to-l vertex becomes essential to perform a CFD simulation with moving boundaries. In this context, we developed a semiautomatic morphing tool able to create 4D high-quality structured meshes starting from a segmented 4D dataset. To prove the versatility and efficiency, the method was tested on three different 4D datasets (Ultrasound, MRI, and CT) by evaluating the quality and accuracy of the resulting 4D meshes. Furthermore, an estimation of some physiological quantities is accomplished for the 4D CT reconstruction. Future research will aim at extending the region of interest, further automation of the meshing algorithm, and generating structured hexahedral mesh models both for the blood and myocardial volume