AnXplore: a comprehensive fluid-structure interaction study of 101 intracranial aneurysms.

Advances in computational fluid dynamics continuously extend the comprehension of aneurysm growth and rupture, intending to assist physicians in devising effective treatment strategies. While most studies have first modelled intracranial aneurysm walls as fully rigid with a focus on understanding blood flow characteristics, some researchers further introduced Fluid-Structure Interaction (FSI) and reported notable haemodynamic alterations for a few aneurysm cases when considering wall compliance. In this work, we explore further this research direction by studying 101 intracranial sidewall aneurysms, emphasizing the differences between rigid and deformable-wall simulations. The proposed dataset along with simulation parameters are shared for the sake of reproducibility. A wide range of haemodynamic patterns has been statistically analyzed with a particular focus on the impact of the wall modelling choice. Notable deviations in flow characteristics and commonly employed risk indicators are reported, particularly with near-dome blood recirculations being significantly impacted by the pulsating dynamics of the walls. This leads to substantial fluctuations in the sac-averaged oscillatory shear index, ranging from -36% to +674% of the standard rigid-wall value. Going a step further, haemodynamics obtained when simulating a flow-diverter stent modelled in conjunction with FSI are showcased for the first time, revealing a 73% increase in systolic sac-average velocity for the compliant-wall setting compared to its rigid counterpart. This last finding demonstrates the decisive impact that FSI modelling can have in predicting treatment outcomes.

In vitro platform to model the function of ionocytes in the human airway epithelium.

BACKGROUND: Pulmonary ionocytes have been identified in the airway epithelium as a small population of ion transporting cells expressing high levels of CFTR (cystic fibrosis transmembrane conductance regulator), the gene mutated in cystic fibrosis. By providing an infinite source of airway epithelial cells (AECs), the use of human induced pluripotent stem cells (hiPSCs) could overcome some challenges of studying ionocytes. However, the production of AEC epithelia containing ionocytes from hiPSCs has proven difficult. Here, we present a platform to produce hiPSC-derived AECs (hiPSC-AECs) including ionocytes and investigate their role in the airway epithelium. METHODS: hiPSCs were differentiated into lung progenitors, which were expanded as 3D organoids and matured by air-liquid interface culture as polarised hiPSC-AEC epithelia. Using CRISPR/Cas9 technology, we generated a hiPSCs knockout (KO) for FOXI1, a transcription factor that is essential for ionocyte specification. Differences between FOXI1 KO hiPSC-AECs and their wild-type (WT) isogenic controls were investigated by assessing gene and protein expression, epithelial composition, cilia coverage and motility, pH and transepithelial barrier properties. RESULTS: Mature hiPSC-AEC epithelia contained basal cells, secretory cells, ciliated cells with motile cilia, pulmonary neuroendocrine cells (PNECs) and ionocytes. There was no difference between FOXI1 WT and KO hiPSCs in terms of their capacity to differentiate into airway progenitors. However, FOXI1 KO led to mature hiPSC-AEC epithelia without ionocytes with reduced capacity to produce ciliated cells. CONCLUSION: Our results suggest that ionocytes could have role beyond transepithelial ion transport by regulating epithelial properties and homeostasis in the airway epithelium.

Analysis of Intracranial Aneurysm Haemodynamics Altered by Wall Movement.

Computational fluid dynamics is intensively used to deepen our understanding of aneurysm growth and rupture in an attempt to support physicians during therapy planning. Numerous studies assumed fully rigid vessel walls in their simulations, whose sole haemodynamics may fail to provide a satisfactory criterion for rupture risk assessment. Moreover, direct in vivo observations of intracranial aneurysm pulsation were recently reported, encouraging the development of fluid-structure interaction for their modelling and for new assessments. In this work, we describe a new fluid-structure interaction functional setting for the careful evaluation of different aneurysm shapes. The configurations consist of three real aneurysm domes positioned on a toroidal channel. All geometric features, employed meshes, flow quantities, comparisons with the rigid wall model and corresponding plots are provided for the sake of reproducibility. The results emphasise the alteration of flow patterns and haemodynamic descriptors when wall deformations were taken into account compared with a standard rigid wall approach, thereby underlining the impact of fluid-structure interaction modelling.

Maximum likelihood-based extended Kalman filter for soft tissue modelling.

Realistic modelling of human soft tissue is very important in medical applications. This paper proposes a novel method by dynamically incorporating soft tissue characterisation in the process of soft tissue modelling to increase the modelling fidelity. This method defines nonlinear tissue deformation with unknown mechanical properties as a problem of nonlinear filtering identification to dynamically identify mechanical properties and further estimate nonlinear deformation behaviour of soft tissue. It combines maximum likelihood theory, nonlinear filtering and nonlinear finite element method (NFEM) for modelling of nonlinear tissue deformation behaviour based on dynamic identification of homogeneous tissue properties. On the basis of hyperelasticity, a nonlinear state-space equation is established by discretizing tissue deformation through NFEM for dynamic filtering. A maximum likelihood algorithm is also established to dynamically identify tissue mechanical properties during the deformation process. Upon above, a maximum likelihood-based extended Kalman filter is further developed for dynamically estimating tissue nonlinear deformation based on dynamic identification of tissue mechanical properties. Simulation and experimental analyses reveal that the proposed method not only overcomes the NFEM limitation of expensive computations, but also absorbs the NFEM merit of high accuracy for modelling of homogeneous tissue deformation. Further, the proposed method also effectively identifies tissue mechanical properties during the deformation modelling process.

Modelling the Collective Mechanical Regulation of the Structure and Morphology of Epithelial Cell Layers.

The morphology and function of epithelial sheets play an important role in healthy tissue development and cancer progression. The maintenance of structure of closely packed epithelial layers requires the coordination of various mechanical forces due to intracellular activities and interactions with other cells and tissues. However, a general model for the combination of mechanical properties which determine the cell shape and the overall structure of epithelial layers remains elusive. Here, we propose a computational model, based on the Cellular Potts Model, to analyse the interplay between mechanical properties of cells and dynamical transitions in epithelial cell shapes and structures. We map out phase diagrams as functions of cellular properties and the orientation of cell division. Results show that monolayers of squamous, cuboidal, and columnar cells are formed when the axis of cell proliferation is perpendicular to the substrate or along the major axis of the cells. Monolayer-to-multilayer transition is promoted via cell extrusion, depending on the mechanical properties of cells and the orientation of cell division. The results and model predictions are discussed in the context of experimental observations.

Personalised statistical modelling of soft tissue structures in the ankle.

BACKGROUND AND OBJECTIVE: Revealing the complexity behind subject-specific ankle joint mechanics requires simultaneous analysis of three-dimensional bony and soft-tissue structures. 3D musculoskeletal models have become pivotal in orthopedic treatment planning and biomechanical research. Since manual segmentation of these models is time-consuming and subject to manual errors, (semi-) automatic methods could improve the accuracy and enlarge the sample size of personalised 'in silico' biomechanical experiments and computer-assisted treatment planning. Therefore, our aim was to automatically predict ligament paths, cartilage topography and thickness in the ankle joint based on statistical shape modelling. METHODS: A personalised cartilage and ligamentous prediction algorithm was established using geometric morphometrics, based on an 'in-house' generated lower limb skeletal model (N = 542), tibiotalar cartilage (N = 60) and ankle ligament segmentations (N = 10). For cartilage, a population-averaged thickness map was determined by use of partial least-squares regression. Ligaments were wrapped around bony contours based on iterative shortest path calculation. Accuracy of ligament path and cartilage thickness prediction was quantified using leave-one-out experiments. The novel personalised thickness prediction was compared with a constant cartilage thickness of 1.50 mm by use of a paired sample T-test. RESULTS: Mean distance error of cartilage and ligament prediction was 0.12 mm (SD 0.04 mm) and 0.54 mm (SD 0.05 mm), respectively. No significant differences were found between the personalised thickness cartilage and segmented cartilage of the tibia (p = 0.73, CI [-1.60 .10-17, 1.13 .10-17]) and talus (p = 0.95, CI[ -1.35 .10-17, 1.28 .10-17]). For the constant thickness cartilage, a statistically significant difference was found in 89% and 92% of the tibial (p < 0.001, CI [0.51, 0.58]) and talar (p < 0.001, CI [0.33, 0.40]) cartilage area. CONCLUSIONS: In this study, we described a personalised prediction algorithm of cartilage and ligaments in the ankle joint. We were able to predict cartilage and main ankle ligaments with submillimeter accuracy. The proposed method has a high potential for generating large (virtual) sample sizes in biomechanical research and mitigates technological advances in computer-assisted orthopaedic surgery.

Bioengineering Clinically Relevant Cardiomyocytes and Cardiac Tissues from Pluripotent Stem Cells.

The regenerative capacity of cardiomyocytes is insufficient to functionally recover damaged tissue, and as such, ischaemic heart disease forms the largest proportion of cardiovascular associated deaths. Human-induced pluripotent stem cells (hiPSCs) have enormous potential for developing patient specific cardiomyocytes for modelling heart disease, patient-based cardiac toxicity testing and potentially replacement therapy. However, traditional protocols for hiPSC-derived cardiomyocytes yield mixed populations of atrial, ventricular and nodal-like cells with immature cardiac properties. New insights gleaned from embryonic heart development have progressed the precise production of subtype-specific hiPSC-derived cardiomyocytes; however, their physiological immaturity severely limits their utility as model systems and their use for drug screening and cell therapy. The long-entrenched challenges in this field are being addressed by innovative bioengingeering technologies that incorporate biophysical, biochemical and more recently biomimetic electrical cues, with the latter having the potential to be used to both direct hiPSC differentiation and augment maturation and the function of derived cardiomyocytes and cardiac tissues by mimicking endogenous electric fields.

Proposal and validation of polyconvex strain-energy function for biological soft tissues.

BACKGROUND: Mechanical simulations for biological tissues are effective technology for development of medical equipment, because it can be used to evaluate mechanical influences on the tissues. For such simulations, mechanical properties of biological tissues are required. For most biological soft tissues, stress tends to increase monotonically as strain increases. OBJECTIVE: Proposal of a strain-energy function that can guarantee monotonically increasing trend of biological soft tissue stress-strain relationships and applicability confirmation of the proposed function for biological soft tissues. METHOD: Based on convexity of invariants, a polyconvex strain-energy function that can reproduce monotonically increasing trend was derived. In addition, to confirm its applicability, curve-fitting of the function to stress-strain relationships of several biological soft tissues was performed. RESULTS: A function depending on the first invariant alone was derived. The derived function does not provide such inappropriate negative stress in the tensile region provided by several conventional strain-energy functions. CONCLUSIONS: The derived function can reproduce the monotonically increasing trend and is proposed as an appropriate function for biological soft tissues. In addition, as is well-known for functions depending the first invariant alone, uniaxial-compression and equibiaxial-tension of several biological soft tissues can be approximated by curve-fitting to uniaxial-tension alone using the proposed function.

A uniaxial force and stiffness model of the vagina during laparoscopic sacrocolpopexy.

BACKGROUND: Laparoscopic sacrocolpopexy is the preferred procedure for restoring vaginal vault prolapse. An assistant uses a vaginal manipulator to position and tension the vault such that the surgeon can dissect the bladder, rectum and vault to eventually suture a synthetic mesh used to suspend the vagina to the longitudinal anterior vertebral ligament. Vaginal vault manipulation requires application of high forces for long periods of time. METHODS: This work quantifies the task by measuring and analyzing the interaction forces and the workspace during vaginal vault manipulation. From the measurements we developed a uniaxial model, expressing the increase in interaction force and stiffness of the vagina. By adapting the model parameters, the difference in interaction force and stiffness between moderate and severe prolapse is predicted. FINDINGS: For moderate prolapse the average interaction force and stiffness start at 2.56 N and 0.11 N mm-1 in the tensionfree state, and go up to 20.14 N and 0.53 N mm-1 after complete insertion of the instrument. For severe degrees of prolapse, tissue interaction is much lower starting at 1.68 N and 0.06 N mm-1 while staying limited to 12.20 N and 0.30 N mm-1 at full extension. INTERPRETATION: Population data shows that the stage of prolapse and total vaginal length increase with age and parity. The interaction force and stiffness of the vagina are correlated with this degree of prolapse. By adapting the model parameters a good estimation of the tissue interaction is found for patients with mild and severe prolapse.

Extended Kalman Filter Nonlinear Finite Element Method for Nonlinear Soft Tissue Deformation.

BACKGROUND AND OBJECTIVE: Soft tissue modelling is crucial to surgery simulation. This paper introduces an innovative approach to realistic simulation of nonlinear deformation behaviours of biological soft tissues in real time. METHODS: This approach combines the traditional nonlinear finite-element method (NFEM) and nonlinear Kalman filtering to address both physical fidelity and real-time performance for soft tissue modelling. It defines tissue mechanical deformation as a nonlinear filtering process for dynamic estimation of nonlinear deformation behaviours of biological tissues. Tissue mechanical deformation is discretized in space using NFEM in accordance with nonlinear elastic theory and in time using the central difference scheme to establish the nonlinear state-space models for dynamic filtering. RESULTS: An extended Kalman filter is established to dynamically estimate nonlinear mechanical deformation of biological tissues. Interactive deformation of biological soft tissues with haptic feedback is accomplished as well for surgery simulation. CONCLUSIONS: The proposed approach conquers the NFEM limitation of step computation but without trading off the modelling accuracy. It not only has a similar level of accuracy as NFEM, but also meets the real-time requirement for soft tissue modelling.

Towards a 2D cortical osseous tissue representation and generation at micro scale. A computational model for bone simulations.

BACKGROUND AND OBJECTIVE: the acquisition of microscopic images of human bones is a complex and expensive process. Moreover, the objective of obtaining a large data bank with microscopic images in order to carry out massive studies or to train automatic generation algorithms is not an option. Consequently, most of the current work focuses on the analysis of small regions captured by a microscope. The aim is the development of a tool to represent bone tissue at microscopic levels which is suitable for performing physical simulations, as well as for the diagnosis of various diseases. This work includes the whole process from the digitization of a human bone to the generation of bone tissue in a determined area of the bone selected through a cutting plane. METHODS: based on the anatomy of the bone structure, the parameters that allow the representation of the bone tissue at mesoscale level have been analyzed. Although the models are randomly generated, they are based on statistical parameters. The model generator is based on the analysis of images of bone tissue and its parameters, performing a representation of each of its relevant structures in a way that fulfils these parameters. RESULTS: the tool is useful for the virtual generation of bone tissue that satisfies the main characteristics of the cortical bone. The models obtained have been favorably evaluated in two stages. In the first stage, a scientific group has examined a set of images, in which images of the models generated were mixed with images obtained through traditional methods. Then, the physical characteristics of the generated tissue have been compared with the morphology of the bone tissue. CONCLUSIONS: the model generator allows us to perform precise simulations in order to obtain realistic images with physical characteristics in accordance with reality. It is necessary to emphasize that even though the most relevant structures are included, the proposed model generator can be expanded to include new parameters or elements, so that it can be adapted to new needs. It could even break down randomness and parameterize it completely in order to allow the recreation of the tissue conditions of other studies.

An automated computational biomechanics workflow for improving breast cancer diagnosis and treatment.

Clinicians face many challenges when diagnosing and treating breast cancer. These challenges include interpreting and co-locating information between different medical imaging modalities that are used to identify tumours and predicting where these tumours move to during different treatment procedures. We have developed a novel automated breast image analysis workflow that integrates state-of-the-art image processing and machine learning techniques, personalized three-dimensional biomechanical modelling and population-based statistical analysis to assist clinicians during breast cancer detection and treatment procedures. This paper summarizes our recent research to address the various technical and implementation challenges associated with creating a fully automated system. The workflow is applied to predict the repositioning of tumours from the prone position, where diagnostic magnetic resonance imaging is performed, to the supine position where treatment procedures are performed. We discuss our recent advances towards addressing challenges in identifying the mechanical properties of the breast and evaluating the accuracy of the biomechanical models. We also describe our progress in implementing a prototype of this workflow in clinical practice. Clinical adoption of these state-of-the-art modelling techniques has significant potential for reducing the number of misdiagnosed breast cancers, while also helping to improve the treatment of patients.

Ultrasonic Based Tissue Modelling and Engineering.

Systems and devices for in vitro tissue modelling and engineering are valuable tools, which combine the strength between the controlled laboratory environment and the complex tissue organization and environment in vivo. Device-based tissue engineering is also a possible avenue for future explant culture in regenerative medicine. The most fundamental requirements on platforms intended for tissue modelling and engineering are their ability to shape and maintain cell aggregates over long-term culture. An emerging technology for tissue shaping and culture is ultrasonic standing wave (USW) particle manipulation, which offers label-free and gentle positioning and aggregation of cells. The pressure nodes defined by the USW, where cells are trapped in most cases, are stable over time and can be both static and dynamic depending on actuation schemes. In this review article, we highlight the potential of USW cell manipulation as a tool for tissue modelling and engineering.

A finite nonlinear hyper-viscoelastic model for soft biological tissues.

Soft tissues exhibit highly nonlinear rate and time-dependent stress-strain behaviour. Strain and strain rate dependencies are often modelled using a hyperelastic model and a discrete (standard linear solid) or continuous spectrum (quasi-linear) viscoelastic model, respectively. However, these models are unable to properly capture the materials characteristics because hyperelastic models are unsuited for time-dependent events, whereas the common viscoelastic models are insufficient for the nonlinear and finite strain viscoelastic tissue responses. The convolution integral based models can demonstrate a finite viscoelastic response; however, their derivations are not consistent with the laws of thermodynamics. The aim of this work was to develop a three-dimensional finite hyper-viscoelastic model for soft tissues using a thermodynamically consistent approach. In addition, a nonlinear function, dependent on strain and strain rate, was adopted to capture the nonlinear variation of viscosity during a loading process. To demonstrate the efficacy and versatility of this approach, the model was used to recreate the experimental results performed on different types of soft tissues. In all the cases, the simulation results were well matched (R2⩾0.99) with the experimental data.

Dynamic cellular finite-element method for modelling large-scale cell migration and proliferation under the control of mechanical and biochemical cues: a study of re-epithelialization.

Computational modelling of cells can reveal insight into the mechanisms of the important processes of tissue development. However, current cell models have limitations and are challenged to model detailed changes in cellular shapes and physical mechanics when thousands of migrating and interacting cells need to be modelled. Here we describe a novel dynamic cellular finite-element model (DyCelFEM), which accounts for changes in cellular shapes and mechanics. It also models the full range of cell motion, from movements of individual cells to collective cell migrations. The transmission of mechanical forces regulated by intercellular adhesions and their ruptures are also accounted for. Intra-cellular protein signalling networks controlling cell behaviours are embedded in individual cells. We employ DyCelFEM to examine specific effects of biochemical and mechanical cues in regulating cell migration and proliferation, and in controlling tissue patterning using a simplified re-epithelialization model of wound tissue. Our results suggest that biochemical cues are better at guiding cell migration with improved directionality and persistence, while mechanical cues are better at coordinating collective cell migration. Overall, DyCelFEM can be used to study developmental processes when a large population of migrating cells under mechanical and biochemical controls experience complex changes in cell shapes and mechanics.

The importance of nonlinear tissue modelling in finite element simulations of infant head impacts.

Despite recent efforts on the development of finite element (FE) head models of infants, a model capable of capturing head responses under various impact scenarios has not been reported. This is hypothesized partially attributed to the use of simplified linear elastic models for soft tissues of suture, scalp and dura. Orthotropic elastic constants are yet to be determined to incorporate the direction-specific material properties of infant cranial bone due to grain fibres radiating from the ossification centres. We report here on our efforts in advancing the above-mentioned aspects in material modelling in infant head and further incorporate them into subject-specific FE head models of a newborn, 5- and 9-month-old infant. Each model is subjected to five impact tests (forehead, occiput, vertex, right and left parietal impacts) and two compression tests. The predicted global head impact responses of the acceleration-time impact curves and the force-deflection compression curves for different age groups agree well with the experimental data reported in the literature. In particular, the newly developed Ogden hyperelastic model for suture, together with the nonlinear modelling of scalp and dura mater, enables the models to achieve more realistic impact performance compared with linear elastic models. The proposed approach for obtaining age-dependent skull bone orthotropic material constants counts both an increase in stiffness and decrease in anisotropy in the skull bone-two essential biological growth parameters during early infancy. The profound deformation of infant head causes a large stretch at the interfaces between the skull bones and the suture, suggesting that infant skull fractures are likely to initiate from the interfaces; the impact angle has a profound influence on global head impact responses and the skull injury metrics for certain impact locations, especially true for a parietal impact.

A three-dimensional large deformation model for soft tissue using meshless method.

BACKGROUND: Recently, a great number of studies have been carried out to model soft tissue deformation in contact with surgical instruments to aid the development of surgical simulators. Precise methods to model the soft tissue such as the Finite Element Method (FEM) lack accuracy in large deformations. METHODS: An innovative meshless method is used, which has high precision and is applicable to large deformations. The meshless simulation method is implemented for a 2D beam and a 3D cube. Experiments are conducted for two silicone-gel samples to verify the correctness of the method. RESULTS: The meshless results in 2D and 3D show better accuracy for large deformations in comparison with the FEM. This method is used to model human organs such as liver and gallbladder. CONCLUSION: It is concluded that the proposed model exhibits good accuracy as well as speed. Thus, it seems promising to be employed in surgical simulators. Copyright © 2015 John Wiley & Sons, Ltd.

Soft tissue modelling with conical springs.

This paper presents a new method for real-time modelling soft tissue deformation. It improves the traditional mass-spring model with conical springs to deal with nonlinear mechanical behaviours of soft tissues. A conical spring model is developed to predict soft tissue deformation with reference to deformation patterns. The model parameters are formulated according to tissue deformation patterns and the nonlinear behaviours of soft tissues are modelled with the stiffness variation of conical spring. Experimental results show that the proposed method can describe different tissue deformation patterns using one single equation and also exhibit the typical mechanical behaviours of soft tissues.

Glassy dynamics in three-dimensional embryonic tissues.

Many biological tissues are viscoelastic, behaving as elastic solids on short timescales and fluids on long timescales. This collective mechanical behaviour enables and helps to guide pattern formation and tissue layering. Here, we investigate the mechanical properties of three-dimensional tissue explants from zebrafish embryos by analysing individual cell tracks and macroscopic mechanical response. We find that the cell dynamics inside the tissue exhibit features of supercooled fluids, including subdiffusive trajectories and signatures of caging behaviour. We develop a minimal, three-parameter mechanical model for these dynamics, which we calibrate using only information about cell tracks. This model generates predictions about the macroscopic bulk response of the tissue (with no fit parameters) that are verified experimentally, providing a strong validation of the model. The best-fit model parameters indicate that although the tissue is fluid-like, it is close to a glass transition, suggesting that small changes to single-cell parameters could generate a significant change in the viscoelastic properties of the tissue. These results provide a robust framework for quantifying and modelling mechanically driven pattern formation in tissues.