Non‐invasive coronary computed tomography (CT) angiography‐derived fractional flow reserve (cFFR) is an emergent approach to determine the functional relevance of obstructive coronary lesions. Its feasibility and diagnostic performance has been reported in several studies. It is unclear if differences in sensitivity and specificity between these studies are due to study design, population, or "computational methodology." We evaluate the diagnostic performance of four different computational workflows for the prediction of cFFR using a limited data set of 10 patients, three based on reduced‐order modelling and one based on a 3D rigid‐wall model. The results for three of these methodologies yield similar accuracy of 6.5% to 10.5% mean absolute difference between computed and measured FFR. The main aspects of modelling which affected cFFR estimation were choice of inlet and outlet boundary conditions and estimation of flow distribution in the coronary network. One of the reduced‐order models showed the lowest overall deviation from the clinical FFR measurements, indicating that reduced‐order models are capable of a similar level of accuracy to a 3D model. In addition, this reduced‐order model did not include a lumped pressure‐drop model for a stenosis, which implies that the additional effort of isolating a stenosis and inserting a pressure‐drop element in the spatial mesh may not be required for FFR estimation. The present benchmark study is the first of this kind, in which we attempt to homogenize the data required to compute FFR using mathematical models. The clinical data utilised in the cFFR workflows are made publicly available online.

The paper addresses methods for generation of individualized computational domains on the basis of medical imaging dataset. The computational domains will be used in one-dimensional (1D) and three-dimensional (3D)-1D coupled hemodynamic models. A 1D hemodynamic model employs a 1D network of a patient-specific vascular network with large number of vessels. The 1D network is the graph with nodes in the 3D space which bears additional geometric data such as length and radius of vessels. A 3D hemodynamic model requires a detailed 3D reconstruction of local parts of the vascular network. We propose algorithms which extend the automated segmentation of vascular and tubular structures, generation of centerlines, 1D network reconstruction, correction, and local adaptation. We consider two modes of centerline representation: (i) skeletal segments or sets of connected voxels and (ii) curved paths with corresponding radii. Individualized reconstruction of 1D networks depends on the mode of centerline representation. Efficiency of the proposed algorithms is demonstrated on several examples of 1D network reconstruction. The networks can be used in modeling of blood flows as well as other physiological processes in tubular structures. Copyright © 2015 John Wiley & Sons, Ltd.

Fluorescence optical imaging techniques have revolutionized the field of cardiac electrophysiology and advanced our understanding of complex electrical activities such as arrhythmias. However, traditional monocular optical mapping systems, despite having high spatial resolution, are restricted to a two-dimensional (2D) field of view. Consequently, tracking complex three-dimensional (3D) electrical waves such as during ventricular fibrillation is challenging as the waves rapidly move in and out of the field of view. This problem has been solved by panoramic imaging which uses multiple cameras to measure the electrical activity from the entire epicardial surface. However, the diverse engineering skill set and substantial resource cost required to design and implement this solution have made it largely inaccessible to the biomedical research community at large. To address this barrier to entry, we present an open source toolkit for building panoramic optical mapping systems which includes the 3D printing of perfusion and imaging hardware, as well as software for data processing and analysis. In this paper, we describe the toolkit and demonstrate it on different mammalian hearts: mouse, rat, and rabbit.

Atherosclerotic diseases of coronary vessels are the main reasons of myocardial ischemia. The value of the fractional flow reserve (FFR) factor is the golden standard for making decision on coronary network surgical treatment. The FFR measurements require expensive endovascular diagnostics. We propose a noninvasive method of the virtual FFR assessment in patient-specific coronary network based on angiography and computer tomography data. Also we analyze sensitivity of the model to the heart stroke volume.

Since the 1970s fluorescence imaging has become a leading tool in the discovery of mechanisms of cardiac function and arrhythmias. Gradual improvements in fluorescent probes and multi-camera technology have increased the power of optical mapping and made a major impact on the field of cardiac electrophysiology. Tandem-lens optical mapping systems facilitated simultaneous recording of multiple parameters characterizing cardiac function. However, high cost and technological complexity restricted its proliferation to the wider biological community. We present here, an open-source solution for multiple-camera tandem-lens optical systems for multiparametric mapping of transmembrane potential, intracellular calcium dynamics and other parameters in intact mouse hearts and in rat heart slices. This 3D-printable hardware and Matlab-based RHYTHM 1.2 analysis software are distributed under an MIT open-source license. Rapid prototyping permits the development of inexpensive, customized systems with broad functionality, allowing wider application of this technology outside biomedical engineering laboratories.

Patient-speci c simulations of human physiological processes remain the challenge for many years. Detailed 3D reconstruction of body anatomical parts on the basis of medical images is an important stage of individualized simulations in physiology. In this paper we present and develop the methods and algorithms for construction of patient-speci c discrete geometric models. These models are represented by anatomically correct computational meshes. Practical use of these methods is demonstrated for two important medical applications: numerical evaluation of fractional ow reserve in coronary arteries and electrocardiography simulation.Individualized numerical simulations of physiological processes in the human body remain the challenge for many years. Contemporary resolution of medical images and new algorithms for their postprocessing allow to develop anatomically correct numerical models of various processes such as patient-speci c blood circulation, cardiac electrophysiology etc. In this paper we present and develop the methods and algorithms for construction of patient-speci c discrete geometric models. These models are represented by anatomically correct computational meshes. The methods are general-purpose and can be applied to any region or network of the human body. We demonstrate practicability of the methods for two important medical applications. Each application imposes speci c restrictions on both the input medical images and the output patient-speci c discrete model, and, therefore, calls for a speci c class of 3D reconstruction methods. The rst application deals with numerical evaluation of fractional ow reserve (FFR) in coronary arteries. The second application deals with electrocardiography simulation (ECG).Atherosclerotic diseases of coronary vessels are the main reasons of widespread myocardial ischemia frequently resulting in disability or death. The basic methods of medical treatment assume invasive endovascular intervention (bypassing, stenting, et al.). The use of these methods is restricted in some cases due to personal contraindications or low e ectiveness. The main criterion of the endovascular surgical treatment efciency is the value of FFR. It is calculated as the ratio of the mean coronary pressure distal to the lesion after dilator administration to the mean aortic pressure [20, 27, 52]. The coronary pressure is normally measured by ultrasound endovascular transducer. This measurement requires expensive and invasive endovascular intervention. Contemporary non-invasive methods are based on 3D blood ow simulation in the vicinity of the lesion [20]. The method is subject to criticism [34, 50] due to bad posedness of upstream and downstream boundary conditions, rigid wall approximation for the vessel tissue, large computational cost, general diculties in parameters tting.

Abstract:In this study, we present several image segmentation techniques for various image scales and modalities. We consider cellular-, organ-, and whole organism-levels of biological structures in cardiovascular applications. Several automatic segmentation techniques are presented and discussed in this work. The overall pipeline for reconstruction of biological structures consists of the following steps: image pre-processing, feature detection, initial mask generation, mask processing, and segmentation post-processing. Several examples of image segmentation are presented, including patient-specific abdominal tissues segmentation, vascular network identification and myocyte lipid droplet micro-structure reconstruction.

Abstract. 1D model is used to simulate blood flow in major vessels of the upper body and head. The 1D part is stated in terms of viscous incompressible fluid flow in the network of elastic tubes. Two different types of junctions are considered: junctions between major vessels and junctions between arteries and veins. Vessel network reconstruction algorithm consists of vessel segmentation, thinning-based obtaining of set of centerlines, and graph reconstruction. Input data is 3D DICOM datasets, obtained with contrast enhanced Computed Tomography (CT) Angiography. Constructed model is used to study the influence of carotid artery stenosis on the direction of blood flow in the circle of Willis.