An experimental and numerical investigation of the blood flow across the stenosis embedded in the elastic artery had been carried out within a framework of a project aimed at the development of the decision support system for the diagnostics of stable coronary artery disease, using a novel hybrid physics-informed machine learning (ML) and computational fluid dynamics (CFD) approach. Integration of CFD equations into a ML framework requires the development and validation of a CFD model optimized for this specific task. The values of empirical coefficients required for the implementation of the CFD component of the project were obtained by collecting experimental data on a pressure drop in elastic arterial models with stenoses in the physiologically relevant range of flow conditions, and the results were used for the validation of the numerical solver. Analysis of the experimental data also demonstrated a strong impact of the vessels' elasticity on the pressure drop across a stenosis, as well as a substantial role of the time-dependent (pulsative) flow parameters. It has been shown that fine-tuning of the values of the viscous and turbulent resistance coefficients to account for the elasticity of the vessels surrounding the stenosis can substantially improve the accuracy of the pressure drop prediction in the intermediate lesions, specifically relevant for clinical applications.

Despite being a common procedure, abdominal hernia treatments still require improvement due to the number of relapses and other postoperative issues. In silico testing can be employed to predict the behavior of the complex abdominal wall and implant systems. Here, uncertainty quantification and sensitivity analysis are required in order to optimize the parameters of hernia repair models. This paper concerns the modeling of an abdominal wall and implant using the finite element method. A Gasser-Ogden-Holzapfel (GOH) material model is used for the abdominal wall and an orthotropic material model for the implant. The parameters of the GOH model and the orientation of the implant are assumed to be uncertain. Regression-based polynomial chaos expansion is used as a meta-modeling method for uncertainty propagation and global sensitivity analysis. The maximum force in the connection between the implant and native tissue is considered as the quantity of interest. A failure risk criterion is also defined and presented. It has been found that the significance of the material parameters depends on the type of implant that is analyzed. Likewise, the risk of connection failure varies considerably depending on the implant used. Models with different types of implant produce very diverse results. Moreover, these differences also appear in the global sensitivity index and the risk of connection failure. This would indicate that specific implant designs and material properties are crucial to the success of hernia repair surgery.

ABSTRACTHeat‐based cancer treatment, so‐called hyperthermia, can be used to destroy tumour cells directly or to make them more susceptible to chemotherapy or radiation therapy. To apply heat locally, iron oxide nanoparticles are injected into the bloodstream and accumulate at the tumour site, where they generate heat when exposed to an alternating magnetic field. However, the temperature must be precisely controlled to achieve therapeutic benefits while avoiding damage to healthy tissue. We therefore present a computational model for nanoparticle‐mediated hyperthermia treatment fully integrated into a multiphase porous‐media model of the tumour and its microenvironment. We study how the temperature depends on the amount of nanoparticles accumulated in the tumour area and the specific absorption rate of the nanoparticles. Our results show that host tissue surrounding the tumour is also exposed to considerable doses of heat due to the high thermal conductivity of the tissue, which may cause pain or even unnecessary irreversible damage. Further, we include a lumped and a discrete model for the cooling effect of blood perfusion. Using a discrete model of a realistic microvasculature reveals that the small capillaries do not have a significant cooling effect during hyperthermia treatment and that the commonly used lumped model based on Pennes' bioheat equation may overestimate the effect: within the specific conditions analysed, the difference between lumped and discrete approaches is approximatively 0.75°C, which could influence the therapeutic intervention outcome. Such a comprehensive computational model, as presented here, can provide insights into the optimal treatment parameters for nanoparticle‐mediated hyperthermia and can be used to design more efficient treatment strategies.

The purpose of this study was to propose a multiobjective shape optimization approach using the MOPSO algorithm for the femoral stems with the aim of increasing the long-term survivorship of hip implants. The Taperloc Complete femoral stem was selected and its reference geometry was defined with 67 variables. 10 new stem shapes were produced as the swarm members by randomly changing the values of the variables. The values of the stress shielding, initial relative micro-motion, and bone-implant interface stress for each stem shape were calculated as the objectives by the finite element analysis and the position of each swarm member was updated iteratively. The geometry that caused a 37% and 45% decrease in the interface stress and stress shielding, respectively, and a 65% increase in the initial micro-motion compared to the Taperloc Complete stem was selected as the optimized shape. It was shown that thinning the femoral stems without changing their length reduced the induced stress shielding and initial micro-motion and increased the interface stress, whereas shortening the femoral stems reduced the stress shielding and interface stress and increased the initial micro-motion. The proposed approach may be used for the shape optimization of commercial femoral stems to increase their lifetime.

Magneto-acousto-electrical tomography with magnetic field measurement technique (MAET-MI) is a hybrid imaging method that brings high spatial resolution of ultrasound imaging in electrical impedance tomography. This study investigates the impact of the quality factor of circular and figure-of-eight coils on the imaging performance of MAET-MI. Induced MAET signals on the circular coil are accurately obtained by modeling a circuit representation of an air-cored circular coil and deriving its transfer function through impedance measurements. The study demonstrates a significant improvement in signal-to-noise ratio (SNR) using high-quality factor coils compared to unity quality factor coils. Additionally, a 16-element linear phased array (LPA) ultrasound transducer, an air core circular coil, and a figure-of-eight coil are numerically modeled to obtain sector scan images of two-dimensional conductivity distributions. Point spread function (PSF) is characterized, and the lateral resolution of sector scan conductivity images is enhanced through two-dimensional deconvolution with PSF. The combined use of circular and figure-of-eight coils provides comprehensive imaging coverage. Notably, this research presents a practical method for estimating both circular and figure-of-eight coils' transfer functions, achieving 12.9 dB SNR improvement with high-quality factor coils. A simplified breast model is rotated 16 steps, and sector scan conductive boundary images are reconstructed for both coils. A two-dimensional image of a breast model is obtained by combining images from two different coils. These findings offer significant advancements in MAET-MI imaging, particularly in low SNR environments.

While the need for employing subject-specific computational biomechanics models for treatment planning in orthopaedics is being increasingly voiced, it has not been clear when such specificity is essential and for which questions simpler models might be adequate. This study uses a novel modelling approach to generate finite element models to examine the influence of subject-specificity in the treatment of distal femur fractures. Three subject-specific finite element models are created from clinical CT scans, and the proposed approach is employed to impose identical fractures and locking plate treatments upon them. Additionally, the performance of the generic two-material model based on a Sawbones fourth generation femur is also evaluated. Interfragmentary motions, plate stresses, and strains at the screw-bone interface are examined due to a physiological loading at different stages of healing. The study finds that subject-specificity has a major effect on strains in the bone at the screw-bone interface. However, interfragmentary motions at the far cortex and plate stresses show minimal sensitivity to subject-specific factors, while near-cortical and shear interfragmentary motions are influenced by them. The influence of subject-specificity decreases as healing progresses. These results indicate that while generic approaches may be sufficient to calculate global assembly responses, material heterogeneity and subject-specific bone stock variations have a large impact on the interaction between the screws and bone. The study also shows that the proposed method, which enables manipulating bone geometry while retaining subject-specific properties, can be used to evaluate the influence of subject-specificity for other orthopaedic simulations.

Microfluidic devices (MDs) present a novel method for detecting circulating tumor cells (CTCs), enhancing the process through targeted techniques and visual inspection. However, current approaches often yield heterogeneous CTC populations, necessitating additional processing for comprehensive analysis and phenotype identification. These procedures are often expensive, time-consuming, and need to be performed by skilled technicians. In this study, we investigate the potential of a cost-effective and efficient hyperuniform micropost MD approach for CTC classification. Our approach combines mathematical modeling of fluid-structure interactions in a simulated microfluidic channel with machine learning techniques. Specifically, we developed a cell-based modeling framework to assess CTC dynamics in erythrocyte-laden plasma flow, generating a large dataset of CTC trajectories that account for two distinct CTC phenotypes. Convolutional neural network (CNN) and recurrent neural network (RNN) were then employed to analyze the dataset and classify these phenotypes. The results demonstrate the potential effectiveness of the hyperuniform micropost MD design and analysis approach in distinguishing between different CTC phenotypes based on cell trajectory, offering a promising avenue for early cancer detection.

Liquid biopsy, particularly the isolation of circulating tumor cells (CTCs) from blood, is a promising approach in the fight against cancer. However, the main reason why CTCs are hardly used as biomarkers in the clinic is their complicated isolation from the patient's blood. Existing exvivo systems use a small volume of blood and can therefore only isolate very few CTCs. To overcome this problem and increase the number of isolated CTCs, a new invivo method-the BMProbe was introduced, which can isolate CTCs directly from the patient's bloodstream. This study investigates the efficiency of the BMProbe by using Computational Fluid Dynamics simulations to evaluate parameters influencing the attachment probability of CTCs to the probe surface. The analyzed parameters include screened blood volume, residence time, and wall normal rate. Additionally, the impact of probe geometry, vein diameter, and blood flow velocity on probe efficiency was examined. The numerical data suggest that the geometry has a strong influence on cell binding efficiency. Increasing the number of windings from 4 to 32 improves the transport of cells to the surface (negative wall normal rate) from 0 to -29 [mm2/s] and the screened blood volume by 138% but decreases the residence time of particles in the close vicinity of the probe by 77%. When compared to experimental data, the screened blood volume and the wall normal rate indicate cell attachment very well, whereas the residence time does not show a significant impact on the attachment of cells. For the 32-windings BMProbe, the screened blood volume is determined to be 130-313 mL, depending on the vein diameter, which is a multiple of the volume achieved by common CTC isolation techniques.

Hybrid repair is a valuable alternative treatment for aortic arch disease in Marfan syndrome patients after proximal aorta replacement. This study aimed to investigate the long-term durability of this technique with the use of parallel stent-grafts and evaluate strategies to prevent abdominal aortic dilation. One Marfan syndrome patient who underwent hybrid aortic repair with parallel stent-grafts for arch dissection after the Bentall procedure was admitted. Five patient-specific three-dimensional models were reconstructed based on preoperative and follow-up computed tomography angiography scans. Three hypothetical models addressing the closure of an endoleak or reentry tears were created. Hemodynamic parameters were assessed using computational fluid dynamics. Postoperatively, increased blood flow into the descending aorta and rising abdominal aortic pressure were observed. During the 5-year follow-up, no new thoracic aorta-related adverse events occurred. One early type III endoleak persisted, and three reentry tears were identified in the descending aorta. The abdominal aorta dilated from 31 to 49 mm. Simultaneously addressing both the endoleak and reentry tears was more effective in reducing false lumen pressure and flow velocity in the abdominal aorta and expanding the high-value relative residence time region. Longitudinal follow-up imaging demonstrated the long-term durability of hybrid aortic arch repair with parallel stent-grafts in a Marfan syndrome patient after ascending aorta replacement. The increased pressure resulting from blood flow redistribution was associated with downstream aortic dilation. Furthermore, computational fluid dynamics simulations can offer predictive analyses for optimizing intervention strategies in the treatment of distal aneurysmal degeneration.