International Journal for Numerical Methods in Biomedical Engineering最新文献

To accurately simulate all phases of the cardiac cycle, computational models of hemodynamics in heart chambers need to include a sufficiently faithful model of cardiac valves. This can be achieved efficiently through resistive methods, and the resistive immersed implicit surface (RIIS) model in particular. However, the conventional RIIS model is not suited to fluid–structure interaction (FSI) simulations, since it neglects the reaction forces by which valves are attached to the cardiac walls, leading to models that are not consistent with Newton's laws. In this paper, we propose an improvement to RIIS to overcome this limitation, by adding distributed forces acting on the structure to model the attachment of valves to the cardiac walls. The modification has a minimal computational overhead thanks to an explicit numerical discretization scheme. Numerical experiments in both idealized and realistic settings demonstrate the effectiveness of the proposed modification in ensuring the physical consistency of the model, thus allowing us to apply RIIS and other resistive valve models in the context of FSI simulations.

During a large vessel occlusion, the survivability of the affected brain tissue depends on the ability of blood to reach the compromised territory. Consequently, the severity of ischemic strokes and the outcome of interventional treatments like thrombectomy are strongly influenced by individual anatomical features of the brain's vascular network, particularly its collateralization. However, analyzing the role of collateral circulation has proven particularly challenging, as it requires highly detailed models of arterial networks that include very small collateral vessels (~50 μm diameter). This article presents a computational framework for constructing realistic brain vascular models that capture the anatomical variability of both the circle of Willis and the pial collateral network. The methodology integrates image-based vascular reconstruction, arterial tree extension via constrained constructive optimization, and generation of leptomeningeal collateral vessels. Blood flow simulations are performed using lumped parameter models, while virtual angiograms are generated through distributed compartment modeling of transport. A virtual patient population with variable collateralization is used to study the impact of anatomical differences on collateral flow and angiographic signatures in the presence of large vessel occlusions. The results show good agreement with in vivo data and highlight features that could help infer the level of collateralization from clinical angiograms. This framework offers a foundation for improving patient-specific stroke treatment planning and understanding the hemodynamic implications of vascular variability.

Recently, researchers have explored the wall shear stress (WSS) obtained from medical images and computational fluid dynamics (CFD) to provide medical support. However, low-frequency noise caused by the resolution of the medical images increases the surface roughness of the geometry, thereby reducing the calculation accuracy of WSS. To reduce the surface roughness, regular smoothing methods are applied to geometries obtained from low-resolution medical images; however volume changes are a problem. In this study, we developed a method to obtain geometries with reduced surface roughness and minimal volume changes from medical images used in checkups, which have low resolution. Our approach combines interpolation of coordinate points with selective removal of low-frequency noise. This method was applied to 12 carotid artery geometries and one cerebral artery geometry obtained from medical images during the medical checkups; the changes in surface roughness, volume, and WSS in the CFD were compared with before and after smoothing. As a result, we found that the surface roughness of the carotid artery geometries after applying the developed method was approximately 27%–32% smaller than the original geometries, with the volume change remaining minimal, approximately a few percent. The WSS in CFD was found to be approximately 4.2% lower than that of the original geometries. These results demonstrate that our approach improves CFD accuracy for carotid and cerebral arteries, making it useful for medical support based on low-resolution medical images.

Residual thoracic aortic dissection (RTAD) is a pathology whose patient-dependent evolution is an important clinical issue, and for which numerical fluid–structure interaction (FSI) models can be helpful. However, the proposal of an ad hoc mechanical wall and flap model remains a challenge. To make progress on this issue, we seek to understand the respective influence of the flap and the wall in interaction with the flow in RTAD. Based on a patient's RTAD geometry, we established different FSI models to simulate the mechanical behaviour of the wall and flap. We varied the Young's modulus of the wall between 1.2 MPa (W12) and 2.7 MPa (W27) and the Young's modulus of the flap between 0.6 MPa (F06) and 1.2 MPa (F12), resulting in 3 different cases to study (F06W12, F12W12 and F06W27) and allowing a relative comparison. Structural displacements and stresses are equivalent in F06W12 and F12W12, resulting in equivalent flow characteristics. When comparing F06W12 with F06W27, we show that a stiffer wall reduces flap motion by 49.8%, 51% and 52% respectively, around the first entry tear, second and third one respectively. The difference in flow pressure between channels, which reflects the resistance to flow, is very small (about 1–2 mmHg) and similar for all 3 cases. This result seems to be highly related to the current geometry with one entry and two re-entry tears. Our results show that the wall is the main driver of the overall mechanical behaviour of the RTAD. We demonstrated that a stiffer pathological wall leads to smaller flap displacements, which is consistent with clinical observations in the chronic phase.

Red blood cells (RBCs) undergo large structural deformation, including bending, when passing through capillaries. They also exhibit a range of complex shapes such as stomatocytes, discocytes and echinocytes that form due to altered blood pH and salt levels, ingested drugs and adenosine triphosphate depletion. Discrete-spring-network structural models of RBCs employ different numerical treatments of the continuum bending energy. This affects bending accuracy and the prediction of accurate RBC shapes. This research compares three representations called bending energy scheme (BES) A, B and C to evaluate their accuracy in shape predictions. BES A, seen throughout the literature, is based on the formulations of Kantor and Nelson, while BES B and BES C are, respectively, spring-based and node-based curvature calculation methods based on the formulations of Jülicher. Flat and enclosed spring-network membrane test cases are presented, and predictions using the schemes are compared. The flat membrane test cases explored the bending of stiff and soft membranes while the enclosed membrane test cases evaluated equilibrium vesicle and RBC shape prediction, including predictions of the stomatocyte-to-discocyte-to-echinocyte sequence. Predictions showed that BES A and BES B have limitations and can underestimate the true bending deformation. Additionally, BES A and BES B are also unable to capture the necking behaviour critical to the accurate prediction of complex RBC shapes. BES C on the other hand was seen to be accurate and robust and predicted shapes closely matched expected biological shapes. Based on this research, BES C is recommended for all future spring-network RBC structural modelling.

Recently, the concept of a virtual population (Vpop) has attracted attention to provide large-scale, diverse datasets without compromising individual privacy. The development of the Vpop modelling method for the cerebrovasculature shape is necessary to be established with simple parameter tuning and post-processing. This study introduces a multivariate normal distribution (MVND) method to generate a Vpop for the cerebrovasculature shape. We defined an MVND by using the position and inner radius, which represent the vascular shape (centerline), as variables. Patient-specific arteries (basilar artery and internal carotid artery) obtained from MR images were used as a real population (Rpop) to generate an MVND. Then, virtual arteries were sampled from this MVND to generate a Vpop. To evaluate the validity of this method for reproducing shape diversity, we calculated the geometrical features of the centerline in each population. The centerline shows qualitatively similar characteristics between Vpop and Rpop. Geometrical features such as average length calculated from Vpop are in the same range as those of Rpop. Moreover, the distribution of geometrical features exhibits a good degree of fit between Vpop and Rpop. Since MVND considers the correlation among all position and inner radius variables, centerline continuity and anatomical characteristics of cerebrovasculature can be automatically included. Hence, geometric features and their distribution can be reproduced without any parameter tuning. The consistency in geometric parameters between the two populations supports the validity of the MVND method and indicates the potential for generating a Vpop for the cerebrovasculature in a more straightforward and simplified manner.