3D Coronary Vessel Reconstruction from Bi-Plane Angiography Using Graph Convolutional Networks

X-ray coronary angiography (XCA) is used to assess coronary artery disease and provides valuable information on lesion morphology and severity. However, XCA images are 2D and therefore limit visualisation of the vessel. 3D reconstruction of coronary vessels is possible using multiple views, however lumen border detection in current software is performed manually resulting in limited reproducibility and slow processing time. In this study we propose 3DAngioNet, a novel deep learning (DL) system that enables rapid 3D vessel mesh reconstruction using 2D XCA images from two views. Our approach learns a coarse mesh template using an EfficientB3-UNet segmentation network and projection geometries, and deforms it using a graph convolutional network. 3DAngioNet outperforms similar automated reconstruction methods, offers improved efficiency, and enables modelling of bifurcated vessels. The approach was validated using state-of-the-art software verified by skilled cardiologists

FastSVD-ML-ROM\textit{FastSVD-ML-ROM}: A Reduced-Order Modeling Framework based on Machine Learning for Real-Time Applications

Digital twins have emerged as a key technology for optimizing the performance of engineering products and systems. High-fidelity numerical simulations constitute the backbone of engineering design, providing an accurate insight into the performance of complex systems. However, large-scale, dynamic, non-linear models require significant computational resources and are prohibitive for real-time digital twin applications. To this end, reduced order models (ROMs) are employed, to approximate the high-fidelity solutions while accurately capturing the dominant aspects of the physical behavior. The present work proposes a new machine learning (ML) platform for the development of ROMs, to handle large-scale numerical problems dealing with transient nonlinear partial differential equations. Our framework, mentioned as $\textit{FastSVD-ML-ROM}$, utilizes $\textit{(i)}$ a singular value decomposition (SVD) update methodology, to compute a linear subspace of the multi-fidelity solutions during the simulation process, $\textit{(ii)}$ convolutional autoencoders for nonlinear dimensionality reduction, $\textit{(iii)}$ feed-forward neural networks to map the input parameters to the latent spaces, and $\textit{(iv)}$ long short-term memory networks to predict and forecast the dynamics of parametric solutions. The efficiency of the $\textit{FastSVD-ML-ROM}$ framework is demonstrated for a 2D linear convection-diffusion equation, the problem of fluid around a cylinder, and the 3D blood flow inside an arterial segment. The accuracy of the reconstructed results demonstrates the robustness and assesses the efficiency of the proposed approach.Comment: 35 pages, 22 figure

Strut protrusion and shape impact on endothelial shear stress: insights from pre-clinical study comparing Mirage and Absorb bioresorbable scaffolds

Protrusion of scaffold struts is related with local coronary flow dynamics that can promote scaffold restenosis and thrombosis. That fact has prompted us to investigate in vivo the protrusion status of different types of scaffolds and their relationship with endothelial shear stress (ESS) distributions. Six Absorb everolimus-eluting Bioresorbable Vascular Scaffolds (Absorb, Abbott Vascular) and 11 Mirage sirolimus-eluting Bioresorbable Microfiber Scaffolds (Mirage, Manli Cardiology) were implanted in coronaries of eight mini pigs. Optical coherence tomography (OCT) was performed post-scaffold implantation and obtained images were fused with angiographic data to reconstruct the three dimensional coronary anatomy. Blood flow simulation was performed and ESS distribution was estimated for each scaffold. Protrusion distance was estimated using a dedicated software. Correlation between OCT-derived protrusion and ESS distribution was assessed for both scaffold groups. A significant difference was observed in the protrusion distances (156 ± 137 µm for Absorb, 139 ± 153 µm for Mirage; p = 0.035), whereas difference remained after adjusting the protrusion distances according to the luminal areas. Strut protrusion of Absorb is inversely correlated with ESS (r = -0.369, p < 0.0001), whereas in Mirage protrusion was positively correlated with EES (r = 0.192, p < 0.0001). Protrusion distance was higher in Absorb than in Mirage. The protrusion of the thick quadratic struts of Absorb has a tendency to lower shear stress in the close vicinity of struts. However, circular shape of the less thick struts of Mirage didn't show this trend in creating zone of recirculation around the struts. Strut geometry has different effect on the relationship between protrusion and shear stress in Absorb and Mirage scaffolds

Cell-based maximum entropy approximants for three-dimensional domains: Application in large strain elastodynamics using the meshless total Lagrangian explicit dynamics method

We present the cell-based maximum entropy (CME) approximants in E3 space by constructing the smooth approximation distance function to polyhedral surfaces. CME is a meshfree approximation method combining the properties of the maximum entropy approximants and the compact support of element-based interpolants. The method is evaluated in problems of large strain elastodynamics for three-dimensional (3D) continua using the well-established meshless total Lagrangian explicit dynamics method. The accuracy and efficiency of the method is assessed in several numerical examples in terms of computational time, accuracy in boundary conditions imposition, and strain energy density error. Due to the smoothness of CME basis functions, the numerical stability in explicit time integration is preserved for large time step. The challenging task of essential boundary condition (EBC) imposition in noninterpolating meshless methods (eg, moving least squares) is eliminated in CME due to the weak Kronecker-delta property. The EBCs are imposed directly, similar to the finite element method. CME is proven a valuable alternative to other meshless and element-based methods for large-scale elastodynamics in 3D. A naive implementation of the CME approximants in E3 is available to download at https://www.mountris.org/software/mlab/cme.Fil: Mountris, Konstantinos A.. Universidad de Zaragoza; EspañaFil: Bourantas, George C.. University of Western Australia; AustraliaFil: Millán, Raúl Daniel. Universidad Nacional de Cuyo. Facultad de Ciencias Aplicadas a la Industria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Joldes, Grand R.. University of Western Australia; AustraliaFil: Miller, Karol. Cardiff University; Reino Unido. University of Western Australia; AustraliaFil: Pueyo, Esther. Centro de Investigacion Biomedica En Red.; España. Universidad de Zaragoza; EspañaFil: Wittek, Adam. University of Western Australia; Australi

A voxelized immersed boundary (VIB) finite element method for accurate and efficient blood flow simulation

We present an efficient and accurate immersed boundary (IB) finite element (FE) method for internal flow problems with complex geometries (e.g., blood flow in the vascular system). In this study, we use a voxelized flow domain (discretized with hexahedral and tetrahedral elements) instead of a box domain, which is frequently used in IB methods. The proposed method utilizes the well-established incremental pressure correction scheme (IPCS) FE solver, and the boundary condition-enforced IB (BCE-IB) method to numerically solve the transient, incompressible Navier--Stokes flow equations. We verify the accuracy of our numerical method using the analytical solution for the Poiseuille flow in a cylinder, and the available experimental data (laser Doppler velocimetry) for the flow in a three-dimensional 90{\deg} angle tube bend. We further examine the accuracy and applicability of the proposed method by considering flow within complex geometries, such as blood flow in aneurysmal vessels and the aorta, flow configurations that would otherwise be difficult to solve by most IB methods. Our method offers high accuracy, as demonstrated by the verification examples, and high applicability, as demonstrated through the solution of blood flow within complex geometry. The proposed method is efficient, since it is as fast as the traditional finite element method used to solve the Navier--Stokes flow equations, with a small overhead (not more than 5$\%$) due to the numerical solution of a linear system formulated for the IB method.Comment: arXiv admin note: substantial text overlap with arXiv:2007.0208

A physics-based machine learning technique rapidly reconstructs the wall-shear stress and pressure fields in coronary arteries

With the global rise of cardiovascular disease including atherosclerosis, there is a high demand or accurate diagnostic tools that can be used during a short consultation. In view of pathology, abnormal blood flow patterns have been demonstrated to be strong predictors of atherosclerotic lesion incidence, location, progression, and rupture. Prediction of patient-specific blood flow patterns can hence enable fast clinical diagnosis. However, the current state of art for the technique is by employing 3D-imaging-based Computational Fluid Dynamics (CFD). The high computational cost renders these methods impractical. In this work, we present a novel method to expedite the reconstruction of 3D pressure and shear stress fields using a combination of a reduced-order CFD modelling technique together with non-linear regression tools from the Machine Learning (ML) paradigm. Specifically, we develop a proof-of-concept automated pipeline that uses randomised perturbations of an atherosclerotic pig coronary artery to produce a large dataset of unique mesh geometries with variable blood flow. A total of 1407 geometries were generated from seven reference arteries and were used to simulate blood flow using the CFD solver Abaqus. This CFD dataset was then post-processed using the mesh-domain common-base Proper Orthogonal Decomposition (cPOD) method to obtain Eigen functions and principal coefficients, the latter of which is a product of the individual mesh flow solutions with the POD Eigenvectors. Being a data-reduction method, the POD enables the data to be represented using only the ten most significant modes, which captures cumulatively greater than 95% of variance of flow features due to mesh variations. Next, the node coordinate data of the meshes were embedded in a two-dimensional coordinate system using the t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm. The reduced dataset for t-SNE coordinates and corresponding vector of POD coefficients were then used to train a Random Forest Regressor (RFR) model. The same methodology was applied to both the volumetric pressure solution and the wall shear stress. The predicted pattern of blood pressure, and shear stress in unseen arterial geometries were compared with the ground truth CFD solutions on 'unseen' meshes. The new method was able to reliably reproduce the 3D coronary artery haemodynamics in less than 10 seconds

Acid-Base and Electrolyte Abnormalities in Patients With Acute Leukemia

Disturbances of acid-base balance and electrolyte abnormalities are commonly seen in patients with acute leukemia. Our study aimed at illuminating the probable pathogenetic mechanisms responsible for these disturbances in patients with acute leukemia admitted to our hospital. We studied 66 patients (24 men and 44 women) aged between 17 and 87 years old on their admission and prior to any therapeutic intervention. Patients with diabetes mellitus, acute or chronic renal failure, hepatic failure, patients receiving drugs that influence acid-base status and electrolyte parameters during the last month, such as corticosteroids, cisplatin, diuretics, antacids, aminoglycosides, amphotericin, penicillin, and K + , PO 4 3− , or Mg 2+ supplements were excluded. Forty-one patients had at least one acid-base or electrolyte disturbance. There were no significant differences in the incidence of acid-base balance and electrolyte abnormalities between patients with acute myeloid leukemia (AML) and patients with acute lymphoblastic leukemia (ALL). The most frequent electrolyte abnormality was hypokalemia, observed in 41 patients (63%), namely in 34 patients with AML, and 7 with ALL; the main underlying pathophysiologic mechanism was inappropriate kaliuresis. Furthermore, hypokalemic patients more frequently experienced concurrent electrolyte disturbances (i.e., hyponatremia, hypocalcemia, hypophosphatemia, and hypomagnesemia), as well as various acid-base abnormalities compared to normokalemic patients. Hypokalemia in patients with acute leukemia may serve as an indicator of multiple concurrent, interrelated electrolyte disturbances, especially in patients with AML. Am