Mechanical Characterization of the Vessel Wall by Data Assimilation of Intravascular Ultrasound Studies

Atherosclerotic plaque rupture and erosion are the most important mechanisms underlying the sudden plaque growth, responsible for acute coronary syndromes and even fatal cardiac events. Advances in the understanding of the culprit plaque structure and composition are already reported in the literature, however, there is still much work to be done toward in-vivo plaque visualization and mechanical characterization to assess plaque stability, patient risk, diagnosis and treatment prognosis. In this work, a methodology for the mechanical characterization of the vessel wall plaque and tissues is proposed based on the combination of intravascular ultrasound (IVUS) imaging processing, data assimilation and continuum mechanics models within a high performance computing (HPC) environment. Initially, the IVUS study is gated to obtain volumes of image sequences corresponding to the vessel of interest at different cardiac phases. These sequences are registered against the sequence of the end-diastolic phase to remove transversal and longitudinal rigid motions prescribed by the moving environment due to the heartbeat. Then, optical flow between the image sequences is computed to obtain the displacement fields of the vessel (each associated to a certain pressure level). The obtained displacement fields are regarded as observations within a data assimilation paradigm, which aims to estimate the material parameters of the tissues within the vessel wall. Specifically, a reduced order unscented Kalman filter is employed, endowed with a forward operator which amounts to address the solution of a hyperelastic solid mechanics model in the finite strain regime taking into account the axially stretched state of the vessel, as well as the effect of internal and external forces acting on the arterial wall. Due to the computational burden, a HPC approach is mandatory. Hence, the data assimilation and computational solid mechanics computations are parallelized at three levels: (i) a Kalman filter level; (ii) a cardiac phase level; and (iii) a mesh partitioning level. To illustrate the capabilities of this novel methodology toward the in-vivo analysis of patient-specific vessel constituents, mechanical material parameters are estimated using in-silico and in-vivo data retrieved from IVUS studies. Limitations and potentials of this approach are exposed and discussed.Fil: Maso Talou, Gonzalo Daniel. Laboratorio Nacional de Computacao Cientifica; BrasilFil: Blanco, Pablo Javier. Laboratorio Nacional de Computacao Cientifica; BrasilFil: Ares, Gonzalo Damián. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Departamento de Mecanica. Grupo de Ingeniería Asistida Por Computador; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; ArgentinaFil: Guedes Bezerra, Cristiano. Heart Institute (Incor); BrasilFil: Lemos, Pedro A.. Heart Institute (Incor); BrasilFil: Feijóo, Raúl Antonino. Laboratorio Nacional de Computacao Cientifica; Brasi

Coronary Artery Segmentation and Motion Modelling

Conventional coronary artery bypass surgery requires invasive sternotomy and the use of a cardiopulmonary bypass, which leads to long recovery period and has high infectious potential. Totally endoscopic coronary artery bypass (TECAB) surgery based on image guided robotic surgical approaches have been developed to allow the clinicians to conduct the bypass surgery off-pump with only three pin holes incisions in the chest cavity, through which two robotic arms and one stereo endoscopic camera are inserted. However, the restricted field of view of the stereo endoscopic images leads to possible vessel misidentification and coronary artery mis-localization. This results in 20-30% conversion rates from TECAB surgery to the conventional approach. We have constructed patient-specific 3D + time coronary artery and left ventricle motion models from preoperative 4D Computed Tomography Angiography (CTA) scans. Through temporally and spatially aligning this model with the intraoperative endoscopic views of the patient's beating heart, this work assists the surgeon to identify and locate the correct coronaries during the TECAB precedures. Thus this work has the prospect of reducing the conversion rate from TECAB to conventional coronary bypass procedures. This thesis mainly focus on designing segmentation and motion tracking methods of the coronary arteries in order to build pre-operative patient-specific motion models. Various vessel centreline extraction and lumen segmentation algorithms are presented, including intensity based approaches, geometric model matching method and morphology-based method. A probabilistic atlas of the coronary arteries is formed from a group of subjects to facilitate the vascular segmentation and registration procedures. Non-rigid registration framework based on a free-form deformation model and multi-level multi-channel large deformation diffeomorphic metric mapping are proposed to track the coronary motion. The methods are applied to 4D CTA images acquired from various groups of patients and quantitatively evaluated

Coronary Artery Calcium Quantification in Contrast-enhanced Computed Tomography Angiography

Coronary arteries are the blood vessels supplying oxygen-rich blood to the heart muscles. Coronary artery calcium (CAC), which is the total amount of calcium deposited in these arteries, indicates the presence or the future risk of coronary artery diseases. Quantification of CAC is done by using computed tomography (CT) scan which uses attenuation of x-ray by different tissues in the body to generate three-dimensional images. Calcium can be easily spotted in the CT images because of its higher opacity to x-ray compared to that of the surrounding tissue. However, the arteries cannot be identified easily in the CT images. Therefore, a second scan is done after injecting a patient with an x-ray opaque dye known as contrast material which makes different chambers of the heart and the coronary arteries visible in the CT scan. This procedure is known as computed tomography angiography (CTA) and is performed to assess the morphology of the arteries in order to rule out any blockage in the arteries. The CT scan done without the use of contrast material (non-contrast-enhanced CT) can be eliminated if the calcium can be quantified accurately from the CTA images. However, identification of calcium in CTA images is difficult because of the proximity of the calcium and the contrast material and their overlapping intensity range. In this dissertation first we compare the calcium quantification by using a state-of-the-art non-contrast-enhanced CT scan method to conventional methods suggesting optimal quantification parameters. Then we develop methods to accurately quantify calcium from the CTA images. The methods include novel algorithms for extracting centerline of an artery, calculating the threshold of calcium adaptively based on the intensity of contrast along the artery, calculating the amount of calcium in mixed intensity range, and segmenting the artery and the outer wall. The accuracy of the calcium quantification from CTA by using our methods is higher than the non-contrast-enhanced CT thus potentially eliminating the need of the non-contrast-enhanced CT scan. The implications are that the total time required for the CT scan procedure, and the patient\u27s exposure to x-ray radiation are reduced

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

Intravascular Ultrasound

Intravascular ultrasound (IVUS) is a cardiovascular imaging technology using a specially designed catheter with a miniaturized ultrasound probe for the assessment of vascular anatomy with detailed visualization of arterial layers. Over the past two decades, this technology has developed into an indispensable tool for research and clinical practice in cardiovascular medicine, offering the opportunity to gather diagnostic information about the process of atherosclerosis in vivo, and to directly observe the effects of various interventions on the plaque and arterial wall. This book aims to give a comprehensive overview of this rapidly evolving technique from basic principles and instrumentation to research and clinical applications with future perspectives

Cardiac Phase Retrieval in Intravascular Ultrasound Sequences

Acquisition of intravascular ultrasound (IVUS) sequences is a major artifact for 3-D exploring of coronary arteries. Most current techniques are based on the electrocardiogram (ECG) signal to obtain a gated pullback without longitudinal motion by using specific hardware or the ECG signal itself. We present an image-based approach for cardiac phase retrieval from coronary IVUS sequences without an ECG signal. A signal reflecting cardiac motion is computed by exploring the image intensity local mean evolution. The signal is filtered by a series of filters namely Gaussian and Optical at the main cardiac frequency. After applying minimum and maximum values of threshold, based on the pixel values at different position, the cardiac phase is retrieved and the report is mailed to the concerned. DOI: 10.17762/ijritcc2321-8169.15039

Optical coherence tomography-based patient-specific coronary artery reconstruction and fluid–structure interaction simulation

Plaque rupture is related to the mechanical stress it suffered. The value and distribution of the mechanical stress in plaque could help on assessing plaque vulnerability. To look into the stress conditions in the coronary artery, a patient-specific coronary model was created by using optical coherence tomography (OCT) and angiography imaging data. The reconstructed coronary model consisted of the structure of the lumen, the arterial wall and plaque components. Benefited by the high resolution of OCT, detailed structures such as the thin fibrous cap could be observed and built into the geometry. On this reconstructed coronary model, a fully coupled fluid–structure interaction (FSI) simulation was performed. The principle stress in coronary plaque and the wall shear stress (WSS) were analyzed. The FSI simulation results show that the cap thickness had a significant effect on the stress, and the principle stress at the thin cap area was more than double of those at the locations with a larger thickness. WSS is thought as an important parameter to assess the potentially dangerous areas of the atherosclerosis-prone (caused by low WSS) and the plaque rupture (high WSS). From the WSS plots of our FSI model, the area with abnormal WSS value was detected around the position where a lipid core existed. The FSI simulation results were compared with the results from the conventional structure-only and the computational fluid dynamics (CFD)-only computational models to quantify the difference between the three models. We found little difference in the principle stress results between the FSI and the structure-only model, but a significant difference between the FSI and the CFD-only model when looking into the WSS. The WSS values at the two observation spots from the CFD-only model were higher than the values from the FSI model by 17.95% and 22.66% in average, respectively. Furthermore, the FSI model detected more areas of low WSS, because the fluid domain could expand circumferentially when pressure loaded on the flexible arterial. This study suggests that OCT-based FSI model may be useful for plaque vulnerability assessment and it may be critical to perform the FSI simulation if an accurate WSS value is required