Multiscale mechano-morphology of soft tissues : a computational study with applications to cancer diagnosis and treatment

Cooperation of engineering and biomedical sciences has produced significant advances in healthcare technology. In particular, computational modelling has led to a faster development and improvement of diagnostic and treatment techniques since it allows exploring multiple scenarios without additional complexity and cost associated to the traditional trial-and-error methodologies. The goal of this thesis is to propose computational methodologies to analyse how the changes in the microstructure of soft tissues, caused by different pathological conditions, influence the mechanical properties at higher length scales and, more importantly, to detect such changes for the purpose of quantitative diagnosis and treatment of such pathologies in the scenario of drug delivery. To achieve this objective different techniques based on quasi-static and dynamic probing have been established to perform quantitative tissue diagnosis at the microscopic (tissue) and macroscopic (organ) scales. The effects of pathologies not only affect the mechanical properties of tissue (e.g. elasticity and viscoelasticity), but also the transport properties (e.g. diffusivity) in the case of drug delivery. Such transport properties are further considered for a novel multi-scale, patient-specific framework to predict the efficacy of chemotherapy in soft tissues. It is hoped that this work will pave the road towards non-invasive palpation techniques for early diagnosis and optimised drug delivery strategies to improve the life quality of patientsJames-Watt Scholarship, Heriot-Watt Annual Fund and the Institute of Mechanical, Process and Energy Engineering (IMPEE) Grant

A systematic study of Brain Tissue microstructure: from composition to biomechanics and modelling of White Matter

This thesis aims to shed light on the biomechanical knowledge of the brain, in particular of its white matter (WM). An extensive, multidisciplinary and bottom-up research has been carried out to understand its micromechanical response focusing on three areas: Corpus Callosum, Corona Radiata and Fornix. Axons and the surrounding matrix with its accessory cells, the two main components of the tissue, have been analysed via Focused Ion Beam Scanning Electron Microscopy (FIB-SEM). Tissue volumes have been sampled, stained, embedded and imaged to finally be 3D-reconstructed, appearing as unidirectional composite materials. They have been geometrically characterised, creating a location-specific database of: cross-sectional area, diameter, ellipticity and tortuosity of the axons, together with the volume fraction of the volumes. The AFM-enabled nanoindentations investigated the mechanical response of WM. Stress-relaxation experiments have been performed on samples with axons running either parallel or perpendicular to the testing plane. The tissue showed linear viscoelasticity and slight anisotropy at the investigated level. The perpendicular samples showed a higher initial stiffness than parallel samples while the relative change in stiffness after relaxation was higher for the parallel samples than for the perpendicular ones. Finally, micromechanical modelling of the areas was performed. Location-speci c Representative Volume Elements have been created with the geometrical info obtained via FIB-SEM. Via an inverse-modelling approach, using the AFM data, material parameters of the axons and the matrix, the tissue components, have been obtained. The predicted stress-relaxation curves simulated by the finite element analysis showed good agreement with the experimental curves. The acquired knowledge of the microenvironment is fundamental for a comprehensive microscopical characterisation of the white matter. It provides important information to reduce axonal damage during neurosurgery, by predicting the local mechanical response and planning accordingly, and to improve the efficacy and therapeutic reach of Convection Enhanced Delivery, by exploiting the cytoarchitecture, leading to minimal side effects and maximal efficacy of the treatments.Open Acces

The biomechanical basis for glenoid labral tears

Froth flotation is the most widely used and versatile method for separating and concentrating minerals. Recent industrial experimental work has shown a positive metallurgical response can be achieved from a bank of flotation cells bv distributil1!.! the same volume of gas differently. This studv developed a froth based model of a rougher flotation bank to determine. by simulation. an air profile that will produce a high !.!rade concentrate from the first cell of the bank and a hi!.!h cumulative recovery. FrothSim is a phvsics based model of the froth zone in a flotation cell. This simulator has previously been used to model the !.!radc and recovery of multiple minerals from a sinulc tank. However. FrothSil1l requires experimental measurements of the overflowing bubble sizc and the fraction of the inlet air which overflows the \Veir of the cell as unburst bubbles. the air recavcn'. to make predictions. In this work FrothSil1l was used. for the first time. to develop a base case model of a bank of roLH!her flotation cells based on industrial experimental data. A close a!.!reement was obtained betwcen the modelled results and the experimental results for both floatable and cntrained minerals. A rigorous and robust procedure for the development of sin!.!le cell and full bank models was produced. An empirical model to predict the superficial velocity of the gas lost throu!.!h the froth surface has been developed. This model can be used to predict the air recovery from a flotation cell at different air rates. In conjunction. overflowin!.! bubble sizes were inferred from expcrimental data usin!.! a theoretical model. These were found not to vary significantly with air rate. These two models were combined with the FrothSil1l base case model to predict flotation performance at different gas distribution profiles. A new profile was found to vield the desired performance. The improved perfomlance can be attributed to an increase in froth recovery at all points in the hank. An industrial sampling campai!.!n was carried out to verifv the predicted operatin!.! performance for three eas distribution profiles. The experimental !.!rade rccoven' curves. for both floatable and primarilY entrained minerals. showed the same trends as the predicted results. The new gas distribution profile eave a hi!.!h grade concentrate from cell 1 and the hi!.!hest cumulatiye recoven'. This froth modelling approach. in which empirical models are combined with a physics based froth model can therefore be used to successfully manage gas distribution to a bank of cells.Imperial Users onl

Empowering Materials Processing and Performance from Data and AI

Third millennium engineering address new challenges in materials sciences and engineering. In particular, the advances in materials engineering combined with the advances in data acquisition, processing and mining as well as artificial intelligence allow for new ways of thinking in designing new materials and products. Additionally, this gives rise to new paradigms in bridging raw material data and processing to the induced properties and performance. This present topical issue is a compilation of contributions on novel ideas and concepts, addressing several key challenges using data and artificial intelligence, such as:- proposing new techniques for data generation and data mining;- proposing new techniques for visualizing, classifying, modeling, extracting knowledge, explaining and certifying data and data-driven models;- processing data to create data-driven models from scratch when other models are absent, too complex or too poor for making valuable predictions;- processing data to enhance existing physic-based models to improve the quality of the prediction capabilities and, at the same time, to enable data to be smarter; and- processing data to create data-driven enrichment of existing models when physics-based models exhibit limits within a hybrid paradigm

Ultrasound-driven microbubble dynamics in microvessels

Ultrasound and microbubble induced blood-brain barrier opening has shown success in clinical trials as a promising method to deliver drugs to the brain. Shelled gas bubbles, a few micrometres in diameter, are administered intravenously, and distribute throughout the cardiovascular system. When ultrasound is applied to the brain, the microbubbles expand and contract within the vasculature, temporarily disrupting the blood-brain barrier, and allowing drugs to pass through. While this technique has been shown to be effective at delivering drugs, its mechanisms remain relatively poorly understood. Better understanding how microbubbles interact with tissues could enable refinement of therapies. This thesis investigates the fundamental physical interactions between microbubbles and soft tissues using two distinct but related experimental platforms that utilise high-speed microscopy. Firstly, microbubbles within soft tissue-mimicking hydrogel channels are observed during exposure to typical therapeutic ultrasound pulses. The primary radiation force is shown to be significant, and can cause bubbles to deform the soft gels by several micrometres. Microbubbles are also investigated in brain tissue, using acute cortical slices from the brains of juvenile rats, transcardially perfused post-mortem with a concentrated solution of SonoVue®. This technique is shown to be an effective method of observing microbubbles using optical microscopy within the microvasculature of live brain tissue. Radial oscillations of bubbles within brain microvessels can deform surrounding tissue at both microsecond and millisecond time scales. Extravasation of microbubbles due to the primary radiation force can occur during typical ultrasound pulses, and is common at higher ultrasound pressures (mechanical index of 0.6 and above). These results demonstrate the significance of both radial oscillations and the primary radiation force as ways in which microbubbles can physically impact their surroundings. Additionally, acute brain slices are shown to be a valuable tool to investigate microbubble behaviours and mechanisms of drug delivery in a physiologically relevant environment.Open Acces

Computational ultrasound tissue characterisation for brain tumour resection

In brain tumour resection, it is vital to know where critical neurovascular structuresand tumours are located to minimise surgical injuries and cancer recurrence. Theaim of this thesis was to improve intraoperative guidance during brain tumourresection by integrating both ultrasound standard imaging and elastography in thesurgical workflow. Brain tumour resection requires surgeons to identify the tumourboundaries to preserve healthy brain tissue and prevent cancer recurrence. Thisthesis proposes to use ultrasound elastography in combination with conventionalultrasound B-mode imaging to better characterise tumour tissue during surgery.Ultrasound elastography comprises a set of techniques that measure tissue stiffness,which is a known biomarker of brain tumours. The objectives of the researchreported in this thesis are to implement novel learning-based methods for ultrasoundelastography and to integrate them in an image-guided intervention framework.Accurate and real-time intraoperative estimation of tissue elasticity can guide towardsbetter delineation of brain tumours and improve the outcome of neurosurgery. We firstinvestigated current challenges in quasi-static elastography, which evaluates tissuedeformation (strain) by estimating the displacement between successive ultrasoundframes, acquired before and after applying manual compression. Recent approachesin ultrasound elastography have demonstrated that convolutional neural networkscan capture ultrasound high-frequency content and produce accurate strain estimates.We proposed a new unsupervised deep learning method for strain prediction, wherethe training of the network is driven by a regularised cost function, composed of asimilarity metric and a regularisation term that preserves displacement continuityby directly optimising the strain smoothness. We further improved the accuracy of our method by proposing a recurrent network architecture with convolutional long-short-term memory decoder blocks to improve displacement estimation and spatio-temporal continuity between time series ultrasound frames. We then demonstrateinitial results towards extending our ultrasound displacement estimation method toshear wave elastography, which provides a quantitative estimation of tissue stiffness.Furthermore, this thesis describes the development of an open-source image-guidedintervention platform, specifically designed to combine intra-operative ultrasoundimaging with a neuronavigation system and perform real-time ultrasound tissuecharacterisation. The integration was conducted using commercial hardware andvalidated on an anatomical phantom. Finally, preliminary results on the feasibilityand safety of the use of a novel intraoperative ultrasound probe designed for pituitarysurgery are presented. Prior to the clinical assessment of our image-guided platform,the ability of the ultrasound probe to be used alongside standard surgical equipmentwas demonstrated in 5 pituitary cases

Responsive nanostructures for controlled alteration of interfacial properties

Responsive materials are a class of materials that are capable of “intelligently” changing properties upon exposure to a stimulus. Silk ionomers are introduced as a promising candidate of biopolymers that combine the robust, biocompatible properties of silk fibroin with the responsive properties of poly-l-lysine (PL) and poly-l-glutamic acid (PG). These polypeptides can be assembled using the well-known technique of layer-by-layer processing, allowing for the creation of finely tuned nanoscale multilayers coatings, but their properties remain largely unexplored in the literature. Thus, this research explores the properties of silk ionomer multilayers assembled in different geometries, ranging from planar films to three-dimensional microcapsules with the goal of created responsive systems. These silk ionomers are composed of a silk fibroin backbone with a variable degree of grafting with PG (for anionic species) or PL or PL-block- polyethylene glycol (PEG) (for cationic species). Initially, this research is focused on fundamental properties of the silk ionomer multilayer assemblies, such as stiffness, adhesion, and shearing properties. Elastic modulus of the materials is considered to be one of the most important mechanical parameters, but measurements of stiffness for nanoscale films can be challenging. Thus, we studied the applicability of various contact mechanics models to describe the relationship between force distance curves obtained by atomic force microscopy and the stiffness of various polymeric materials. Beyond considerations of tip size, we also examine the critical regions at which various commonly used indenter geometries are valid. Following this, we employed standard AFM probes and colloidal probes coated with covalently bonded silk ionomers to examine the friction and adhesion between silk ionomers layers. This technique allowed us to compare the interactions between silk ionomers of different chemical composition by using multilayer films containing standard silk ionomers or silk ionomers grafted with polyethylene glycol PEG. This led to the unexpected result that the PEG grafted silk ionomers experienced a higher degree of adhesion and a larger friction coefficient compared to the standard silk ionomers. Next, we move to microscale responsive systems based on silk ionomer multilayers. The first of these studies looks at the effect of assembly pH and chemical composition on the ultimate properties of hollow, spherical microcapsules. This study shows that all compositions and processing conditions yield microcapsules that show a substantial change in elastic modulus, swelling, and permeability, with maximum changes in property values (from acidic pH to basic pH) of around a factor of 6, 1.5, and 5, respectively. In addition, it was discovered that the use of acidic pH assembly inverts the permeability response (i.e. causes a drastic reduction in permeability at higher pH), whilst the use of PEG largely damps any observable trend in permeability, without adversely affecting the swelling or elastic modulus responses. In the second part of these studies, we constructed tri-component photopatterned arrays for the purpose of creating self-rolling films. This study demonstrated that the ultimate geometry of the final rolled shape can be tuned by controlling the thickness of various components, due to the creation of a stress mismatch at high pH conditions. Additionally, it was revealed that pH-driven, semi-reversible delamination of silk ionomers from polystyrene exhibited a change in both magnitude and wavelength with the addition of methanol treated silk fibroin as a top layer. Finally, we showcase examples of biologically compatible systems that incorporate non-polymeric materials in order to generate tunable optical behavior. In one study, we fabricated composite nanocellulose-silk fibroin meshes that contained genetically engineered bacteria that acted as chemically sensitive elements with a fluorescent response. The addition of silk fibroin was found to drastically improve the mechanical properties of the cellulose composite structures, safely contain the bacteria to prevent efflux into the medium, and protect the cells from moderate ultraviolet radiation exposure. The final study concludes with the creation of a self-assembled segmented gold-nickel nanorod array used as a responsive element when anchored into a hydrogen-bonded polymer multilayer. Because of the mild tethering conditions and the magnetic nickel component, the nanorods were able to tilt in response to an external magnetic field. This, in turn, allowed for the creation of a never before reported magnetic-plasmonic system capable of continuously-shifting multiple surface polariton scattering peaks (up to 100 nm shifts) with nearly complete reversibility and rapid (<1 s) response times. Overall, this research develops the understanding of the fundamental properties of several different species of silk ionomers and related polymeric materials. This understanding is then utilized to fabricate pH-responsive systems with drastic changes in modulus, permeability, and geometry. In the end, the research prototypes two types of systems with optical responses and chemical/magnetic stimuli, using materials that are chemically (i.e. silk fibroin-based) or structurally (i.e. multilayers) translatable to future work on silk ionomers. These projects all serve the purpose of advancing the understanding of materials and assembly strategies that will allow for the next generation of bioinspired responsive materials.Ph.D

High Fidelity Computational Modeling and Analysis of Voice Production

This research aims to improve the fundamental understanding of the multiphysics nature of voice production, particularly, the dynamic couplings among glottal flow, vocal fold vibration and airway acoustics through high-fidelity computational modeling and simulations. Built upon in-house numerical solvers, including an immersed-boundary-method based incompressible flow solver, a finite element method based solid mechanics solver and a hydrodynamic/aerodynamic splitting method based acoustics solver, a fully coupled, continuum mechanics based fluid-structure-acoustics interaction model was developed to simulate the flow-induced vocal fold vibrations and sound production in birds and mammals. Extensive validations of the model were conducted by comparing to excised syringeal and laryngeal experiments. The results showed that, driven by realistic representations of physiology and experimental conditions, including the geometries, material properties and boundary conditions, the model had an excellent agreement with the experiments on the vocal fold vibration patterns, acoustics and intraglottal flow dynamics, demonstrating that the model is able to reproduce realistic phonatory dynamics during voice production. The model was then utilized to investigate the effect of vocal fold inner structures on voice production. Assuming the human vocal fold to be a three-layer structure, this research focused on the effect of longitudinal variation of layer thickness as well as the cover-body thickness ratio on vocal fold vibrations. The results showed that the longitudinal variation of the cover and ligament layers thicknesses had little effect on the flow rate, vocal fold vibration amplitude and pattern but affected the glottal angle in different coronal planes, which also influenced the energy transfer between glottal flow and the vocal fold. The cover-body thickness ratio had a complex nonlinear effect on the vocal fold vibration and voice production. Increasing the cover-body thickness ratio promoted the excitation of the wave-type modes of the vocal fold, which were also higher-eigenfrequency modes, driving the vibrations to higher frequencies. This has created complex nonlinear bifurcations. The results from the research has important clinical implications on voice disorder diagnosis and treatment as voice disorders are often associated with mechanical status changes of the vocal fold tissues and their treatment often focus on restoring the mechanical status of the vocal folds

Numerical modelling of additive manufacturing process for stainless steel tension testing samples

Nowadays additive manufacturing (AM) technologies including 3D printing grow rapidly and they are expected to replace conventional subtractive manufacturing technologies to some extents. During a selective laser melting (SLM) process as one of popular AM technologies for metals, large amount of heats is required to melt metal powders, and this leads to distortions and/or shrinkages of additively manufactured parts. It is useful to predict the 3D printed parts to control unwanted distortions and shrinkages before their 3D printing. This study develops a two-phase numerical modelling and simulation process of AM process for 17-4PH stainless steel and it considers the importance of post-processing and the need for calibration to achieve a high-quality printing at the end. By using this proposed AM modelling and simulation process, optimal process parameters, material properties, and topology can be obtained to ensure a part 3D printed successfully

Spatial Control of Mechanical Factors: a New Design Rationale for Nerve Tissue Engineering

Peripheral nerve injuries (PNI) result from traumatic injury, surgery or repetitive compression, and are reported in 3-5% of all trauma patients. The impact ranges from severe (major loss of sensory/motor function and/or intractable neuropathic pain) to mild (some sensory and/or motor deficits) and in both cases, is devastating for the patient. PNI affect ∼1M people in Europe and the US p.a. of whom 660,000 have surgery. PNI has high healthcare, unemployment, rehabilitation, societal costs and affects mostly young people. The current surgical practice for nerve gaps >3 cm is to bridge the site of injury with a graft taken from the patient. However, this involves additional time, cost and damage to a healthy nerve, limited supply, and unsatisfactory functional recovery (50% of the cases). For these reasons, research has focused on developing artificial nerve conduits to replace grafts, but to-date those available for clinical use do not match and/or exceed the functional performance of the autograft. This project develops a rational basis for promoting neurite growth through tissue-engineered conduits for peripheral nerve repair, by exploiting the response of cells to spatial variations in mechanical properties of conduits to inform their design. This is achieved through an interdisciplinary approach, that combines in vitro experimentation with mathematical modelling. First of all, the mechanical and structural properties of RAFTStabilised collagen gels (RsC) are explored, physiologically coherent RsC stiffness gradients are fabricated and characterised as well as the neuronal response to them. Finally, a predictive framework to inform the design of nerve conduits is parameterised and tested using experimental results and literature. The use of this multidisciplinary approach can help tissue engineers in the development of novel tissue repair solutions, as well as informing mathematical models of neurite behaviour which can contribute to the design process