Optimisation of human cutaneous inflammatory challenge models: the role of skin’s biophysical properties
Supervisors: James Fullerton, Richard Day, Majella Lane
Skin blisters generated on volunteers via either chemical application or physical means (suction) are extensively used in drug development. Acting as a controlled inflammatory insult (permitting access to cells and soluble mediators) they allow experimental assessment of a drugs effect of the immune system. A key problem with the technique however is inter-individual variability in response. This project seeks to explore the contribution of the biophysical properties of the skin to this. Working in the Division of Medicine and School of Pharmacy using multimodal techniques (including confocal Raman microscopy, tape stripping, trans-epidermal water loss, vascular laser Doppler, flow cytometry) the student(s) will seek to assess whether differences in biophysical metrics explain variance and whether they can be used to normalise responses. The project will be undertaken in conjunction with clinicians and biomedical scientists with industry links (GSK) and is anticipated to be of high clinical and pharmaceutical value.
Surface charge on insulators
Supervisors: Sergio Bertazzo, Elena Tsolaki
Electrostatic induction phenomena have been studied for centuries and results are consolidated e.g. in Maxwell’s Treatise. Fundamental concept are well established and their application is straightforward for metals and semiconductors but not for insulators. Insulator electrostatic charging is hardly reproducible or predictable and this is related to the current lack of agreement on the nature of charge carriers in electrified insulators.
Current thinking considers that electrification of a dielectric arises from charge displacement within the solid under the action of an external electric field. A macroscopic electric dipole (or multipole) is thus formed, aligned with the field on which the solid is immersed. Following these ideas, charge displacement from one solid to another is held as the mechanism for electret formation by contact electrification.
The influence of the surrounding atmosphere on electrostatic phenomena is well-known but it is not well understood. Some reports describe the effect of water adsorption on polymer contact charging but it is interpreted as a modifying factor in the electron-pair donor-acceptor interactions that these authors hold responsible for contact charging.
These results led to the formulation of the following hypothesis: electrostatic phenomena under atmospheric conditions have a contribution from atmospheric ions as well as from excess ions generated by charging of adsorbed water. Atmospheric ions are charge carriers that migrate under the action of electric fields, distribute within electric potential gradients according to Poisson-Boltzmann equation, adsorb on solid and liquid surfaces, and discharge electrochemically on metal and semiconductor surfaces.
The project will consist to design and create an electrical isolated apparatus to measure the build-up and dissipation of electrical charges in insulators. The student need to be trained at the Institute of Making and will use its facilities for the project. The student will participate in the development of the project, experimental measurements, analysis of the data and interpretation. This is an experimental research project where laboratory experience is a plus but not a requirement.
Developing a Phase Unwrapping Technique for MRI Using Machine Learning
Supervisors: Barbara Dymerska, Karin Shmueli
The signal in Magnetic Resonance Imaging (MRI) has two constituents: magnitude and phase. In most clinical MRI studies, only the magnitude information is used. Recently, there has been rapid development of MRI methods which utilise the phase as it can be used to increase image contrast, correct image distortions or provide information about the tissue magnetic properties (e.g. in quantitative susceptibility mapping). Phase image processing is not trivial as the range of phase values which can be measured in MRI is limited to 2π radians. This means that any phase value larger than 2π is wrapped back into this range and phase unwrapping methods are necessary to recover the true phase values. Existing unwrapping methods are often time-consuming and fail in regions which are noisy or have strong phase variations. The initial aim of this project is to develop realistic numerical models of phase variations in the human brain. These models will then be used to train a convolutional neural network to give a fast and robust solution to the phase unwrapping problem and compare it against existing unwrapping algorithms. This project, in a fast-moving and exciting research area, will give you an insight into MRI signal properties, MRI image processing, Matlab programming and machine learning algorithms.
Characterising phantoms for elastography
Supervisors: Peter Munro and Pilar Garcia Souto
Student: Ana-Maria Nicolaie
Optical Coherence Elastrography obtains images of the mechanical properties of tissue and is performed by using Optical Coherence Tomography to image how tissue deforms in response to a mechanical load. A crucial part of this project is the development of phantoms with controllable mechanical properties. This project entails the construction of such phantoms along with their characterisation using an instron machine which provides gold standard characterisation of the mechanical properties of materials. This project is largely experimental, though an elementary understanding of continuum mechanics will be beneficial.
fNIRS data analysis in the Brain Imaging for Global Health (BRIGHT)
Supervisors: Anna Blasi, Chiara Bulgarelli
Functional near-infrared spectroscopy (fNIRS) uses near-infrared light to measure colour changes of the blood in the brain as indicator of functional activation. This innovative neuroimaging method is compact, it can be used with awake infants and can record neural activation during live interactions. This has led to a rapid growth in its use over the last decade, and has enabled its application in previously understudied settings, such as low- and middle-income countries. In fact, thanks to fNIRS, we have been able to conduct the first neuroimaging study in The Gambia, West Africa, in the context of the Brain Imaging for Global Health (BRIGHT) project. This project aims to look at the impact of undernutrition on infant growth, and to establish brain function-for-age curves. In this respect, fNIRS plays a pivotal role, as it allows us to assess brain activation in Gambian infants and toddlers for whom behavioural assessments and growth measures are also collected. More than 200 infants in the Gambia and 60 in the UK have been recruited into the BRIGHT project and have been followed longitudinally from birth to 2 years of age. The aim of the proposed work is to contribute to the fNIRS data analysis for one of the tasks included in the BRIGHT project, the Deferred Imitation task. The aim of this task is to assess neural correlates of memory development as measured by infants’ imitation during a live interaction. In order to determine cortical activation elicited by the memory processes enabling imitation, it is necessary to precisely time-lock acquired fNIRS data with the experimental sequence, which varies across infants and includes the following experimental conditions: (1) presentation of the toy; (2) free play of the infant with the toy; (3) experimenter demonstrating how to play with the toy; (4) repeated free play with the same toy. The student will develop an efficient strategy to select the timing of the different testing conditions presented to the infants from the videos of the testing sessions, and will incorporate them into the fNIRS data files. This is a fundamental part of the data analysis process, as it will synchronise the fNIRS data with the onsets of conditions of the task, and will prepare the data for further analysis. As data analysis will be mainly performed in Matlab, this project is suitable for students with some knowledge of computer programming, or a motivation to learn.
Modeling Parkinson's disease biomarker trajectories
Supervisors: Andre Altmann, Leon Aksman, Neil Oxtoby
In this project we will try to understand how imaging, clinical and biofluid Parkinson's disease biomarkers change over time, when measured longitudinally within subjects. We will have the opportunity to use both standard and recently developed trajectory models.
Feasibility of imaging fast neural activity using Electrical Impedance Tomography (EIT) with depth electrodes: simulation study.
Supervisors: Kirill Aristovich, Sana Hannan
EIT has a potential to be a unique tool in neuroscience research and clinical practice. In order to improve spatial resolution of EIT when preforming 3D imaging of fast neural activity, the so-called depth electrodes can be introduced. The project will involve investigation of their usefulness and potential improvement on image quality through performing simulation study. The student will be using existing models and simulation software, alter the geometry and parameters, and investigate their influence on image quality. The project will suit somebody with a good programming skills, and experience with matlab. Interest in finite element analysis is a bonus.
The effect of transcutaneous electrical stimulation of the median nerve on thenar muscle oxygenation
Supervisors: Gemma Bale, Anne Vanhoest
Student: Lorenzo Molinari
There are many reports in the literature on the use of electrical stimulation to promote wound healing, especially for patients with impaired arterial blood flow. In this project, the student will use a set of sensors to assess the impact of electrical stimulation on physiological parameters associated with blood circulation. The student will use a broadband near-infrared spectroscopy system to assess the impact of the electrical stimulation on blood oxygenation (through haemoglobin oxygenation changes) and muscle metabolism (via the oxidation state of cytochrome c oxidase). The student will design and conduct a large study on healthy volunteers (n>20) and analyse the data with statistical testing to assess whether electrical stimulation has the potential to treat wound healing.
Development of a dynamic phantom for diffuse optical imaging based on smart film technology
Supervisors: Jem Hebden, Eve Hatten
Student: Ariana Avaei
The UCL Biomedical Optics Research Laboratory (BORL) has extensive expertise in the development of physical models of human tissue (known as “phantoms”) which have optical properties matched to those of real human organs. These are used to evaluate new optical techniques and instruments for diagnostic monitoring and imaging, and of the brain in particular. For example, optical imaging techniques are commonly used to observe localised changes occurring in the brain, due to variation in blood flow and oxygenation. The objective of this project is to develop a “dynamic” phantom which can mimic a rapid change in localised optical properties within the brain. The change will be produced by a layer of “smart film” whose optical transparency changes when an electrical voltage is applied. The student will experiment with samples of smart film, and then develop a means of activating the smart film when embedded within a solid block of polyester resin. Pigments will be added to the resin to mimic the scattering and absorbing properties of biological tissues. Finally, images of the dynamic phantom will be generated using the UCL diffuse optical topography system, and its optical properties will be fully characterised.
Automated segmentation of organs-at-risk for paediatric radiotherapy
Supervisors: Catarina Veiga and TBC
Currently, 40-50% of children with cancer receive radiotherapy (RT) as part of their treatment. In RT treatments, the tumour and organs-at-risk (OARs) are manually delineated on planning CT scans, and then the treatment beams are designed to maximise dose to tumour while minimising dose to healthy tissues. Children are particularly sensitive to radiation effects, and so it is very important to quantify and minimise dose delivered to OARs, even if they are not directly in the beam path. However, in clinical practice such delineations are not always available since manual delineation is laborious and time-consuming – automated techniques are therefore highly desirable.
This project will suit an intercalated medical student with interests in radiation oncology and willing to learn computing skills such as MATLAB. The student will create a database of CT images and segmentations of organs at risk in medulloblastoma patients, a common type of childhood cancer. These will be used to test different strategies for automatic organ segmentation, and identify the most promising approaches.
Glass-glass sealing for implantable electronics
Supervisor: Henry Lancashire and Kirill Aristovich
Implanted electronic devices must be protected from the corrosive environment of the body. One way of protecting electronics is to create a hermetic, or water impermeable, package which keeps the contents dry. These packages are often made from metals, ceramics, or glasses. You will investigate methods of creating very small hermetic packages by fusing glass layers using high-temperature or laser sintering. A successful student will have knowledge of electronics manufacturing and the attention to detail needed for working in a cleanroom environment.
Neural electrode discharge following monophasic pulses
Supervisors: Henry Lancashire & Nick Donaldson
To avoid tissue and electrode damage during neural stimulation electrodes must remain within safe potential limits. In addition, stimulation is balanced by current reversal to discharge the electrode and reverse the electrochemical processes at the electrode. If, after a first negative or positive pulse, the current is not reversed and the electrode is left open circuit slow discharge takes place. This project aims to understand the processes occurring during slow discharge of stimulating electrodes.
During this project you will:
Test a current controlled stimulator designed by a previous student.
Investigate the slow discharge following stimulation pulses at a range of pH.
Investigate the role of diffusion on the slow discharge following stimulation.
Indirect x-ray detectors for medical imaging: comparison between commercial flat panel and custom-built camera
Supervisors: Marco Endrizzi, Lorenzo Massimi
Indirect conversion detectors are widely used in X-ray imaging which is nowadays a key element in many diverse applications, ranging from industrial non-destructive testing to the medical and biomedical fields.
This project involves a complete characterization of a commercial detector based on this technology. It will include classical parameters such as noise and spatial resolution, but also more sophisticated and method-specific ones like signal spill-out between adjacent pixels and detector response as a function of energy, modulation transfer functions and detective quantum efficiency. Subsequently an X-ray camera will be custom-built by the student, which will entail conceptual design and integrations of the various components. Once the custom camera is built and fine-tuned, it will be characterised by means of the above mentioned quantitative metrics and directly compared to the state-of-the-art commercial solution.
This project is an excellent opportunity to learn fundamentals of detectors and image analysis as well as for a direct hands-on experience in the design and realisation of a custom X-ray camera.
Advanced reconstruction methods for laboratory-based phase-contrast tomography at high-resolution
Supervisors: Marco Endrizzi, Jinxing Jian
X-ray Computed Tomography enables the non-destructive visualisation of the internal structure of samples, with high resolution. Crucial to many diverse fields, from airport security to medical diagnosis, X-ray CT is conventionally based on absorption contrast. In many cases of interest, like for example soft tissues discrimination, absorption is not a reliable source of contrast and phase-effects can instead be exploited to produce an image.
This project will focus on the reconstruction of three-dimensional images from data acquired with our custom-built X-ray phase-contrast scanner. The student will learn the basics of radiography and computed tomography and how phase contrast imaging can complement the conventionally available contrast channel. Subsequently, working on simulated and real experimental data, a dedicated data processing workflow will be developed in such a way that all the system characteristics are included and accounted for in the reconstruction. It is expected that this will lead to a very significant improvement in the achievable image quality.
This project is an excellent opportunity to learn fundamentals of X-ray imaging and tomography and to gain hands-on experience in designing and implementing dedicated reconstruction algorithms that can take an imaging system's performance to the next level.
Sample-dependent scan parameters for x-ray phase contrast computed tomography
Supervisors: Charlotte Hagen, tbc
X-ray phase contrast imaging (XPCi) stands for a class of radiographic imaging techniques, which, in addition to x-ray attenuation, are sensitive to phase shifts. This is especially beneficial for the imaging of weakly attenuating biological tissues. In an XPCi radiograph, phase and attenuation information are typically present simultaneously, and can be separated via a procedure called phase retrieval. We know from theory that different types of samples require slightly different scan parameters for achieving the best possible image quality in a phase contrast tomogram (3D phase contrast image); however, we have not yet validated this theory. The student will aim to fill this gap by simulating and comparing phase contrast tomograms of different sample materials taken under different experimental conditions, analyse the results in terms of key image quality metrics such as contrast-to-noise ratio and spatial resolution, and inform the choice of scan parameters for specific imaging applications. This will be of crucial importance in several medical and biomedical areas requiring that the best possible image quality is achieved, e.g. tissue engineering, and in-theatre imaging of excised breast tissue.
The project will be based on simulations (a simulation code implemented in Matlab will be provided), with a possibility of verifying results experimentally. Skills required: knowledge of medical imaging with ionising radiation, image quality metrics, image analysis, programming in Matlab
Mechanical Design of a Brain Implant
Supervisors: Nick Donaldson and Ahmad Shah Idil
We are developing a brain implant to treat epilepsy. The implant uses LEDs which will be bonded to their connection pads. The device will then be encapsulated in silicone polymer which is intended to prevent leakage currents flowing in parallel to the LED due to liquid from the body. The polymer must remain adherent to the surfaces so that there are no voids into which water can condense. If there is too much stress in the polymer after curing, the adhesion may fail, voids will form, and failure will occur. This type of failure is particularly likely underneath the LEDs. To develop our design, we can use a COMSOL model but without some validation, we would not be confident that the COMSOL model is realistic.
This project is to create a scaled up physical model which will validate the COMSOL model and we hope will give confidence that COMSOL is realistic at real scale. The scaled-up physical model will be made from alumina diaphragms, on which a strain gauge Wheatstone bridge has been formed from thick-film resistor paste. The diaphragms will be calibrated by varying differential gas pressure. They will then be fixed to the ends of short lengths of glass tube, have some silicone poured on top and cured. Many of these will be made. We want to know how the mean direct stress (= pressure) after curing depends on: depth of rubber, cure temperature, and modulus of the rubber. When these empirical relationships are known, they can be compared with results from COMSOL.
A home-made Humbug
Supervisors: Nick Donaldson & Lynsey Duffell
Whether recorded in a neuroscience or clinical situation, biopotential signals, such as ECG or EEG, are often contaminated by mains interference. What this means is that superimposed on the signal is a periodic waveform, often far from sinusoidal, but with a period of 50ms, and time-locked to the mains live voltage. Humbug is the trade name of a device that removes this interference. The signal is routed from the amplifier through a Humbug before being digitised or displayed. The commercial devices work very well. However they cost well over £1000 each so are often too expensive, certainly where many signals are being recorded simultaneously.
This is a project to develop an alternative using a microprocessor. The device should have the contaminated signal and the mains as inputs, and the cleaned signal as the output. The project will be useful if it is embodied in an actual device that can be used in research involving biopotential recording.
Developing a Background Phase Removal Technique for Magnetic Resonance Imaging (MRI) Using Deep Learning
Supervisors: Karin Shmueli, Anita Karsa
The MRI signal has two components: magnitude and phase. Although clinical MRI traditionally uses the magnitude image, the MRI phase is being increasingly exploited as it contains useful and complementary information. For example, the phase map can be used to calculate the tissue magnetic susceptibility, a property that is directly related to the concentration of pathophysiologically relevant components in the body such as iron, calcium, or deoxyhaemoglobin in the blood. To accurately measure the subtle susceptibility variations within the body using a method called Quantitative Susceptibility Mapping (QSM), background phase removal is necessary to eliminate the background phase variations induced by the large susceptibility difference between the body and the surrounding air. Existing background phase removal methods are based on various assumptions about the susceptibility distribution that are often violated, especially near the surface of the tissue where all techniques tend to be inaccurate. The aim of this project is to numerically simulate realistic background phase variations in the body and use these to train a convolutional neural network to perform accurate and robust removal of the background phase. This new, deep learning-based method will then be compared with existing background phase removal techniques. This project combines the emerging and exciting research areas of MRI susceptibility mapping and deep learning and will also give you hands-on experience in MRI image processing, and MATLAB/Python programming.
Design and test of a thermal manikin of human torso
Supervisors: Pilar Garcia Souto
Studies of thermoregulation and thermal comfort often use thermal manikins for data collection instead of real people. These manikins have become highly sophisticated and good at representing how humans interact thermally with the environment. However, they are very expensive and full size which is no always necessary. This project aims to design, develop and test an alternative device. This device should be relatively small and be representative / mimic the thermoregulation of the human torso, particularly the skin temperature. Testing will include the use of a thermal camera. This is mainly a design and experimental project.
What changes my circadian rhythm?
Supervisors: Pilar Garcia Souto
This is an experimental project that investigates factors that might affect the circadian rhythm of the core temperature. The student undertaking this project will select some parameters of interest, but some could be: type of working patterns such as 9am to 5pm versus other shifts, level of fitness activity, parents with young babies versus pregnant women versus other adults, etc. Data will be collected using new temperature monitoring devices that allow us to record the core temperature and heart rate from volunteers remotely and for space of 24+ hours. The project might also involve some work with arduinos.
What temperature I am measuring?
Supervisors: Pilar Garcia Souto
Physics tell us that measuring temperature accurately is challenging, as once you introduce a thermometer onto the system to measure it, you are already changing its temperature. A particularly challenging case is measuring the skin temperature, which is relevant to many biomedical and medical applications. This project aims to critically test and analyse a commercial temperature sensor for surfaces that claims to be very accurate, and investigate the effect that it has onto the surface temperature itself. Part of the testing will be performed using a thermal camera. This is mainly an experimental project and involves the use of arduinos and some basic electronics.
Predicting cortical regional tau levels from structural MRI
Supervisors: Andre Altmann, Frederik Barkhof
The two hallmarks of Alzheimer's disease (AD) are the buildup of amyloid plaques and neurofibrillary tangles of the tau protein in the brain. The distribution of both pathological features can be quantified in vivo through expensive and invasive PET imaging. In this project we aim to predict regional levels of tau (as quantified through PET) from available structural MRI scans using machine learning.
Comparison of Brain Masking Methods for Application in MRI Quantitative Susceptibility Mapping
Supervisors: Karin Shmueli and Russell Murdoch
Project Description: Quantitative Susceptibility Mapping (QSM) is an emerging magnetic resonance imaging (MRI) technique which can be used to obtain clinically useful information on the composition of tissues within the body. A key step in calculating QSM in the brain is obtaining an accurate brain mask. With a number of different masking methods available, the effect of different brain masks on QSM magnetic susceptibility values has not been comprehensively studied. The objective of this project is to investigate the effect of different brain masks on QSM susceptibility values and optimize the brain masking process in QSM. The student will use Matlab to process MRI images from two clinical studies, gain experience using a number of key medical imaging software programmes, including FSL and SPM, and have the opportunity to develop a novel brain masking approach.
Experimental evaluation of an optical method for detecting stroke in newborn infants
Supervisors: Jem Hebden, Adam Gibson
Student: Arihant Bijjala
Optical techniques are being developed at UCL as a means of detecting evidence of stroke in the newborn infant brain. Stroke is caused by blockage of one of the main arteries in the brain, and can result in lifelong disability. It usually occurs around the time of birth, with an incidence of about 1 in every 2500 infants. Currently it is difficult to make a diagnosis of stroke within hours of the event, which is necessary in order to develop treatments to minimise the occurrence of brain injury. Diffuse optical tomography (DOT) involves shining near-infrared (NIR) onto the scalp, which can penetrate several centimetres into the brain where it is strongly scattered, and absorbed by blood. Light scattered back to the surface carries information about the distribution of blood in the brain and its oxygenation. The aim of this project is to investigate whether measurements of photon flight-times in the head can be used to detect stroke. Initially the student will construct a so-called “phantom” – a resin block which has optical properties matched to those of the infant brain, and which contains regions which simulate the presence of a stroke. The project will then involve performing measurements on the phantom using a sophisticated DOT system, and then evaluate various methods of detecting the stroke regions.
Development of a non-invasive tissue impedance measurement devices for estimation of bladder volume
Student: Anton Kavaldzhiev
Supervisors: Sean Doherty and Anne Vanhoest
A device able to accurately estimate bladder volume could have numerous clinical applications, including in chronic management of bladder dysfunctions, though there is no non-invasive method of measuring bladder volume available that is appropriate for daily use. Measurement of the body’s changing impedance during bladder filling is a method that holds promise.
This project aims to enable future clinical research by developing a system to measure tissue impedance non-invasively, suitable for research use, in human participants, by researchers. Specifically, it aims to deliver a method of measuring the impedance of a cross-section of tissue using 4 surface electrodes to record measurements and display them on a Graphical User Interface. The project will build upon an existing prototype, aiming to better characterise the circuit, develop the design and implement it onto a PCB. The design must be safe for use on humans and be tested appropriately to ensure this.
Developing a Game Based Trainer for Ultrasound or Keyhole Surgery
Supervisors: Stephen Thompson, Matthew J. Clarkson
We have recently developed a software based ultrasound simulator (SNAPPYSonic https://pypi.org/project/snappysonic/) that has been used for public engagement events. This project will investigate ways to build on this to develop game based clinical trainers, for ultrasound and/or keyhole surgery.
This would suit a student with an interest in clinical education to design the trainer. Alternatively a student with an interest in software development in Python could help develop the software.