Airflow in a Multiscale Subject-Specific Breathing Human Lung Model

The airflow in a subject-specific breathing human lung is simulated with a multiscale computational fluid dynamics (CFD) lung model. The three-dimensional (3D) airway geometry beginning from the mouth to about 7 generations of airways is reconstructed from the multi-detector row computed tomography (MDCT) image at the total lung capacity (TLC). Along with the segmented lobe surfaces, we can build an anatomically-consistent one-dimensional (1D) airway tree spanning over more than 20 generations down to the terminal bronchioles, which is specific to the CT resolved airways and lobes (J Biomech 43(11): 2159-2163, 2010). We then register two lung images at TLC and the functional residual capacity (FRC) to specify subject-specific CFD flow boundary conditions and deform the airway surface mesh for a breathing lung simulation (J Comput Phys 244:168-192, 2013). The 1D airway tree bridges the 3D CT-resolved airways and the registration-derived regional ventilation in the lung parenchyma, thus a multiscale model. Large eddy simulation (LES) is applied to simulate airflow in a breathing lung (Phys Fluids 21:101901, 2009). In this fluid dynamics video, we present the distributions of velocity, pressure, vortical structure, and wall shear stress in a breathing lung model of a normal human subject with a tidal volume of 500 ml and a period of 4.8 s. On exhalation, air streams from child branches merge in the parent branch, inducing oscillatory jets and elongated vortical tubes. On inhalation, the glottal constriction induces turbulent laryngeal jet. The sites where high wall shear stress tends to occur on the airway surface are identified for future investigation of mechanotransduction.Comment: This submission is part of the APS DFD Gallery of Fluid Motio

Attention-driven tree-structured convolutional LSTM for high dimensional data understanding

Modeling sequential information for image sequences is a vital step of various vision tasks and convolutional long short-term memory (ConvLSTM) has demonstrated its superb performance in such spatiotemporal problems. Nevertheless, the hierarchical data structures (e.g., human body parts and vessel/airway tree in biomedical images) in various tasks cannot be properly modeled by sequential models. Thus, ConvLSTM is not suitable for analyzing tree-structured image data that has a rich relation among its elements. In order to address this limitation, we present a tree-structured ConvLSTM model for tree-structured image analysis which can be trained end-to-end. To demonstrate its effectiveness, we first evaluate the proposed tree-structured ConvLSTM model on a synthetic Tree-Moving-MNIST dataset for tree-structured modeling. Experimental results demonstrate the superiority of the tree-structured ConvLSTM model for tree-structured image analysis compared with other alternatives. Additionally, we present a tree-structured segmentation framework which consists of a tree-structured ConvLSTM layer and an attention fully convolutional network (FCN) model. The proposed framework is validated on four large-scale coronary artery datasets. The results demonstrate the effectiveness and efficiency of the proposed method, showing its potential use cases in the analysis of tree-structured image data

Poly(arylene ether nitrile) Composites with Surface-Hydroxylated Calcium Copper Titanate Particles for High-Temperature-Resistant Dielectric Applications

In order to develop high-performance dielectric materials, poly(arylene ether nitrile)-based composites were fabricated by employing surface-hydroxylated calcium copper titanate (CCTO) particles. The results indicated that the surface hydroxylation of CCTO effectively improved the interfacial compatibility between inorganic fillers and the polymer matrix. The composites exhibit not only high glass transition temperatures and an excellent thermal stability, but also excellent flexibility and good mechanical properties, with a tensile strength over 60 MPa. Furthermore, the composites possess enhanced permittivity, relatively low loss tangent, good permittivity-frequency stability and dielectric-temperature stability under 160 °C. Therefore, it furnishes an effective path to acquire high-temperature-resistant dielectric materials for various engineering applications

Hemodynamics analysis of the serial stenotic coronary arteries

Abstract Coronary arterial stenoses, particularly serial stenoses in a single branch, are responsible for complex hemodynamic properties of the coronary arterial trees, and the uncertain prognosis of invasive intervention. Critical information of the blood flow redistribution in the stenotic arterial segments is required for the adequate treatment planning. Therefore, in this study, an image based non-invasive functional assessment is performed to investigate the hemodynamic significances of serial stenoses. Twenty patient-specific coronary arterial trees with different combinations of stenoses were reconstructed from the computer tomography angiography for the evaluation of the hemodynamics. Our results showed that the computed FFR based on CTA images (FFRCT) pullback curves with wall shear stress (WSS) distribution could provide more effectively examine the physiological significance of the locations of the segmental narrowing and the curvature of the coronary arterial segments. The paper thus provides the diagnostic efficacy of FFRCT pullback curve for noninvasive quantification of the hemodynamics of stenotic coronary arteries with serial lesions, compared to the gold standard invasive FFR, to provide a reliable physiological assessment of significant amount of coronary artery stenosis. Further, we were also able to demonstrate the potential of carrying out virtual revascularization, to enable more precise PCI procedures and improve their outcomes