International Journal for Numerical Methods in Biomedical Engineering

Liu, Yingzhi; Qi, Fenfen; Cai, Xiao‐Chuan

2023 International Journal for Numerical Methods in Biomedical Engineering

doi: 10.1002/cnm.3771pmid: 37688432

In this paper, we develop an algorithm to simulate blood flows in aneurysmal arteries and focus on the construction of robust and efficient multilevel preconditioners to speed up the convergence of both linear and nonlinear solvers. The work is motivated by the observation that in the local aneurysmal region, the flow is often quite complicated with one or more vortices, but in the healthy section of the artery, the principal component of blood flows along the centerline of the artery. Based on this observation, we introduce a novel two‐level additive Schwarz method with a mixed‐dimensional coarse preconditioner. The key components of the preconditioner include (1) a three‐dimensional coarse preconditioner covering the aneurysm; (2) a one‐dimensional coarse preconditioner covering the central line of the healthy section of the artery; (3) a collection of three‐dimensional overlapping subdomain preconditioners covering the fine meshes of the entire artery; (4) extension/restriction operators constructed by radial basis functions. The blood flow is modeled by the unsteady incompressible Navier–Stokes equations with resistance outflow boundary conditions discretized by a stabilized finite element method on fully unstructured meshes and the second‐order backward differentiation formula in time. The resulting large nonlinear algebraic systems are solved by a Newton‐Krylov algorithm accelerated by the new preconditioner in two ways: (1) the initial guess of Newton is obtained by solving a linear system defined by the coarse preconditioner; (2) the Krylov solver of the Jacobian system is preconditioned by the new preconditioner. Numerical experiments indicate that the proposed preconditioner is highly effective and robust for complex flows in a patient‐specific artery with aneurysm, and it significantly reduces the numbers of linear and nonlinear iterations.

Chen, Zhendong; Peng, Bo; Zhou, Yuqi; Hao, Yinan; Xie, Xiaohua

2023 International Journal for Numerical Methods in Biomedical Engineering

doi: 10.1002/cnm.3775pmid: 37740645

Arterial pulse waveforms contain a wealth of information about the cardiovascular system. There is a lack of physical meaning in the mathematical model of arterial pulse waves, while the physical model fails to offer individuality as too many assumptions are involved. In this article, we focus on promoting the interpretability of the arterial pulse mathematical model. The proposed method is based on newly developed 3‐term fitting functions individualized by the physiological parameter assignment, which are the peak times of the reflected and dicrotic waves in a pulse. In this manner, the model allows decomposition of the pulse into sub‐signals with clear physiological significance. With nearly 10,000 pulse fitting experiments, it is demonstrated that the proposed method outperforms the standard methods in fitting accuracy while providing parameters linked to hemodynamic characteristics and common clinical indices such as the peripheral augmentation index (pAI). The proposed method innovatively maintains the individuality and accuracy of mathematical models while improving the interpretability of their parameters. The applications of this newly developed method, which explicitly incorporates hemodynamic characteristics, are expected to be particularly valuable in future pulse wave decomposition studies.

Denton, Alex N.; Umberger, Brian R.

2023 International Journal for Numerical Methods in Biomedical Engineering

doi: 10.1002/cnm.3777

Optimal control musculoskeletal simulation is a valuable approach for studying fundamental and clinical aspects of human movement. However, the high computational demand has long presented a substantial challenge, creating a need to improve simulation performance. The OpenSim Moco software package permits musculoskeletal simulation problems to be solved in parallel on multicore processors using the CasADi optimal control library, potentially reducing the computational demand. However, the computational performance of this framework has not been thoroughly examined. Thus, we aimed to investigate the computational speed‐up obtained via multicore parallel computing relative to solving problems serially (i.e., using a single core) in optimal control simulations of human movement in OpenSim Moco. Simulations were solved using up to 18 cores with a variety of temporal mesh interval densities and using two different initial guess strategies. We examined a range of musculoskeletal models and movements that included two‐ and three‐dimensional models, tracking and predictive simulations, and walking and reaching tasks. The maximum overall parallel speed‐up was problem specific and ranged from 1.7 to 7.7 times faster than serial, with most of the speed‐up achieved by about 6 processor cores. Parallel speed‐up was generally greater on finer temporal meshes, while the initial guess strategy had minimal impact on speed‐up. Considerable speed‐up can be achieved for some optimal control simulation problems in OpenSim Moco by leveraging the multicore processors often available in modern computers. However, since improvements are problem specific, achieving optimal computational performance will require some degree of exploration by the end user.

Johari, Nasrul H.; Menichini, Claudia; Hamady, Mohamad; Xu, Xiao Y.

2023 International Journal for Numerical Methods in Biomedical Engineering

doi: 10.1002/cnm.3772

Restenosis typically occurs in regions of low and oscillating wall shear stress, which also favor the accumulation of atherogenic macromolecules such as low‐density lipoprotein (LDL). This study aims to evaluate LDL transport and accumulation at the carotid artery bifurcation following carotid artery stenting (CAS) by means of computational simulation. The computational model consists of coupled blood flow and LDL transport, with the latter being modeled as a dilute substance dissolved in the blood and transported by the flow through a convection‐diffusion transport equation. The endothelial layer was assumed to be permeable to LDL, and the hydraulic conductivity of LDL was shear‐dependent. Anatomically realistic geometric models of the carotid bifurcation were built based on pre‐ and post‐stent computed tomography (CT) scans. The influence of stent design was investigated by virtually deploying two different types of stents (open‐ and closed‐cell stents) into the same carotid bifurcation model. Predicted LDL concentrations were compared between the post‐stent carotid models and the relatively normal contralateral model reconstructed from patient‐specific CT images. Our results show elevated LDL concentration in the distal section of the stent in all post‐stent models, where LDL concentration is 20 times higher than that in the contralateral carotid. Compared with the open‐cell stents, the closed‐cell stents have larger areas exposed to high LDL concentration, suggesting an increased risk of stent restenosis. This computational approach is readily applicable to multiple patient studies and, once fully validated against follow‐up data, it can help elucidate the role of stent strut design in the development of in‐stent restenosis after CAS.

Kuznetsov, Ivan A.; Kuznetsov, Andrey V.

2023 International Journal for Numerical Methods in Biomedical Engineering

doi: 10.1002/cnm.3770pmid: 37688421

Recent publications report that although the mitochondria population in an axon can be quickly replaced by a combination of retrograde and anterograde axonal transport (often within less than 24 hours), the axon contains much older mitochondria. This suggests that not all mitochondria that reach the soma are degraded and that some are recirculating back into the axon. To explain this, we developed a model that simulates mitochondria distribution when a portion of mitochondria that return to the soma are redirected back to the axon rather than being destroyed in somatic lysosomes. Utilizing the developed model, we studied how the percentage of returning mitochondria affects the mean age and age density distributions of mitochondria at different distances from the soma. We also investigated whether turning off the mitochondrial anchoring switch can reduce the mean age of mitochondria. For this purpose, we studied the effect of reducing the value of a parameter that characterizes the probability of mitochondria transition to the stationary (anchored) state. The reduction in mitochondria mean age observed when the anchoring probability is reduced suggests that some injured neurons may be saved if the percentage of stationary mitochondria is decreased. The replacement of possibly damaged stationary mitochondria with newly synthesized ones may restore the energy supply in an injured axon. We also performed a sensitivity study of the mean age of stationary mitochondria to the parameter that determines what portion of mitochondria re‐enter the axon and the parameter that determines the probability of mitochondria transition to the stationary state. The sensitivity of the mean age of stationary mitochondria to the mitochondria stopping probability increases linearly with the number of compartments in the axon. High stopping probability in long axons can significantly increase mitochondrial age.

Liang, Fuyou; Qin, Kairong; Wang, Lizhen

2023 International Journal for Numerical Methods in Biomedical Engineering

doi: 10.1002/cnm.3774pmid: 37749822

Fernandes, Joshua; Kang, Sangmo

2023 International Journal for Numerical Methods in Biomedical Engineering

doi: 10.1002/cnm.3773

We present a numerical investigation of the photothermal response of gold nanoshell (AuNS) dimers when subjected to femtosecond laser pulse irradiation. The time‐varying temperature fields for core–shell AuNS dimers are quantified by implementing finite element modeling, integrating the electromagnetic and thermal dual‐physics simulations. Given the ultrafast nature of laser pulses, we employ a two‐temperature model to accurately portray the energy transfer from excited electrons to the lattice system, a process typically completed post pulse‐termination. The temporal analysis of the temperature in the AuNS and the surrounding medium, together with the spatial temperature distribution under different separation distances, elucidates the processes that drive the AuNS dimers' transient temperature distribution and heat dissipation. We report on the critical effects of geometrical parameters on the photothermal response, demonstrating that thinner shells maximize the total deposited energy per unit volume, resulting in increased temperature fields, while decreasing separation distances result in excessive field amplification due to plasmonic modes' production. Our robust numerical approach, enabling simulations with tunable material properties and configurations, may help design nanomaterials with desired features for photothermal cancer treatment and imaging.

Liu, Ying‐Bin; Qin, Jian‐Hua; Zeng, Gui‐Fen

2023 International Journal for Numerical Methods in Biomedical Engineering

doi: 10.1002/cnm.3776

Acupuncture points have a positive effect on the auxiliary prevention and treatment of diseases, so medical devices such as acupuncture robots often need to combine acupuncture points to improve the treatment effect when working, however, intelligent acupoint selection technology is not yet mature, the automatic rapid and accurate positioning of acupoints is still challenging. Therefore, this paper proposes a method of back acupoint location and an evaluation index of acupoint location. First, we propose an improved Keypoint RCNN network for the preliminary location of back acupoints and introduce a channel and spatial attention mechanism module (CBAM) to improve the performance of the model. Then, we set up a posterior median line positioning method to improve the accuracy of acupoint positioning. Finally, expand and locate other acupoints according to the prior information of acupoints. According to the experimental results, the accuracy of acupoint positioning was 87.32%. After the correction of acupoint positioning, the accuracy was increased by 2.8%, which was 90.12%. In this paper, the application of depth learning in automatic location of back acupoints is realized for the first time. Only one image can be used to locate the back acupoints, with an accuracy of 90.12%.

Golla, Sandhya Kumari; Maloji, Suman

2023 International Journal for Numerical Methods in Biomedical Engineering

doi: 10.1002/cnm.3769pmid: 37740655

Professional medical experts use a visual electroencephalography (EEG) signal for epileptic seizure detection, although this method is time‐consuming and highly subject to bias. The majority of previous epileptic detection techniques have poor efficiency, detection performance and also which are unsuited to handle large datasets. In order to solve the aforementioned issues and to assist medical professionals with an advanced technology, a computerized epileptic seizure detection system is essential. Therefore, the proposed work intends to design an automated detection tool for predicting an epileptic seizure from EEG signals. For this purpose, a novel non‐linear feature analysis and deep learning algorithms are deployed in this work. Initially, the signal decomposition, filtering and artifacts removal operations are carried out with the use of finite Haar wavelet transformation technique. After that, the finite spectral entropy (FSE) based feature extraction model has been used to extract the time, frequency, and time‐frequency features from the normalized signal. Consequently, the novel gated term memory unit recursive network (GTRN) model is employed to predict the given EEG signal as whether healthy or seizure affected including the class with high accuracy. During this process, the recently developed Ladybug Beetle Optimization (LBO) algorithm is used to compute the logistic sigmoid function based on the solution. The purpose of using this algorithm is to simplify the process of classification with increased seizure prediction accuracy and performance. Moreover, the standard and popular benchmark EEG datasets are used to validate and test the results of the proposed FSE‐GTRN‐LBO mechanism. By leveraging the finite Haar wavelet transformation and FSE‐based feature extraction, we can efficiently process EEG signals. The utilization of the GTRN model enables accurate classification of healthy and seizure‐affected EEG data. To optimize the classification process further, we integrate the LBO algorithm, streamlining the computation of the logistic sigmoid function. Through comprehensive validation on standard EEG datasets, our proposed FSE‐GTRN‐LBO mechanism achieves outstanding seizure prediction accuracy and performance, surpassing existing state‐of‐the‐art techniques.