Quantitative cardiac dual source CT; from morphology to function

Cardiovascular diseases are a large contributor to the global mortality rate. Non-invasive imaging techniques, such as computed tomography (CT) imaging, have been playing a growing role in the risk assessment, diagnosis, and prognosis of coronary artery disease (CAD). One of the main challenges in the evaluation of CAD is the establishment of the optimal workflow to evaluate the anatomical as well as the functional aspects of CAD in all phases of the ischemic cascade.The research described in this thesis investigates the possibilities of CT to perform both morphological and functional evaluation of CAD and it is debated whether CT can be used clinically for the visualization of the entire ischemic cascade.Results show that the diagnostic and prognostic value of CT procedures for coronary artery disease evaluation can be improved by adding additional functional information to the anatomical evaluation. This was concluded from research done on two new technologies analyzing the blood flow through the coronaries and through the heart muscle. Besides that, important questions regarding protocol optimization and standardization have been investigated. Although CT shows great potential for the evaluation of CAD, the clinical workflow and combination of techniques to be used is yet to be optimized. Automating processes, for example with the use of Artificial Intelligence (AI), can enhance the clinical implementation and can help the field of cardiac radiology deal with the increased demand for cardiac imaging

Patient-specific modeling of the biomechanics of vulnerable coronary artery plaques

Coronary artery atherosclerosis is a local, multifactorial, complex disease, and the leading cause of death in the US. Complex interactions between biochemical transport and biomechanical forces influence disease growth. Wall shear stress (WSS) affects coronary artery atherosclerosis by inducing endothelial cell mechanotransduction and by controlling the near- wall transport processes involved in atherosclerosis. The current management guidelines for detection of atherosclerotic plaques focus on morphological characterizations and the blockage percentage of the stenosis based on coronary computed tomography angiography (CCTA). Despite the progress achieved in therapeutics, the relation between hemodynamic environment and the composition of atherosclerotic plaques remains unexplored. This dissertation is divided into two main sections: the association between hemodynamics/biotransport and longitudinal changes in the plaque vulnerability characteristics and developing a 1D automatic vascular network generation package with the ability to be coupled with a 3D patient-specific model. Biochemical-specific mass transport models were developed to study low-density lipoprotein, nitric oxide, adenosine triphosphate, oxygen, monocyte chemoattractant protein-1, and monocyte transport. The transport results were compared with WSS vectors and WSS Lagrangian coherent structures (WSS LCS). High WSS magnitude protected against atherosclerosis by increasing the production or flux of atheroprotective biochemicals and decreasing the near-wall localization of atherogenic biochemicals. Low WSS magnitude promoted atherosclerosis by increasing atherogenic biochemical localization. To find the association between hemodynamics/biotransport and longitudinal changes in the atherosclerotic plaque characteristics, a plaque quantification software was developed with the aim of performing a segment-specific assessment to accurately calculate the volumes of low attenuation plaque (LAP), fibrous plaque (FP), calcium plaque (CP), and vessel wall and identify the quantitative plaque characteristics including spotty calcification, presence of napkin-ring sign, and positive remodeling. The changes in the different plaque characteristics were compared against the hemodynamic/biotransport parameters. The results showed that WSS magnitude is moderately correlated with the longitudinal changes in LAP, FP, and vessel wall volumes. Also, WSS magnitude and local concentration of nitric oxide (NO) showed a meaningful correlation with the presence of positive remodeling in the follow-up. A hybrid 1D-3D solver was developed in Simvascular software and validated against the existing data in the literature. The results of our coupled 1D-3D solver showed a good agreement with the 3D, deformable wall models. This solver can be used to solve the blood flow in a large network of 1D vessels coupled with a patient-specific 3D model. Finally, an automatic vascular network generation framework was developed using the Constraint Constructive Optimization (CCO) algorithm to study the generation of arterial trees based on theoretical perfusion maps. The algorithm simulated angiogenesis by optimizing the total vessel volume governed by physiological and geometrical constraints

Patient-specific blood flow modelling in diagnosis of coronary artery disease

This thesis presents the approach for development of patient-specific coronary blood flow models in 3D and 0D domains based on coronary artery geometries reconstructed from Coronary Computed Tomography Angiography datasets (CCTA). The computed flow patterns extend the diagnostic value of CCTA, which, being noninvasive imaging modality, provides only static information on the anatomy of epicardial arteries. The clinical indices extracted from the virtual blood flow can be potentially employed in the assessment of the haemodynamic severity of Coronary Artery Disease (CAD) lesions as well at the analysis of the underlying mechanisms of formation and localisation of atherosclerotic plaques. However, the existing patient-specific coronary blood flow modelling approaches are generally characterised by relatively high levels of uncertainty and instability due to a number of unknown factors and modelling assumptions. Analysis and comparison of the impact of various modelling assumptions has the potential to reduce this uncertainty. The overarching contributions of this thesis are the thorough analysis and investigation of the existing issues in patient-specific coronary blood flow simulations and the provision of the guidelines for the design and implementation of blood flow models in order to improve and ensure the reliability and accuracy of the numerical results. In addition, a novel approach for the implementation of spatially extended patient-specific 0D blood flow models was proposed, which significantly decreases the high computational costs generally associated with 3D blood flow simulations. While the classical 0D models based on the electrical–hydraulic analogy use the lumped-parameter representation of major vessel tree structures and are thus characterised by limited spatial characteristics, the proposed method for modelling of individual vessel tree branches through a series of 0D elements provides the means for correlation of the computed flow with the precise location along a vessel. Therefore, this extends the applicability of 0D modelling in patient-specific blood flow simulations for the assessment of functional stenosis severity

Personalized Three-Dimensional Printed Models in Congenital Heart Disease

Patient-specific three-dimensional (3D) printed models have been increasingly used in cardiology and cardiac surgery, in particular, showing great value in the domain of congenital heart disease (CHD). CHD is characterized by complex cardiac anomalies with disease variations between individuals; thus, it is difficult to obtain comprehensive spatial conceptualization of the cardiac structures based on the current imaging visualizations. 3D printed models derived from patient’s cardiac imaging data overcome this limitation by creating personalized 3D heart models, which not only improve spatial visualization, but also assist preoperative planning and simulation of cardiac procedures, serve as a useful tool in medical education and training, and improve doctor–patient communication. This review article provides an overall view of the clinical applications and usefulness of 3D printed models in CHD. Current limitations and future research directions of 3D printed heart models are highlighted

ImageCAS: A Large-Scale Dataset and Benchmark for Coronary Artery Segmentation based on Computed Tomography Angiography Images

Cardiovascular disease (CVD) accounts for about half of non-communicable diseases. Vessel stenosis in the coronary artery is considered to be the major risk of CVD. Computed tomography angiography (CTA) is one of the widely used noninvasive imaging modalities in coronary artery diagnosis due to its superior image resolution. Clinically, segmentation of coronary arteries is essential for the diagnosis and quantification of coronary artery disease. Recently, a variety of works have been proposed to address this problem. However, on one hand, most works rely on in-house datasets, and only a few works published their datasets to the public which only contain tens of images. On the other hand, their source code have not been published, and most follow-up works have not made comparison with existing works, which makes it difficult to judge the effectiveness of the methods and hinders the further exploration of this challenging yet critical problem in the community. In this paper, we propose a large-scale dataset for coronary artery segmentation on CTA images. In addition, we have implemented a benchmark in which we have tried our best to implement several typical existing methods. Furthermore, we propose a strong baseline method which combines multi-scale patch fusion and two-stage processing to extract the details of vessels. Comprehensive experiments show that the proposed method achieves better performance than existing works on the proposed large-scale dataset. The benchmark and the dataset are published at https://github.com/XiaoweiXu/ImageCAS-A-Large-Scale-Dataset-and-Benchmark-for-Coronary-Artery-Segmentation-based-on-CT.Comment: 17 pages, 12 figures, 4 table

A Deep Learning Approach to Evaluating Disease Risk in Coronary Bifurcations

Cardiovascular disease represents a large burden on modern healthcare systems, requiring significant resources for patient monitoring and clinical interventions. It has been shown that the blood flow through coronary arteries, shaped by the artery geometry unique to each patient, plays a critical role in the development and progression of heart disease. However, the popular and well tested risk models such as Framingham and QRISK3 current cardiovascular disease risk models are not able to take these differences when predicting disease risk. Over the last decade, medical imaging and image processing have advanced to the point that non-invasive high-resolution 3D imaging is routinely performed for any patient suspected of coronary artery disease. This allows for the construction of virtual 3D models of the coronary anatomy, and in-silico analysis of blood flow within the coronaries. However, several challenges still exist which preclude large scale patient-specific simulations, necessary for incorporating haemodynamic risk metrics as part of disease risk prediction. In particular, despite a large amount of available coronary medical imaging, extraction of the structures of interest from medical images remains a manual and laborious task. There is significant variation in how geometric features of the coronary arteries are measured, which makes comparisons between different studies difficult. Modelling blood flow conditions in the coronary arteries likewise requires manual preparation of the simulations and significant computational cost. This thesis aims to solve these challenges. The "Automated Segmentation of Coronary Arteries (ASOCA)" establishes a benchmark dataset of coronary arteries and their associated 3D reconstructions, which is currently the largest openly available dataset of coronary artery models and offers a wide range of applications such as computational modelling, 3D printed for experiments, developing, and testing medical devices such as stents, and Virtual Reality applications for education and training. An automated computational modelling workflow is developed to set up, run and postprocess simulations on the Left Main Bifurcation and calculate relevant shape metrics. A convolutional neural network model is developed to replace the computational fluid dynamics process, which can predict haemodynamic metrics such as wall shear stress in minutes, compared to several hours using traditional computational modelling reducing the computation and labour cost involved in performing such simulations