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Sample-dependent scan parameters for x-ray phasOptimisation 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

Student:    Rachel Ng

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

Student: Rita Kharboush

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 

Student: Barnaby Pickering

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

Student: Karol Murawski
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

Student: Regan Lee

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

Student: Maksym Kudryashov

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, Alessandro Olivo

Student: Fatimah Zachariah Ali

 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

Student: Maimuna Mohamud

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

Student: Nirusa Uthayasooriyan

 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

Student:  Artur Tokarski

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

Student: Oriana Arsenov

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 and Jem Hebden

Student: Lamees Arbi

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 and Jem Hebden

Student: George Meeran

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

Student: Ruxandra Ion
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 

Student: Iman Bouharrat

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.

Measuring optical attenuation in polymers used in biomedical ultrasound sensors

Supervisors: Jamie Guggenheim and Paul Beard 

Student: Yuanyuan Lyu

 The aim of this project is to experimentally measure the amount of optical attenuation in Parylene, a polymer used in specialised optical ultrasound sensors.

 UCL has pioneered the use of highly sensitive ultrasound sensors based on optical interferometers; devices trap light inside a thin, (nearly) transparent film[1,2], in this case made of Parylene. We suspect that optical attenuation in Parylene could play a critical role in limiting the sensitivity of these devices but this is difficult to assess further because the attenuation properties are currently unknown. It is therefore of interest to measure them.

 For many materials, optical attenuation can be measured easily; simply by recording how much light is transmitted through a sample and computing the loss per unit distance. However, this requires a sample that is sufficiently thick as to cause a significant (accurately measurable) optical loss. This presents a challenge for Parylene as it is very challenging to fabricate films that are thick enough to exhibit appreciable losses.

 To address this challenge this project will investigate measuring optical attenuation in Parylene in a new way. This will be achieved by applying a newly developed method involving measuring the optical transmittance of multiple films simultaneously by stacking them together inside a liquid filled cuvette. This enables extending the optical path within the material thereby increasing the sensitivity of attenuation measurements.

 This is a predominantly experimental project that will involve carefully following an established measurement protocol, making a large number of experimental measurements, learning to use a commercial spectrophotometer, handling and arranging delicate polymer/liquid samples and assessing and visualising results using Matlab. The student should ideally already have some knowledge of optics, some experience of programming in Matlab and an aptitude for making rigorous experimental measurements.

Monitoring microvascular blood flow with a dark field imager in cirrhosis patients

Supervisors: Terence Leung and Raj Mookerjee

Student: Arshiyah Jeelani

Liver disease is the 5th leading cause of death in Europe and there are currently more than 1.5 million advanced cirrhosis patients in need of regular monitoring, as currently the mortality stands at 170K deaths a year. The deterioration of cirrhosis (decompensation) often leads to a change in microvascular blood flow, among other physiological changes. The aim of this project is to measure microvascular blood flow in cirrhosis patients using a handheld dark field imager and a smartphone, respectively, in order to provide an early warning of a declining condition so that timely intervention can be enacted. The student will develop Matlab programs to capture blood flow information from video clips collected from the UCL Institute for Liver and Digestive Health, Royal Free Hospital. The project is suitable for a student who is interested in data analysis, optical imaging and Matlab programming.

Detecting anaemia in pregnant women using smartphone photography

Supervisors: Terence Leung, Judith Meek and Sara Hillman

Student: Precious Gestopa

Anaemia is a condition in which the haemoglobin concentration in the blood is reduced, impairing its capacity to transport oxygen. The World Health Organisation (WHO) estimated that anaemia affects a quarter of the world’s population. Pregnant women are especially susceptible to developing anaemia. Severe anaemia in pregnant women is also associated with a low birth weight of the baby, stillbirth and newborn death.  Once the condition has been identified, most cases of anaemia can be treated effectively. We are developing a smartphone-based technique to diagnose anaemia based on the colour of blood vessels in the eye area. The student will have the opportunity to join our multinational team made up of clinicians and scientists from the UK and India, and participate in the development of a non-invasive imaging technique for screening anaemia in pregnant women. This project will suit a student who is interested in global health, signal and image analysis, statistics and digital photography.

Development of a smartphone app to screen for neonatal jaundice

Student: Josephine Windsor-Lewis

Supervisors: Terence Leung and Judith Meek

Neonatal jaundice is a common condition among newborn infants, caused by an increased bilirubin level in the blood and tissues. Bilirubin is a yellow breakdown product of haemoglobin and jaundiced infants can therefore appear to have yellow colouration in their skin and sclera. In collaboration with University of Ghana, Korle Bu Teaching Hospital and Greater Accra Teaching Hospital, we have developed a smartphone app to screen for severely jaundiced newborns with promising results. In this project, the student will revise the existing jaundice app to improve its user friendliness and incorporate new functionalities. This project will suit a student with app programming experience. 

Building a smartphone-based ophthalmoscope

Supervisors: Terence Leung, TBC

Student: Adnan El-Gendi

Besides being the window to the soul, the eye also allows access to the vasculature (blood vessel pattern) that can reveal pathologies, providing useful diagnostic information. For instance, the vasculature of the retina of a diabetic patient is often different from that of a healthy person and can potentially be exploited for early diagnosis. The aim of this project is to develop a smartphone-based ophthalmoscope to capture the vasculature in the eye, benefiting from advanced functionalities available in a smartphone camera, e.g., ultra-high definition (UHD) resolution, macro photography and optical image stabilisation. This project will suit a student who is interested in geometric optics, optical imaging, instrumentation and smartphone photography. The student will have the opportunity to learn about eye imaging systems in Moorfields Eye Hospital.

Estimating the stresses in the encapsulation of a brain implant using COMSOL

Supervisors: Nick Donaldson & Ahmad Shah Idil

Student: Sophie Edwards

In the CANDO project, a brain implant is being developed to treat intractable epilepsy by optogenetics. The implant will have 64 very small LEDs and will be surgically implanted into the affected cortex. The reliability of the device depends on the polymer encapsulant remaining bonded to the LED to prevent ionic currents short-circuiting the LEDs and causing corrosion. The longevity of the bonds will depend on the stress which remains after curing (cooling after hot cure) and that, in turn, depends on the geometry (shape), material properties and temperature profile used for curing. The sizes are very small: the LED is about 200 microns long, and the geometry appears to be very unfavourable, so the success of the CANDO project depends on whether this design is viable.

The aim of this project is to use COMSOL to estimate the stresses in the encapsulant at two regions of the implant: (i) around each LED; (ii) between the forks in the head.

The project should comprise the following stages. (a) Learning to use COMSOL. (b)  Making measurements on the encapsulant (MED6015) to find its relevant material properties (e.g. Young’s Modulus). (c) Making test structures incorporating 6015 which can be loaded and the change measured (for example, in shape). This change will then compared to a COMSOL simulation using the material properties that were found. This is a validation test. (d) Modelling stresses in the implant for the given design and for several temperature cure profiles.

Modelling light transport in the canine brain for near-infrared spectroscopy 

Supervisors: Gemma Bale and Robert Cooper

 Abstract: Near-infrared spectroscopy (NIRS) is a non-invasive optical technique that can measure brain activation via changes in haemoglobin oxygenation. It has been successful in human neuroscience studies as it can be used on difficult to study populations, such as babies, or in situations that are realistic, such as in real-world interactions. There is an interest in developing a NIRS device that can monitor the dog brain in a natural way for neuroscience and preclinical studies of disease. However, there are challenges to using NIRS on dogs, partly because their anatomy is very different to humans and also because of the large variation between breeds.

This project will lay the groundwork for the development of a dog NIRS system. In order to create the design specification for the device, an understanding of how near-infrared light will travel through the dog brain is required. Specifically, the student will use structural MRI scans of a range of dog breeds, segment the MRI into tissue types (e.g. skull, muscle, brain, etc.), assign each tissue optical properties, and generate 3D mesh models. These models will be used for simulations of light transport to identify the regions of the brain which NIRS could be sensitive to, per breed of dog. This data will create a set of specifications for the design of a new device. Further work may include pilot studies on dogs with existing equipment and/or the development of designs for a novel device.

The student will need to be enthusiastic to work on a computing-based project, and will preferably be proficient in MATLAB or other programming languages.

Functional brain assessment of newborns using broadband NIRS
Supervisors: Ilias Tachtsidis and Gemma Bale
Student: Georgina Leadley
A lack of oxygen supply to the brain during birth can have disastrous effects on newborns that can last for the rest of their lives. If the brain doesn’t receive enough oxygen or blood during birth, a type of brain injury known as hypoxic ischemia (HI) can occur, which in the worst cases can result in fatality. In some infants it is immediately detectable, but in others it can go relatively unnoticed until later in life. The clinical  manifestation of the issues associated with this type of brain injury is known as neonatal encephalopathy and can range from cerebral palsy to cognitive and behavioural issues. There is therefore a strong incentive to detect any signs of brain injury as early as possible so that some kind of intervention can occur.
To answer this we have developed optical technologies based on near-infrared spectroscopy or NIRS that can measure non-invasively the changes in brain oxygenation (haemoglobin oxygenation) and metabolism (the mitochondrial enzyme cytochrome-c-oxidase). In the healthy brain during functional activity we expect an increase in oxy- haemoglobin (HbO2) and a decrease in deoxy- haemoglobin (HHb), as measured by functional NIRS. Recently, we have demonstrated in adults and 6-8 month old infants that using broadband NIRS (bNIRS) we see an increase in the oxidation of cytochrome-c-oxidase (oxCCO) during brain activity that does not always coincide with the haemoglobin response [see recent review Bale et al. Journal of Biomedical Optics 2016]. We are currently running a pilot study using a multichannel bNIRS instrument to monitor the brain activity of infants with hypoxic-ischaemic encephalopathy (HIE) during a passive motor cortex movement. The student will familiarise themselves with the multichannel broadband NIRS equipment and assist
in the data collection and data analysis which will involve some image processing and statistical tests.

Implementation of a 3D Magnetic Tracking System for Spatial Registration of Neuroimaging Devices

Supervisors: Rob Cooper, Ernesto Vidal

Student: Aman Ganglani

Registration of the location of the sensors used by non-invasive neuroimaging methods (such as EEG, fNIRS) to appropriate models of the head and brain anatomy is critical to the production of accurate images using these techniques.

The advent of wearable electronic sensors for neuroimaging opens a new path to optimize and automate this registration process.  Motion sensors have the potential to allow these neuroimaging devices to provide intrinsic registration of their 3D location in real time.

In this project, the student will be responsible for designing and implementing a proof-of-concept magnetic tracking system for registration of wearable neuroimaging sensors. The student will gain knowledge in fields including neuroimaging, magnetic field modelling, and optical electronics and inverse problems.  They will have the opportunity to develop useful skills including programming (Arduino, Matlab), the use of microcontrollers and circuit design. They will also gain significant practical experimental experience. These skills will be applicable in a wide range of future studies and careers.

 Approximate Project Outline:

  • Review literature of magnetic tracking methods, pulsed DC tracking, magnetometers (W1-W2)
  • Familiarize yourself with data output of Invensense MPU chip, magnetometer data sheets and performance (W1-W2)
  • Produce set-up to acquire magnetometer data in real time using development boards (W2-W4) and programmable magnetic field generator
  • Using 3D printing methods, design and develop measurement jig to allow precise positioning of magnetometer relative to field generator (W4-W8)
  • Create detail dataset that encapsulates the measurements obtained from the magnetometer in the field as a function of field generator settings, driving current, position in jig, time, board, etc. (W8-W12)
  • Use this dataset to investigate inverse solutions that yield position in the field given knowledge of the field and measurements taken from the magnetometer (W12-W16)
  • Integrate these solutions to create a prototype, real-time, 3D positioning system

Endoscopic ultrasound workflow recognition using deep learning

Supervisors: Ester Bonmati, Yipeng Hu and Dean Barratt

Endoscopic ultrasound is a minimally invasive procedure that allows clinicians to look inside human body by introducing a flexible instrument through the mouth to the stomach. Ultrasound images from a small transducer, placed at the tip of the instrument, is important for a number of clinical applications, such as diagnosis and treatment of kidney and pancreas cancer. This however is a complex procedure that requires extensive skills and experience from the clinicians. Context-aware computer assistance has the potentials to provide useful information to help the operator by automatically recognising the stages of the procedure, such as preparation, insertion, diagnosis, needle insertion and retrieval. This, in turn, can help the operator to navigate, to predict the remaining procedure time and to evaluate procedure performance. The aim of this project is to develop a fully-automated computer algorithm for this purely image-based classification task, using modern artificial intelligence methods with deep learning. During this project you will 1) gain insight in endoscopic ultrasound imaging; 2) implement a deep neural network using Python and TensorFlow; and 3) train the network and analyse the results.

In this project, you do not need to have prior experience with interventional ultrasound imaging (though maybe beneficial), but you will need good coding skills in Python.

Multi-vessel segmentation on laparoscopic ultrasound for liver resection using deep learning

Supervisors: Ester Bonmati and Joao Ramalhinho

Laparoscopic Ultrasound imaging (LUS) is frequently used to guide minimally invasive procedures such as liver resection by imaging blood vessels and tumours. However, the reduced field of view and low signal to noise ratio of LUS images complicates their interpretation and subsequently surgical guidance. A possible solution for this problem is to provide the surgeon with the location of major vessels in LUS images such as the portal vein and hepatic vein. Recent advances in machine learning have led to the development of fully automatic methods that are able to successfully define regions of interest such as vessels on ultrasound images. The aim of this project is to detect and segment multiple vessels on a LUS image that will be useful for guidance during liver resection. During this project you will 1) gain insight into laparoscopic ultrasound and liver resection, 2) implement a neural network using Python and TensorFlow, 3) train a network to identify multiple vessels on ultrasound images, 4) evaluate the segmentation results by comparing the automatic and manual segmentations and 5) draw conclusions from your work.

In this project, you do not need to have prior experience with interventional ultrasound imaging (though maybe beneficial), but you will need good coding skills in Python.

Estimating the risk of second cancers after radiotherapy for paediatric patients

Supervisors: Catarina Veiga and Yaru

Student: Sophie Taylor

Childhood cancer treatment has become relatively successful, and nowadays 75% of the patients survive for 10 years or more. Radiotherapy (RT) had a fundamental role in improving outcomes, but with increasing survival the late treatment-induced side-effects also become more important. Namely, ~20% of survivors are expected to develop new primary cancers decades later which were induced by the RT they received during their childhood.

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 of childhood cancer patients with segmentations of organs at risk of developing second cancers. These are required to apply existing mathematical models of second cancer risk to estimate the lifetime absolute risk of different RT modalities (i.e., conventional RT, intensity modulated-RT and proton therapy). The aim is to help identifying the best RT modality to individual patients.

Investigate the derivation of a marker of mitochondrial pressure passivity that is related to outcome in infants with brain injury

Supervisors: Ilias Tachtsidis Tachtsidis and Subha Mitra 

Student: Mariya Tariq

Perinatal cerebral hypoxic-ischemia (HI) is a condition resulting from reduced oxygen delivery and blood flow occurring either in around the time of birth. It occurs in 1 to 2 per 1000 live births and can result in significant mortality and morbidity. Although significant advances have been made in the understanding of changes in brain physiology during this period and in the treatment of asphyxiated babies, the pathophysiological changes are yet to be fully explored. It is extremely important to assess the changes on brain perfusion and metabolism following HI to develop future neuroprotection strategies. This project is part of an exciting collaboration between the neonatal neurology and neuroprotection group in the neonatal unit in UCL/UCLH and the Biomedical Optics Research Laboratory at UCL that aims to deliver novel measurements to investigate brain physiology and neuromonitoring through combination optical (broadband near infrared) technologies along with magnetic resonance imaging and spectroscopy biomarkers. Both these groups are world leaders in their respective fields and is continuously contributing towards the advancement of optical neuromonitoring methods and future neuroprotection.

We have recently published [1] that using our novel broadband NIRS derived metabolic reactivity index, we can identify infants with severe injury at the cot side early in life. It would be interesting to review the same dataset to identify the minimum period of recording that will give us the same identification of severity of illness. We would also like to include our off line data processing technique (wavelet analysis) into the online live monitoring software.  Any previous experience with MATLAB software will be an advantage.

 Reference (further reading)

 1. Mitra et al. Pressure passivity of cerebral mitochondrial metabolism is associated with poor outcome following perinatal hypoxic ischemic brain injury. Journal of Cerebral Blood Flow & Metabolism 2019 Jan;39(1):118-130. doi: 10.1177/0271678X17733639

Detection of bladder events from single-channel pressure data to enable conditional neuromodulation

Supervisors: Sean Doherty and Anne Vanhoestenberghe

Following neurological injury, there is a high prevalence of bladder dysfunction. This often leads to overactivity of the bladder muscle and urinary incontinence. By measuring pressure in the bladder we can detect these events, however traditionally bladder pressure has required concurrent measurement of abdominal pressure to remove noise, something undesirable for long-term implantable sensors. This project therefore will investigate methods of automatic event detection from single-channel pressure data that could be obtained through an implantable sensor.

You will use anonymised data from past urodynamic studies to develop and test event detection algorithms. During your project, you will propose a series of approaches based on literature and clearly implement selected approaches in Matlab for testing. This project will suit someone with an interest in signal analysis and with previous experience of Matlab or other programming.

X-ray Generator Digital Interface

Supervisors: Robert Moss and JC Khong

In a laboratory, medical or industrial environment X-ray generators are common and are used primarily for imaging of people and objects. 

This project is about developing a digital interface for an X-ray generator in the Radiation Physics Laboratory.  There is a requirement to design and build hardware and to develop software to control the generator.  The hardware will initially be built on a breadboard but should rapidly be developed so that the project outcome is a PCB or Arduino shield that is robust and reproducible.  The software must allow the user to set generator parameters, such as voltage (kV) and current (mA); turn the X-ray on and off; and provide feedback on the generator status. 

In the UK there are strict rules that govern the use of X-ray, specifically the Ionising Radiations Regulations 2017 (IRR17).  IRR17 demands certain level of protection to avoid accidental exposure to X-ray which may be harmful.  These are called controls and include shielding, door switches and procedures/training.  According to IRR17, the X-ray generator must be ‘interlocked’ meaning that all the controls must be in place before X-rays can be emitted.  The Radiation Physics Laboratory has a new state-of-the-art interlock system and the digital interface being developed here must be compatible.

The project is firmly rooted in instrument design and will provide an excellent opportunity for anyone who wants to pursue a career in the high tech industry or in physics or engineering research.  Any students interested in this project should arrange a discussion with the project supervisors.


Dedicated, resourceful and self-directing; confident in electronics and associated skills; willing to learn Arduino programming for instrument control; experience with Matlab (or similar) essential; good laboratory skills (work is clean, tidy and reproducible); willing to make contacts outside the supervisory team where necessary.  A significant time commitment is expected and will be monitored by the supervisors.

System for In-Shoe Temperature Monitoring of Feet

Supervisor: Prabhav Nadipi Reddy and Eve Hatten

Diabetic foot ulcers are a major complication of diabetes that reduce the quality of life of people significantly. Monitoring foot temperatures of diabetic people is a possible means of predicting ulceration. This project seeks to develop an insole with embedded temperature sensors that would record foot temperatures of people as they walk. The project involves building a sensor system along with appropriate data acquisition to record and store the data.

This project will suit an engineering student who is interested in electronics, programming, product design and medical device development.

Wireless Data Acquisition System for Biomedical Signals

Supervisor: Prabhav Nadipi Reddy and Eve Hatten

The aim of the project is to build a wireless data acquisition system based on an Arduino system that could record different biomedical signals. We are hoping to develop a recording system that can be used as an off-the-shelf product for making different kinds of sensor systems.

The project would suit an engineering students interested in building electronics, programming, product design and medical device development.

Estimating Time Spent in Near and Far Work in Children

Supervisor: Prabhav Nadipi Reddy and Eve Hatten

The time spent in doing near work (defined as work in which children are looking at objects less than 50 cms from their eyes) vs. far work is a predictor of prevalence and progress of myopia in children. In this project we want to develop a sensor system to estimate the time children spend doing near and far work. The project would involve selecting sensors, developing appropriate signal conditioning for the sensor and testing the sensor system to prove its efficacy.

The project would suit an engineering students interested in building electronics, programming, product design and medical device development.

Investigating automised image quality assurance in radiotherapy
Supervisors: Esther Baer and Alison Warry 

Description: 'At UCLH, cone beam CT (CBCT) images are acquired before the delivery of a radiotherapy treatment to verify the positioning of the tumour and the treatment field. CatPhan is a physical phantom that can be used to assess the quality of images acquired on CT and CBCT scanners.  It is used at UCLH to perform quality assurance (QA) of CBCT, and will also be used with the new dual energy CT scanner, due to be installed in November 2019. Currently images are evaluated manually which is slow and leads to intraoperator variability. 
PyLinac is free software designed to automate Radiotherapy image analysis, and includes a module for analysis of the CatPhan. The PyLinac CatPhan module automatically registers the images of the phantom, verifies HU uniformity and linearity, geometry/scaling, calculates slice thickness, measures spatial resolution (reported through MTF) and low contrast resolution. However, its use within the clinic requires verification of the results and development of a user friendly interface between Python and an Access database that would be used for long term trending of results. Furthermore, there are some measures, such as the circular symmetry and the point spread function, which are useful but not currently reported by the software, and it requires modifications to work with the latest model of the CatPhan.
This project will help develop the tools and modification required to use PyLinac clinically and verify that it is suitable for this task. It will require good coding skills in Python.’

Motion models for an improved motion estimate for RT planning and delivery

Supervisors: Björn Eiben, Elena Tran, Jamie R. McClelland

4D Computed Tomography (4DCT) has become an invaluable tool for the planning of radiotherapy (RT) for lung cancer patients. However, it has also been shown to underestimate the true motion of the tumour during RT treatment preparation and delivery – which may result in suboptimal treatment. Our group has developed computational methods to build patient-specific respiratory motion models and at the same time reconstruct the internal anatomy of the patient. This method should be able to estimate the true motion of the RT target better than conventional imaging techniques, i.e. 4DCT and 4D cone-beam CT (4DCBCT). The aim of this project is to first use a computational anthropomorphic phantom (XCAT) to simulate the 4DCT acquisition and reproduce the effect of motion underestimation and to quantify this effect with respect to the true motion produced by the phantom. Thereafter, the unsorted data from the 4DCT acquisition will be used to build a motion model, reconstruct a motion-compensated image of the simulated anatomy and then quantify if and to which extend the motion model can overcome the motion underestimation. Depending on the progress of the project, it could be extended to also include 4DCBCT, different motion traces, tumour locations etc. There is also the possibility of comparing the motion estimated by 4DCT and the motion models on real data from RT patients treated at UCH. Requirements: The project is suitable for a student with solid computational skills in Matlab and/or Python. 

Surface EMG based control of a Robotic Hand

Supervisors: Prabhav Nadipi Reddy and Kirill Aristovich

The aim of the project is to develop an EMG based system to control a robotic hand. Starting with simple one channel EMG control, the student would find ways of controlling multiple movements of the artificial hand. This could be either by increasing the number of channels of EMG or using signal classification methods depending on the student's inclination.

The project will suit an engineering student who is interested in electronics, programming, product design and medical device development.

HRV analysis (ECG Recording)

Supervisors: Prabhav Nadipi Reddy and Henry Lancashire

Student: Jack Van-Loo

This project will suit an engineering student who is interested in electronics, programming, product design and medical device development.The heart rate of a healthy person has certain variability, meaning that the duration between successive heart beats varies slightly over time. In fact, the loss of heart rate variability (HRV) can indicate pathological conditions not necessarily related to the heart itself. It has been shown that a drop of HRV in cirrhosis patients often relates to decompensation episodes which if discovered early can enable improved care management of the patient. The aim of this project is to design an HRV monitor that a patient can use at home. This home monitoring device will consist of ECG amplifiers, a microcontroller (Arduino) and a Bluetooth wireless transmitter, all packaged in a compact enclosure comfortable to wear. This device would, at a later stage, be integrated with the algorithm to calculate heart-rate variability from the recorded ECG.

Automatic contour artifact localisation in radiotherapy planning 

Supervisors: Jamie R. McClelland, Adam Szmul and Pravesh Bhudia

Student: Jack Weeks

One of the key tasks in planning radiotherapy treatment is to delineate the target (tumour, metastasis) and healthy tissues. With Increasing advancements in Radiotherapy treatment techniques, there is an inevitable correlation with the complexity and the increasing quantity of these volumes required in the treatment planning process. Whether created by auto segmentation tools, allied health professionals or clinicians, the quality and accuracy of these intricate structures is of paramount importance to ensuring the radiotherapy plan is delivered precisely and safely. 

The aim of this project is to develop an automatic tool for a contour analysis, with the potential to localise any suspected contour artifacts on CT images. A real-time quality control check has the potential to support the professionals at the pre-planning stage, thus mitigating errors picked up along the patient's pathway. Convolutional Neural Networks (CNNs), and Deep Learning in particular, currently represent state-of-the-art methods for  automatic segmentation and localisation tasks. This project will involve designing, training and evaluating a machine learning method, based on one of Deep Learning techniques.     

The project will be implemented in Python programming language using Keras, an open-source neural-network library. The training of the designed method will be performed on a cluster equipped with Graphics Processing Units (GPUs). These make the project suitable for a student with strong mathematical and computing skills.

Project title: Using unsupervised clustering approaches to detect hormone-induced neuronal remodelling

Supervisor: Jonny Kohl and Andre Altmann

Hormones are powerful modulators of behaviour, but the mechanisms by which they alter the form and function of individual neurons in the brain remain largely unknown. Some of the most extreme hormonal changes occur during pregnancy, and these changes are thought to orchestrate physiological and behavioural adaptations, such as the onset of parental behaviour. We have identified a small population of neurons in the mouse brain that controls parental behaviour. These neurons express the neuropeptide galanin (Gal) and are located in the medial preoptic area (MPOA), an evolutionarily highly conserved brain region (MPOAGal neurons). Intriguingly, MPOAGal neurons are hormone-sensitive and show a pregnancy-dependent upregulation of their activity during parental interactions. Our hypothesis therefore is that pregnancy hormones remodel these neurons permanently, thereby eliciting parental behaviour. We have started to perform patch-clamp recordings from MPOAGal neurons in brain slices, allowing us to characterise them both biophysically and morphologically.    

The candidate will perform unsupervised clustering on recorded MPOAGal neurons, using both biophysical (e.g. membrane potential, firing pattern, input resistance) and morphological parameters. The goal is to (1) morphologically reconstruct recorded neurons, to (2) use clustering to uncover how many different classes of MPOAGal neurons exist and (3) to address whether pregnancy changes the properties of these neurons. This project would make a crucial contribution towards our understanding of how hormonal changes affects neural information processing.   

The project is about 80% computational and 20% wet lab-based, but there is some flexibility, of course. Candidates will be introduced to the conceptual and technical background of each project via the relevant literature and by close interactions with lab members.  However, the student should have existing programming skills, and be able to develop them further in a quite independent way. The programming will be done in Python and/or R.

Project title: Identification of pregnancy-induced structural brain changes

Supervisor: Jonny Kohl and Andre Altmann

Pregnancy has been shown to lead to dramatic, long-lasting structural changes in the brains of humans and rats, as demonstrated by structural magnetic resonance imaging (MRI). We would like to address whether this is also the case in mice, where the availability of genetic tools would allow us to further investigate the mechanisms underlying such volumetric remodelling. We have already acquired structural MRI scans from mouse brains at different stages before, during and after pregnancy. 

This project would use these data and implement a computational / image processing pipeline to (1) build a reference brain (i.e. average brain from all samples) and then (2) register each sample brain to this reference brain. This process – deformation-based morphometry (DBM) – allows to detect morphological differences over the entire brain. (3) In a final step, the resulting deformation maps would be registered onto a standard brain atlas to allow for integration with other types of imaging data. Code exists for essentially all steps of this process, but the goal will be to build an integrated image processing pipeline and to implement these steps to run on a high-performance computing cluster. The student would closely work with my group and the Crick’s High-Performance Computing and Bioinformatics teams. In addition, he/she would be trained in performing MRI scans, and there is the option of acquiring new experimental datasets, depending on interest. This project will reveal whether pregnancy leads to structural remodelling of the mouse brain.

The project is about 80% computational and 20% wet lab-based, but there is some flexibility, of course. Candidates will be introduced to the conceptual and technical background of each project via the relevant literature and by close interactions with lab members.  However, the student should have existing programming skills, and be able to develop them further in a quite independent way. The programming will be done in Python and/or R.

Treatment planning for high-intensity focused ultrasound (HIFU) in the brain
Supervisors: Bradley Treeby, Ben Cox

High-intensity focused ultrasound (HIFU) is an emerging technique that can be used to non-invasively destroy regions of the brain, for example, to treat essential tremor (see https://www.youtube.com/watch?v=PQOJEmBTDuI). Simulations are now being increasingly used to plan HIFU treatments to precisely direct the ultrasound waves to the correct place in the brain. However, these simulations often require detailed knowledge about how to convert medical images of the patient into model parameters, and programming and modelling experience to run the simulation correctly.

Over the last ten years, we have been developing an open-source acoustics modelling package called k-Wave (http://www.k-wave.org). This is widely used by researchers all over the world. However, while relatively easy to use, it still requires users to have some knowledge of programming. The broad aim of this project is to integrate k-Wave with Kranion, an open-source visualisation tool for HIFU in the brain developed by the Focused Ultrasound Foundation (https://www.fusfoundation.org/for-researchers/resources/kranion). Kranion has a powerful graphical user interface, but no modelling capabilities.

The specific aims are:

1. Learn how to use k-Wave and Kranion (individually) by working through the examples and setting up simple simulations.

2. Learn about the input and output file formats used by k-Wave and Kranion.

3. Write MATLAB and/or Python scripts to convert a Kranion output -> k-Wave input, and k-Wave output -> Kranion input.

4. Write scripts that automatically call these conversion scripts, and call k-Wave to run the simulation.

This will allow a user to setup a HIFU treatment in Kranion using its graphical user interface, and then run a treatment planning simulation in k-Wave in the background, without needing to programme or have a detailed knowledge of k-Wave, and then load the results back into Kranion to display.

The project requires a good knowledge of programming in either MATLAB or Python. It would suit an ambitious and motivated student who wants to contribute to the state of the art in treatment planning and HIFU.

The effect of hair on transcranial focused ultrasound therapy
Supervisors: Bradley Treeby, Elly Martin

Student: Frank He

Brain disorders affect as many as one third of the adult population, and present a huge challenge for health services across the world. One exciting treatment alternative to drugs and surgery is the use of ultrasound. Ultrasound waves cause mechanical vibrations that affect the brain in different ways. For example, they can cause the tissue to heat up or generate forces that agitate the brain cells and tissue scaffolding. Several different types of treatment are possible depending on the pattern of ultrasound pulses used. This includes precisely destroying small regions of tissue, generating or suppressing electrical signals in the brain, or temporarily opening the blood-brain barrier to allow drugs to be delivered more effectively. Clinical trials in the last few years have demonstrated that these techniques can be highly effective.

One major drawback of ultrasound therapy in the brain is that the head needs shaving. This can be a significant deterrent to some patients when considering the therapy. The reason for head shaving is to allow the ultrasound to have a direct path to the brain, without travelling through air which may be trapped in the hair. However, there is some evidence that treatments can be performed without head-shaving (https://jtultrasound.biomedcentral.com/articles/10.1186/2050-5736-1-24). The broad aim of this project is to quantitatively determine the acoustic properties of hair, and assess methods for avoiding head shaving in clinical treatments.

The specific aims are:

1. Develop a test rig using two ultrasound transducers (a transmitter and a receiver) to measure the acoustic properties (absorption and sound speed) of human hair.

2. Prepare different hair samples, by attaching wigs of human hair of different types and densities to a backing layer.

3. Conduct a series of characterisation measurements, including assessing how the hair properties change with the gas level by degassing the hair samples before the measurements.

4. As a final step, conduct a series of transcranial ultrasound experiments using human skulls with wigs to demonstrate (a) the measurements agree with the measured acoustic properties of hair, and (b) under which conditions treatments can be performed without head shaving.

The project would suit an ambitious and motivated student who wants a lab-based experimental project, and wants to contribute to the state of the art in ultrasound therapy in the brain.

Estimating blood oxygenation in photoacoustic tomography: a numerical simulation study

Supervisors: Ben Cox & Paul Beard

 Photoacoustic tomography is a novel way to image the small blood vessels in living tissue. It works in the following way: a pulse of laser light of a specific wavelength is used to illuminate the tissue. The light is scattered within the tissue and absorbed wherever there are absorbers. Whenever and wherever the light is absorbed, a small pulse of ultrasound is generated. This emitted wave is detected by an array of ultrasound detectors, and used to form an image. As the strongest absorbers in tissue at near-infrared wavelengths are the oxy-and de-oxyhemoglobin in blood, photoacoustic imaging has, in principle, the capability to obtain accurate images of blood oxygenation status: the ratio of the concentration of oxyhemoglobin to the total haemoglobin concentration, and a quantity that is of great clinical and pre-clinical interest. However, determining the blood oxygenation at a point in a photoacoustic image turns out to be challenging.

 If the image were proportional to the optical absorption coefficient, then a set of photoacoustic images at different wavelengths would be linearly related to the concentrations of oxy- and deoxy-hemoglobin via their known molar absorption spectra. This linear system could then be straightforwardly inverted to find the blood oxygenation. Unfortunately, photoacoustic images are not directly proportional to the optical absorption coefficient but to the product of the absorption coefficient and the amount of light that passes through that point (the light fluence), which is itself dependent on the absorption coefficient. Inverting a set of multiwavelength photoacoustic images for the blood oxygenation is therefore a nonlinear inverse problem, and non-trivial for this reason.

 In the photoacoustic imaging literature, a common way to side-step this problem is to assume that the effect of the fluence is negligible, and that the photoacoustic images are proportional to the optical absorption coefficient. This will result in inaccurate estimates of the blood oxygenation. What is not known is how inaccurate those estimates will be. This is a pressing issue, as oxygenation estimates obtained in this way are being used to make clinical and even preclinical judgements. This aim of this project is to determine how accurate this approach to estimating blood oxygenation is in the case of a single blood vessel, using numerical simulations.

 In this project, photoacoustic images of a single blood-filled vessel within a typical tissue medium will be simulated for multiple optical wavelengths and at several depths. The software used for this will be the light-modelling software packages MCX (http://mcx.space/) and, for comparison, ValoMC (https://inverselight.github.io/ValoMC/). These simulations will use either university computing resources or those of the Biomedical Ultrasound Group. From this set of simulated images, the blood oxygenation will be estimated using various approximate approaches, including ignoring the effect of fluence, to see how accurate the estimates are. The results will be examined in order to determine when, if ever, there are circumstances (choices of wavelengths, depth in the tissue, etc) when the blood oxygenation is accurate.

 This project would be suitable for someone with some knowledge of biomedical optics, an interest in numerical modelling, and a good understanding of how to analyse data sets. Experience with Linux would be helpful for setting up and running the simulations, as well as experience of Matlab, as it will be used to analyse the data. Project students in the Photoacoustic Imaging Group are expected to attend the group meetings every other Tuesday at 13:00, and to meet with their supervisor to report their progress at least once a week.

Numerical investigation of reverberation-based measurements to obtain material properties

Supervisors: Ben Cox and Bradley Treeby

Acoustic reverberation measurements are used in several applications to measure or characterise materials. Resonant Ultrasound Spectroscopy is used to determine the elastic properties of crystals by measuring the modal response and fitting it to a model based on the known dimensions of the crystal. Acoustic Resonance Spectroscopy, which measures an acoustic ‘signature’ for different materials, has been used to differentiate liquids, powders and even types of wood. Reverberation measurements are used routinely in room acoustics to measure the acoustic absorption of a material or of the walls and fittings.

 In this project, a numerical model will be used to examine to what extent it might be possible to use a reverberation approach to estimate the acoustic properties (sound speed, absorption, nonlinearity) of tissue-like materials in the MHz frequency range. If such a method were possible – and it may be that it is not – it would be of great benefit in several areas, not least the characterisation of biological tissue samples and tissue-mimicking materials.

 The initial task of the project will involve learning how to use the Matlab Toolbox k-Wave (www.k-wave.org) which, while relatively easy to use, still requires users to have some knowledge of programming. The first series of numerical tests will be to ascertain whether this software is capable of accurately modelling reflection from walls and therefore reverberation. It be may that an alternative approach to the acoustic modelling will need to be taken, eg. modelling single frequency waves using the finite element method. The acoustic fields in reverberant spaces of various geometries containing materials with tissue-like properties will then be simulated. Approaches to extracting materials properties from this simulated data will then be examined. Initially, low frequency methods based on acoustic modes (similar to Resonant Ultrasound Spectroscopy) will simulated and analysed. Following that, higher frequency regime, where the modes can no longer be easily separated, will be studied, as it is the most practicable regime for biological samples. This second part is heading further into the unknown.

 This project would be suitable for someone who has studied some acoustics or ultrasound,

is interested in numerical modelling, and who has good mathematical skills (helpful, in the first instance, for understanding the literature). Experience with Linux would be helpful for setting up and running the simulations, as well as experience of Matlab, as it will be used to analyse the data. There is an expectation that project students in the Biomedical Ultrasound Group take part fully in the group’s activities, attending and occasionally presenting at the group meetings, Mondays at 12:30, as well as joining in with group social events whenever possible. They are also expected to meet with their supervisor to report their progress at least once a week.

Magnetic field hyperthermia - Human volunteer study

Supervisors: Quentin Pankhurst & Paul Southern

Magnetic field hyperthermia (MFH) is a phenomenon in which specific materials (we use iron oxide nanoparticles) can be made to heat up quickly (say, room temperature to 100 °C in less than a minute) by being placed in a time-varying magnetic field. It is being explored for several biomedical applications, where the ability of magnetic fields to penetrate deep into tissue means that we can remotely heat targets that are far away from the skin, such as having been injected into a cancer tumour. We are working on many different aspects of MFH in the UCL laboratories in the Royal Institution in Mayfair, focusing primarily on the underlying physics of the heat transfer, and the systems engineering of the magnetic field generators. 

The object of this particular project is to build on the outcomes of a successful first-in-human volunteer study in 2018/19, to further understand the non-specific peripheral heating properties of time-varying magnetic fields. The project will involve the preparation of an ethical permissions submission document, which, assuming that consent is granted, will be followed by the volunteer study itself. If consent is withheld, an alternative preclinical study will be developed.

Magnetic field hyperthermia - Thermal modelling with in vitro and preclinical testing

Supervisors: Quentin Pankhurst & Paul Southern

Magnetic field hyperthermia (MFH) is a phenomenon in which specific materials (we use iron oxide nanoparticles) can be made to heat up quickly (say, room temperature to 100 °C in less than a minute) by being placed in a time-varying magnetic field. It is being explored for several biomedical applications, where the ability of magnetic fields to penetrate deep into tissue means that we can remotely heat targets that are far away from the skin, such as having been injected into a cancer tumour. We are working on many different aspects of MFH in the UCL laboratories in the Royal Institution in Mayfair, focusing primarily on the underlying physics of the heat transfer, and the systems engineering of the magnetic field generators. Depending on the students skills, the project will be designed to complement existing work and generate new insight into this important new therapeutic modality.
The object of this particular project is to build on recently developed thermal models of heat transfer in the presence of perfusion-based dissipation, to better understand and model data at hand from both in vitro and preclinical models. The project will involve the design and build of new in vitro phantoms to test and validate the models and, if appropriate, it may involve collaborative work with colleagues in other departments who are undertaking preclinical studies of magnetic hyperthermia.

Biomechanical Modelling Using Deep Neural Networks – Predicting Finite Element Solutions

Supervisors: Yipeng Hu and Mark Pinnock

Student: Shaheer Saeed

During image-guided surgical interventions, critical information, such as target tumour location, changes due to organ motion caused by surgical instruments. This usually complicates and slows down the procedure because uncertainty in identifying the regions of interest is introduced. One popular method to account for this type of spatial change is to predict the patient-specific organ motion during an intervention using methods such as biomechanical modelling of data derived from medical images. However, such modelling methods require highly specialised hardware and software, and are in general still too slow to be used in real-time in the clinical setting. Modern machine learning techniques provide an opportunity to learn the patterns of organ motion from precomputed biomechanical simulations and approximate the simulation results in a way that is several orders of magnitude faster. The student undertaking this project will be working with real clinical applications where biomechanical models are derived by segmenting anatomical structures from patient images. Specifically, the student will gain experience of: 1) running biomechanical simulations to obtain training data; 2) building deep learning models to accept the input of the biomechanical models and predict the simulation results; and 3) training a deep learning model and validating the model prediction on new data. She or he is expected to be comfortable with scientific computing (such as Python and MATLAB) and be prepared to learn, understand, and apply the basic principles and practical aspects of biomechanical modelling and machine learning techniques during the course of the project.

Skills required: Experience of computer programming; strong interest in machine learning and computation  

Image analysis and reconstruction in an X-ray Phase-Contrast Computed Tomography scanner

Supervisors: Marco Endrizzi, Glafkos Havariyoun

Student: Neel Maniar 

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.

A prototype biomedical specimen Computed Tomography (CT) scanner is currently under development based on edge illumination phase contrast imaging. This is part of a trial to assess the specificity and sensitivity of this system compared to the “gold standard” technique currently used at most UK hospitals; absorption based planar (2D) cabinet X-ray imaging. This project will focus on the quantitative image quality comparison of the two systems as well as comparison between images obtained with various reconstruction parameters. Prior to this being done appropriate image quality metrics will need to be defined and an optimisation of the image reconstruction algorithms attempted.

Assessment of perinatal brain injury using broadband near infrared spectroscopy

Supervisors: Subhabrata Mitra and Ilias Tachtsidis

Student: Berin Gorgun

Hypoxic ischaemic encephalopathy (HIE) results from the lack of oxygen and blood supply around the time of birth and leads to significant morbidity and mortality in the neonatal population. In UK, incidence rate is between 1-2/1000 live births but this can be ten times higher in developing countries. Currently the only available treatment is therapeutic hypothermia, which involves bringing down the body temperature to 33.5 degree C from normal (37 degree C) and maintain the cooling treatment for 72 hours, bringing the temperature back to normal. Following these babies are taken to the MRI suite to study the effect of the hypoxic ischaemic injury.  Although MRI is the investigation of choice of assessment of injury and prognostication, MRI studies can only provide information at one time point and generally after completion of the clinical treatment. In contrast, optical methods can provide a continuous information regarding the pathophysiological changes in the developing brain. Human tissue is transparent to near-infrared light and has been extensively been used to monitor infants brain oxygenation and metabolism.

 This project is part of an exciting collaboration between the Neonatal Unit at the University College London Hospitals and the Biomedical Optics Research Laboratory at UCL that aims to deliver novel measurements to investigate the effects of brain injury soon after birth and brain neuroprotection. The main aims of this MSc/MRes project are to (1) assist in experimental data collection in the neonatal unit in UCLH; and (2) analyse multimodal data already obtained from these infants to identify the pathophysiological changes following perinatal brain injury. The student will learn how to operate the optical instrument and use novel software tools that will allow quantification of the optical measurements and then will focus on analysing those in conjunction with other measurements.

 This project would be suitable for a student with an interest in optical technologies, physiology/pathophysiology in newborn brain, brain tissue biochemistry; and will involve data collection, data processing and some statistical analysis.

Development of a computational tool box to study biochemical reactions in microfluidic devices

Supervisor: Radoslav Enchev and Sergio Bertazzo

This project would take place within “The Francis Crick Institute”, which is a big biomedical research centre in London that opened in 2016 as a partnership between Cancer Research UK, the Medical Research Council, the Wellcome Trust, and some universities including UCL.

In particular, the project will be done within a young and dynamic lab at the Francis Crick Institute, combining technical expertise and innovation across the fields of biochemistry, structural biology and engineering to gain hitherto unattainable insights into fundamental biological processes.

Our group is developing a method that allows the direct observation of biochemical processes at atomic spatial- and milliseconds time-resolution by combining microfluidics and cryo-electron microscopy (cryo-EM). We utilize microfluidics devices to mix and incubate biochemical samples on sub-second time scales and then rapidly spray-plunge-freeze the reactants for subsequent three-dimensional structure determination by cryo-EM. Iterating the procedure at increasing incubation times after sample mixing, allows the visualization of a biochemical binding and/or enzymatic reaction as a time-lapse “movie”.

Technically we combine state of the art molecular biology and biophysics techniques with advanced imaging and image processing algorithms, simulations, as well as design and manufacture of 3D microfluidic devices produced by photolithography or additive manufacturing. Part of the lab is further dedicated to mechanical and electrical device engineering.

In this project we seek to complement the experimentally obtained structural biology and biophysical results by simulations. We would use COMSOL and Matlab to develop Multiphysics simulations which would replicate the flow- and pressure-induced phenomena in microfluidic chips alongside the biochemical reaction kinetics. These include diffusive and active mixing of two biochemical solutions, laminar flow and the consequent residence time-distribution, as well as the formation of biochemical reaction intermediates and products in various microchannel geometries and environmental conditions. Equivalent experimental studies will be carried out in the lab, resulting in a fruitful exchange between theory and experiment and yielding great understanding of the frontiers between biology and physics.

 Requirements of the student:

 This project is aimed at students with keen interest in computation.  The student will be required to gain intermediate Matlab/Comsol skills as well as basic understanding of fluid dynamics and enzyme kinetics quite early in the project. We will direct students to the relevant resources, but students should be able to develop skills and gain knowledge quite independently. Most importantly, the student should be keen to learn and contribute to a multidisciplinary field at the boundary of science and technology.

Comparing margin-based approaches with uncertainty scenarios for radiotherapy treatment plan assessment

Supervisors: Sarah Gulliford, Vasilis Rompokos and Jamie McClelland

Student: Oskar Wojtczak

 During  the radiotherapy treatment planning process a series of margins are drawn around the tumour.  The clinical target volume (CTV) includes all tissue which needs to be irradiated. The planning target volume (PTV), is designed to account for geometrical uncertainties and ensure that the sufficient dose is delivered to the CTV.  The concepts of CTV and PTV are an international standard defined by the ICRU.  Radiotherapy dose distributions are reported using PTV to allow for inter comparison.   In proton therapy the approach to reporting dose is different and is less standardised.  The concept of the CTV remains however individual scenarios of delivery uncertainty are assessed.  The two main uncertainties are set up (geometrical) and range (translation of CT data to dose calculation).  The UK NHS proton centres will be participating in a number of clinical trials comparing proton and photon radiotherapy.  It is important to understand if the PTV-based and scenario-based concepts are equivalent.  This project will explore the hypothesis that evaluation of CTV under uncertainty is equivalent to PTV for radiotherapy treatment plan assessment.  Students will be required to undertake radiotherapy treatment planning and develop analysis software written in python.     

Development of multimodal imaging tissue-mimicking phantoms for clinical training of minimally invasive procedures

Supervisors: Efthymios Maneas and Adrien Desjardins

Student: Islam, Tasfia

Guidance of minimally invasive procedures can be achieved with a combination of different imaging techniques such as X-ray (computed tomography / fluoroscopy) and ultrasound imaging. Tissue-mimicking phantoms have been shown to be valuable tools for quantifying imaging performance, and for training of young practitioners. This project involves the development and evaluation of new types of multimodal imaging phantoms using open-source software to extract anatomical structures and generate moulds, 3D printing and novel soft tissue-mimicking materials. Potential clinical applications could include liver resection or stent placement. The students will work closely with our clinical collaborators to test and refine these phantoms.

Sensory systems

Supervisor: Lucia Prieto-Godino and Ravi Desai. M

Sensory systems encode the world around us to produce context-dependent appropriate behaviours. However, we know little about the way new sensory evoked behaviours arise as neural circuits are re-shaped during evolution. Tackling this question requires a deep understanding of the circuits underlying a specific behaviour, the availability of experimentally accessible closely related species where this behaviour has diverged, and the integration of this knowledge with tools from other fields, including evolutionary and developmental biology. In the lab, we study the evolution of neural circuits using as a model the larval olfactory system of five related Drosophila species with divergent odour-guided behaviours. For this, we leverage recent technological advancements in a multidisciplinary approach that combines high-throughput behavioural assays, optogenetics, electron microscopic circuit reconstruction, functional imaging, single cell transcriptomics, genome editing and population genetics.

A first step to understand the evolution of olfactory preferences in adaptation to novel environments is to determine the volatiles that are being used by each species to identify their host. This might not be immediately obvious, for example our work and those of others showed that in another specialist, D. sechellia, the most abundant host volatile is not detected by the olfactory system, and instead flies relay on less abundant compounds to locate the fruit. To determine the components of the fruit hosts Pandanus that elicit the most divergent behavioural responses across species, we want to assess the responses of first instar larvae towards the juice of the fruits and each of their main individual components in a high-throughput two-choice custom-built behavioural set-up. The project will be carried out in the Prieto-Godino lab in collaboration with the Making Lab and will involve the development and construction of this high-throughput behavioural set-up based on preliminary worked carried out in the lab already. The project will involve 3D prototyping and manufacturing, control of precision valves that are driven with micro-controllers, fluidic modelling and measuring of generated gradients, programming in Arduino language to precisely control the valves, set-up of video recordings of adequate quality for automatic tracking, improve a already established automatic tracking programme written in Python, and synchronise odour delivery and tracking. The project is best suited for a motivated student with programming experience and willing to learn novel techniques and approaches. There is also a second available simpler project to replicate in the lab a sleep-tracking device for adult flies that has been developed by collaborators at Imperial College.

In silico – in vitro hemodynamic study of simplified models of Arteriovenous Malformation

Supervisors: Vanessa Diaz and Terence Leung

Student: Cyrus Tanade

Arteriovenous Malformations (AVMs) are congenital vascular lesions that result in a direct shunt of high flow rates from arterial feeders to draining veins without an intervening network of capillaries. Centrally positioned AVMs in the body can cause high flow cardiac output failure and chronic venous hypertension, while cerebral AVMs can result in unpredictable hemorrhages. As a result, AVMs require treatment to mitigate hemodynamic effects and to curb its progression, but the hemodynamics within the AVM nidus is relatively unknown. This study will develop three simplified AVM nidus models from the Yakes classification to conduct a parameter sensitivity analysis for boundary conditions using computational fluid dynamics (ANSYS Fluent, Canonsburg, PA). In addition, AVM nidus phantoms will be 3D-printed (Ender 3, Shenzhen, CN) to implement boundary conditions from the in silico study on an in vitro flow loop. This will be used to compare experimental flow with computational results for validation. Transparent AVM nidus phantoms will also be manufactured to visualize flow. The results of this study will inform on AVM nidus hemodynamics and help inform on interventional planning and clinical decision making. 

Improving optical ultrasound generators via surface morphology changes

Supervisors: Richard J Colchester and Sacha Noimark

Student: Barbara Cannady

Optical acoustic generators, where ultrasound is generated using light through the photoacoustic effect, have recently been shown to be a viable alternative to conventional piezoelectric technology. Efficient optical acoustic generators typically consist of a highly optically absorbing dye, which converts incident light into heat, and an elastomeric component that efficiently converts this heat into a pressure increase; typically, polydimethylsiloxane (PDMS) is used due to its high thermal expansion coefficient. In addition, the easy handleability of PDMS allows for a wide range of manufacturing techniques resulting in applications of optical ultrasound that are hard to achieve using piezoelectric technology. However, to this date, the effect of the surface roughness and microstructure on the generated ultrasound and composite coating, has not been investigated.

The aim of this project is to investigate how the surface roughness of the substrate for ultrasound generation affects the generated ultrasound field and the coating uniformity. This work has the potential to change our understanding of how these generators work and to improve future imaging devices. The student will develop these coatings, working with novel nanocomposites and undertake their characterisation, using techniques such as atomic force microscopy. Training in these techniques will be provided. The project will be most experimental in nature; some prior lab experience would hence be advantageous, but not essential.

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