International Journal for Numerical Methods in Biomedical Engineering最新文献

This study proposes an innovative method for generating patient-specific 3D models of the facial soft tissue, which can be integrated into clinical routine to predict the consequences of orthognathic surgery. This surgery, which impacts both facial aesthetics and functionality, requires precise tools tailored to each patient. To this end, a 3D face model has been developed, integrating anatomically precise bone structures (mandible and maxilla) and soft tissues (skin, fat, and muscles). This reference model is then fitted to each patient's anatomy using a nearly automatic method. This process requires only a manual selection of 34 landmarks to be performed in the clinical setting. The remainder of the patient-specific 3D model generation is fully automated from the patient's CT imaging data. Finally, a proof of concept is presented, featuring Finite Element simulations performed with Ansys APDL software, including orthognathic surgery and then muscle contractions applied to a patient-specific 3D model generated by the nearly automatic method.

Abdominal aortic aneurysm (AAA) is a life-threatening condition characterized by the progressive dilation of the aorta, which can lead to rupture if undetected or untreated. Stress-based rupture risk estimation using computational biomechanics has been widely studied; however, it requires wall strength data that cannot be measured in humans in vivo. To overcome this limitation, the goal of this study is to present a new method for biomechanical assessment of AAA via simultaneous consideration of tension and strain in AAA wall. We present a patient-specific, non-invasive method for assessing the structural integrity of the AAA wall using only time-resolved 3D computed tomography angiography (4D-CTA) images and blood pressure data. The proposed approach integrates wall strain (throughout the cardiac cycle) and wall tension analysis to compute a novel index, the Relative Structural Integrity Index (RSII), which quantifies local wall stiffness independently of wall thickness, wall material properties, and blood pressure measurement conditions. We applied our method to 20 patients from three different hospitals to extract visual RSII maps over the AAA wall of each individual patient and to compare the RSII values between aneurysmal and non-aneurysmal aortas in one patient. Our results primarily show similar RSII values across all patients, indicating the consistency of the method. Additionally, we observed patterns consistent with experimental findings reported in the literature: AAA walls exhibited higher stiffness than healthy aortic walls, while localized low-stiffness zones in the AAA wall were predominantly found in the most dilated regions.

Existing lumped arterial stenosis models struggle to accurately capture the pressure and flow relationship of complex lesion morphologies, thereby limiting their ability to accurately evaluate lesions. To overcome these limitations, we introduce a geometry-informed, data-driven lumped stenosis model that incorporates realistic lesion shapes using statistical shape modeling (SSM). By generating a large dataset of synthetic coronary stenoses, hence focusing on epicardial lesions, and evaluating them through high-fidelity 3D computational fluid dynamics (CFD), we derived reference pressure drops across a diverse range of lesion geometries and flow regimes. These CFD-derived pressures and flows, along with their corresponding shape coefficients, were used to train a lumped parameter model capable of rapidly estimating trans-lesional pressure drops. Remarkably, only five shape modes were necessary to effectively describe the geometric variability, underscoring the efficiency of the approach. Compared to a conventional lumped model, our approach significantly improved pressure drop prediction accuracy, especially in the case of irregular stenosis morphologies. Integration of the new data-driven lumped stenosis model within a 1D pulse wave propagation framework was also successful, aligning simulated pressure and flow waveforms much closer with high-fidelity CFD-coupled results. In turn, the estimation of the fractional flow reserve, a clinically validated index of lesion-specific ischemia, also improved by 18% compared to a conventional lumped model. Although only validated using synthetic lesion data, the model's architecture allows easy integration of additional shape features and lesion-specific parameters, paving the way for future validation on patient-derived geometries.

As a kind of the flexible needle, a cannula flexible needle consists of a flexible cannula and a flexible stylet, which can achieve variable-curvature paths through changing the stylet extension lengths out of the cannula, and has the advantages in the path correction and the insertion flexibility compared to the traditional flexible needles. The current path planning is mainly based on the over-idealized wheel-based kinematic models. However, these kinematic models cannot accurately express the bending of the flexible needle and cause the major insertion errors. In this article, a bending model was proposed for predicting the large bending of the cannula flexible needle based on the large bending theory. The relationship between the different stylet extension lengths and the discretization lengths of the mechanical bending model was formulated through the experimental data, and then a function family was established to depict the needle bending relative to the stylet extension length. Based on these, a path planning algorithm for the cannula flexible needle was proposed based on the improved Rapidly-exploring Random Trees in the environments with obstacles. The comparisons between the proposed algorithm and the path planning algorithm based on the wheel-based model were conducted by simulations. The simulation results showed that the proposed path planning algorithm was overall superior to the others not only in terms of the searching speed but also the total insertion length. Then, a series of insertion experiments and the error analysis were performed for the two planned paths. The results were with the mean errors of 0.77 and 0.91 mm, the root mean square errors of 0.85 and 1.13 mm, and the maximum errors of 1.2 and 1.5 mm, for the path-1 and the path-2, respectively. The experimental results met the clinical requirements for the ordinary surgery which proved the effectiveness and the accuracy of the proposed path planning algorithm.

Subject-specific musculoskeletal (MSK) models with individualized anthropometric features are essential for accurate biomechanical analysis, given the considerable anatomical and mechanical variability across individuals. Marker-based scaling provides a noninvasive, efficient, and cost-effective strategy for personalizing generic MSK models, yet its reliability and accuracy remain insufficiently validated. This study proposes a hybrid scaling approach (FlexiScale), which combines segment-wise linear scaling with global nonlinear morphing based on radial basis function (RBF) interpolation. This method enables simultaneous adjustment of relative segment orientations and overall skeletal geometry. To comprehensively evaluate its performance, two simpler baseline models were also constructed, including a uniform linear scaling model and a segmental linear scaling model. A reliability analysis was conducted by comparing knee joint contact forces predicted by the three scaling models with in vivo measurements obtained from an instrumented knee prosthesis under identical gait conditions. Furthermore, an accuracy validation was performed by comparing joint contact forces and muscle forces predicted by each scaling model against those derived from medical image-based subject-specific models across three daily activities (level walking, stair ascent, and stair descent) in both male and female subjects. Compared to conventional linear methods, FlexiScale consistently produced the most accurate and reliable geometric and biomechanical predictions across tasks and subjects. These findings demonstrate that the proposed hybrid approach can generate geometrically and biomechanically more accurate and robust MSK models than conventional linear scaling methods, even without medical imaging, thereby supporting subject-specific assessments and large-scale applications in clinical and research settings.

Fontan-associated liver disease (FALD) is a disorder arising from hemodynamic changes and venous congestion in the liver. This disease is prominent in patients with hypoplastic left heart syndrome (HLHS). Although HLHS patients typically survive into adulthood, they have reduced cardiac output due to their univentricular physiology (i.e., a Fontan circuit). As a result, they have insufficient blood delivery to the liver. In comparison, patients with double outlet right ventricle (DORV), also having a univentricular circuit, have a lower incidence of FALD. In this study, we use a patient-specific, one-dimensional computational fluid dynamics (1D-CFD) model to predict hemodynamics in the liver of an HLHS patient and compare the predictions with an age- and size-matched single-ventricle Fontan DORV control patient. Additionally, we simulate FALD conditions in the HLHS patient to predict hemodynamic changes across various stages of disease progression. Our results show that the HLHS patient has higher hepatic arterial pressure compared to the DORV patient. This difference is exacerbated as FALD conditions progress. HLHS patients also have higher average portal pressures than DORV patients. The wall shear stress (WSS) is higher in the hepatic network for the simulated FALD patients. WSS is slightly decreased in the portal network for the HLHS patients, consistent with the development of portal hypertension. Perfusion analysis gives insight into regions of liver tissue at risk for fibrosis development, showing increasing pressures and reduced flow throughout the liver tissue fed by the portal vein under FALD conditions. Our results provide insight into the specific hemodynamic changes in Fontan circulation that can cause FALD.