MSc Projects 2014-15

Here is a list of projects being offered for MSc students in the Department.
For details of deadlines and for registering submissions, see the Moodle page. Full instructions are available here.

Monitoring cognitive brain functional activation using novel optical instrumentation.

Supervisors: Ilias Tachtsidis and Antonia Hamilton

Near infrared spectroscopy (NIRS) is a non-invasive optical technique which can be used to monitor the oxygenation and haemodynamic response following neuronal activation in specific regions of the brain. The concentration of some tissue absorbers, such as water, melanin, remain virtually constant. However, the absorption of oxygenated-haemoglobin (HbO2) and deoxygenated-haemoglobin (HHb) is dependent on the tissue oxygenation and haemodynamics, which can change during brain functional activation. This project is part of an exciting collaboration between the Biomedical Optics Research Laboratory and the Institute of Cognitive Neuroscience at UCL that aims to deliver novel measurements to investigate brain cognitive function through the use of optical (near infrared) technologies. We have recently acquired a novel wireless NIRS device that allows remote monitoring of brain function during cognitive tasks. For example, participants might be asked to do a social decision making task or a hand action task while brain activity in prefrontal cortex is recorded with NIRS. The main aims of this MSc project are to (1) assist in experimental data collection that will involve brain functional activation experiments in healthy volunteers; and (2) to analyse oxygenation and haemodynamic data from these experiments. 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. The larger scope of the analysis is to investigate the use of NIRS technology to monitor brain function during cognitive tasks. This project would be suitable for a student with an interest in neuroscience; and will involve data collection, data processing and some statistical analysis.

Student: Sarah Power

Exploration of the implementation of combined magnetic resonance and optical methods for assessment of brain metabolism and haemodynamics: application to a preclinical model of birth asphyxia.

Supervisors: Ilias Tachtsidis and Pardis Kaynezhad

Perinatal cerebral hypoxic-ischemia (HI) is a condition resulting from reduced oxygen delivery or/and blood flow occurring either in utero or during delivery. It occurs in 1 to 2 per 1000 live births and can result in physical or sensoreal handicap or fatality. Although there are significant advances in the treatment of asphyxiated babies, little is known about the effects of these neuroprotection strategies on brain blood perfusion and metabolism following HI. This project is part of an exciting collaboration between the Perinatal-Brain Magnetic-Resonance Group at UCH and the Biomedical Optics Research Laboratory at UCL that aims to deliver novel measurements to investigate the effects of brain neuroprotection through combination of magnetic resonance and optical (near infrared) technologies. The main aims of this MSc project are to (1) assist in experimental data collection; and (2) to analyse imaging, perfusion and metabolic data, from the optical instruments, obtained from piglet brains in-vivo after HI. 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 the magnetic resonance imaging & spectroscopy measurements. The larger scope of the analysis is to investigate the benefits of implementing treatment in birth asphyxiated infants. This project would be suitable for a student with an interest in optical technologies, physiology/pathophysiology, brain tissue biochemistry; and will involve data collection, data processing and some statistical analysis.

Student: Cierra Block

Investigation of the optical measurements of oxygenation and haemodynamic changes in neonatal injury.

Supervisors: Ilias Tachtsidis and Gemma Bale

Neonatal encephalopathy (NE) is the clinical presentation of disordered neonatal brain function. The incidence of hypoxic-ischaemic encephalopathy (HIE) is 1-3 per 1000 live births1 - with a term birth rate of 750,000 births in the UK, the number of affected term infants is estimated to be 750-1125 infants annually in the UK. The chain of events leading to NE is complex and multifactorial. There is a need to improve understanding of the timing and evolution of neonatal brain tissue’s response to injury using non-invasive bedside tools, which can provide robust markers of injury. Near-infrared spectroscopy (NIRS) is a promising optical tool for neuromonitoring, which has been applied widely to assess cerebral perfusion and oxygenation in the newborn. In particular NIRS measures in brain tissue changes in the oxidation state of cytochrome c oxidase (oxCCO), oxy- and deoxy- haemoglobin concentrations (HbO2, HHb) and cerebral tissue oxygen saturation. As part of a collaboration between the Biomedical Optics Research Laboratory and UCLH studies have already performed in injured and pre-term babies and they are on-going. The student will require to analyse the data and investigate the relationship between the optical brain tissue measurements and systemic variation (such as blood pressure, arterial oxygen saturation). This project is a mixture of signal and statistical analysis. This project will take place at both (i) the Biomedical Optics Research Laboratory at UCL the UK’s leading research group in biomedical optics, which offers expertise and facilities in optical instrumentation and methodologies and (ii) the UCLH where patient’s recruitment will be done.

Student:Kirushan Sabanayagam

Reconstruction techniques in quantitative susceptibility mapping.

Supervisors: Enrico Kaden and Daniel Alexander

Magnetic susceptibility quantifies the degree to which a sample is magnetised after placing it in a magnetic field. Susceptibility weighting imaging (SWI) is an MRI technique able to measure this material property noninvasively. As different types of tissue may give rise to different susceptibilities, image contrast is generated that enables us to discriminate between brain structures based on their physico-chemical composition. In particular, this imaging technique can detect excessive iron deposition, which is an important marker for neurodegenerative diseases, has shown potential to track demyelinating disorders like multiple sclerosis, and allows the in-vivo quantification of cerebral microbleeds and intracranial calcification. This project aims to develop reconstruction techniques for the estimation of susceptibility-related tissue features in the human brain, which typically cannot be observed directly from the measured signals. Rather, we need to solve an ill-posed inverse problem which links the MR measurements to the underlying tissue material. These quantitative mapping methods include phase unwrapping, background field removal, and dipole inversion in order to recover the magnetic susceptibility in the cerebral white matter. A solid background in mathematics and physics as well as working knowledge in MATLAB, C/C++, or a similar programming environment are required.

Student: (Project available)

Anaysis of edge-illumination based x-ray phase contrast images of breast tumours

Supervisors: Alessandro Olivo and Paul C. Diemoz

X-ray phase contrast imaging is a new imaging modality not based on x-ray attenuation, in which all details in an image are made more evident by intense edge-enhancing fringes running along their borders. This also results in making classically undetectable objects (as they oppose non absorption to x-rays) visible in the image. This method produced unprecedented results in the imaging of breast tissues, where it proved it could visualize lesions previously undetectable (or at a stage at which they are not yet detected by conventional methods). This result was obtained at synchrotrons, huge, complicated and very expensive facilities – only approximately 50 of which exist in the world. This notwithstanding, the above result was so revolutionary that it triggered the construction of the first facility for in vivo x-ray phase contrast mammography at a synchrotron in Italy, despite the very limited number of patients it can handle. More recently, the UCL team has developed a method that could make similar results achievable with conventional sources. This could make the above result widely available in hospitals and clinics across the world, allowing an earlier detection of breast tumours and consequently a reduction in the mortality rate. The team is currently using the method to image a large number of breast tissue samples containing tumours to achieve statistical significance. The student would participate in the data analysis by comparing absorption and phase contrast images of the same breast tissue sample, and quantitatively assessing the improvements brought by the latter. If time allows, synchrotron images would also be provided to allow a comparison of the UCL images against the “gold standard”. The student will gain skills in data and especially image analysis, and familiarize with some of the basic concepts of medical imaging. Basic computing skills are required.

Student: (Project available)

Characterization of detector performance for x-ray phase contrast imaging

Supervisors: Marco Endrizzi and Alessandro Olivo

X-ray phase contrast imaging is a new imaging modality which exploits interference and refraction effects instead of x-ray absorption. As a consequence, it has the potential to radically transform all applications of x-ray imaging (first and foremost diagnostic radiology), as it increases the visibility of all details in an image and it enables the detection of features classically considered x-ray invisible. For many years, this modality was considered to be restricted to very specialized facilities called synchrotrons, but our group has recently developed a method which makes it work with conventional x-ray sources, thus potentially enabling its clinical translation. This method however is highly affected by the performance of the used x-ray detector. Not only does this include classic parameters like noise, spatial resolution and detector response as a function of energy, but also more sophisticated and method-specific ones like signal spill-out between adjacent pixels. The student will be required to thoroughly characterize the available x-ray detectors (especially a state-of-the-art direct conversion flat panel detector based on amorphous selenium), and assess the impact of the extracted parameters on the phase contrast performance of the devices. The skills the student will gain go beyond phase contrast imaging and also cover many aspects of x-ray detector technology and characterization, which are relevant to diagnostic radiology in general.

Student: Oskar Blaszczyk

Exploring the limits of the quantitative retrieval of x-ray phase

Supervisors: Paul C. Diemoz and Alessandro Olivo

X-ray phase-contrast imaging (XPCi) allows the generation of images with highly improved contrast compared to conventional X-ray imaging techniques. While the latter are based on measuring the attenuation of a photon beam when passing through different parts of the sample, XPCi exploits the interference/refraction effects experienced by the photons. Until recently, XPCi has been mostly limited to specialized and expensive synchrotron facilities, due to the need of using photon beams of very high coherence and flux. Our group, however, developed a new implementation of XPCi, the “coded apertures” (CA) technique, which was proven to work efficiently even with standard x-ray sources and laboratory equipment. It has therefore great potential for applications in many fields of x-ray investigation, such as materials science, biomedical and clinical imaging. Recently, we have developed an algorithm to extract quantitative sample information from experimental CA XPCi images. This algorithm is, however, based on some simplifying assumptions that are likely to not be valid for all samples and experimental conditions that could be encountered in practice. This project will consist in studying how the method’s accuracy varies for different sets of experimental parameters and different types of samples. To this aim, the student will make use of a simulation code developed within our group, and use it to test the quantitative values provided by the algorithm against theoretically predicted ones. The ultimate goal of this study is to find some simple rules that describe which parameters are the most important in determining the method’s quantitative accuracy and under which experimental conditions the algorithms provides satisfying results. The student will gain skills in simulation methods, data analysis, and familiarize with the basic concepts of optics. A solid physics background is required.

Student: (Project available)

Comparison of iterative algorithms for x-ray phase contrast CT reconstructions

Supervisors: Charlotte Hagen and Alessandro Olivo

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 and refraction effects. XPCi techniques are especially important for the imaging of weakly attenuating biological samples and are investigated by an increasing number of groups worldwide, including the phase contrast group at UCL.
Edge Illumination (EI) XPCi - a novel method developed at UCL – can measure the refraction angle of x-rays as they pass an object. EI XPCi has recently been implemented as tomographic modality. By rotating the object over an angular range of at least 180 degrees and acquiring images at every rotation angle it is possible to reconstruct volumetric maps of the refractive index distribution within the object. While until now all tomographic reconstructions were carried out using filtered back projection, the next step is to explore the benefit of iterative algorithms for the reconstruction of these maps. The student would be given several experimental EI XPCi datasets on which different iterative reconstruction algorithms can be tested. The student would define metrics that can be used for a comparison and eventually decide which algorithm is most suited to the reconstruction problem at hand.
Besides being involved in the development of a new imaging method, the student would get familiar with the basic concepts of computed tomography and image reconstruction. The project requires mathematics and some experience in Matlab programming. Existing software packages (e.g. the ASTRA toolbox) can be used.

Student: Ben Cockburn

Magnetic stimulation of the human motor cortex: exploring sources of variability in motor evoked potentials

Supervisors: Ricci Hannah and John Rothwell

Transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation technique which is used to investigate human motor cortex physiology in vivo. The response, a motor evoked potential (MEP), can be detected using electromyography, and its amplitude reflects the excitability of the corticospinal tract. Stimulus-response curves, describing the input-output relation of the corticospinal tract, are a common outcome measure in motor learning and neuro-rehabilitation research. The curves are obtained by delivering multiple stimuli at a range of intensities, in a randomised order. The MEP amplitudes are then fitted with a Boltzmann-like model. There are, however, a couple of practical problems which affect obtaining and analysing such data. The first is that the curves take a long time to obtain (~10 minutes) and thus may be affected by waning attention and alertness of the participant. The second is the variability in MEP amplitudes from stimulus-to-stimulus, and the fact that the distribution of amplitudes varies across different stimulus strengths thus violating the assumptions of traditional regression models. A novel model incorporating two sources of variability better describes the input-output characteristics of MEPs. The aim of this project is to examine the variability and it sources under different conditions (e.g. different muscles, rest versus contraction), since this may provide insight into the physiologic site and signalling-related mechanisms of variability. Key to achieving this will be the reducing the data acquisition time, by decreasing the interval between stimuli, and an examining how this affects the variability. This project is a mixture of programming, experimentation and modelling, and will specifically involve: (1) writing a short piece of code in specialist data acquisition software to deliver stimuli of random intensities across a range of inter-stimulus intervals; (2) recruiting healthy human participants; (3) using magnetic brain stimulation and electromyography to collect neurophysiological data (MEP stimulus-response curves); (4) processing and analysing the data. The work will take place within the Transcranial Magnetic Stimulation laboratories of the UCL Institute of Neurology in Queen Square.

Student: (Project available)

Embedded PC

Supervisors: Nick Donaldson and TBC

We are developing a new exercise machine for patients with spinal cord injury. To maximise recovery, we want to use the latent plasticity of the spinal cord by combining electrical stimulation of the paralysed muscles with a system that encourages voluntary effort to drive the pedals while cycling. The voluntary effort is encouraged by the patient participating in a virtual race in which their speed depends on the crank shaft torque without stimulation. The virtual race uses software from the company Tacx that runs on a laptop.
However, for this system to be of widespread use, it must be used by patients at home, which often will be a small flat and they may not be confident computer-users. Therefore an important step is to embed the PC inside the machine so that the program boots up as soon as the power is switched on. Also, to minimise the number of cables, a wireless link to a large display will be preferable. The Tacx software comes on a CD disc but uses an internet connection while running if one is available.
The aim of this project is to investigate the way the Tacx software works and decide whether it can run on embedded hardware, and, specifically, choose a low-cost system. A method for installing the software must be possible and ideally, the system should be set up with many courses, from which races can be chosen, without internet access. An input device that serves as a mouse will be necessary and this must be made part of the small controller that the patient holds while exercising.

Student: (Project available)

Development of a setup that controls Parylene C deposition onto implants via diffusion-limitation

Supervisors: Tim Boretius and Nick Donaldson

Electrodes for neuroprosthetic applications are designed to be implanted into specific body tissues and to restore or enhance lost functions via (e.g.) electrical stimulation. To do so, such electrodes are in need of an encapsulation to protect the electrical circuits from body fluids and the associated risk of corrosion, which would ultimately cause failure of the implanted device. Silicone rubber and parylene C are common encapsulants in use whereas parylene is especially suitable when thin (~10-20µm) and conformal coatings are needed.
Within our new project CANDO, we are developing brain probes for epilepsy treatment, which need a coating of parylene on their tips but not on their base. Since parylene is deposited from the vapour phase, a diffusion-limited approach seems feasible. Thus, the main aims for this project are (1) to develop a setup that limits the diffusion of parylene onto the probe tips and (2) to characterise the resulting taper. The prospective student will learn about biomedical encapsulants, diffusion control, CAD software usage, and laser processing. Strong practical skills are required since most of the work will be done in the lab.

Student: (Project available)

EIT perfusion imaging.

Supervisors: Rebecca Yerworth and Adam Gibson

Electrical Impedance Tomography (EIT) creates images of the internal impedance changes related to physiological function using a series of surface electrodes measurements placed on the surface of the body in real-time. It is increasingly being used as a bedside tool for monitoring regional lung ventilation. However surprisingly little is published on perfusion (blood distribution)/flow. This may be because most clinical systems use serial data collection which, if uncorrected, results in image distortion, and may be obscuring the cardiac signal. This project aim is to see if the application of a recently developed data correction technique enables the cardiac related signal to be visualised and would involve creating simulations and reanalysing previously collected data.

Student: (Project available)

Deformable Registration of Paired Breath-Hold CT Lung Images for the Assessment of the Stage and Severity of Chronic Obstructive Pulmonary Disease.

Supervisors: Jamie McClelland and Felix Bragman

Chronic Obstructive Pulmonary Disease (COPD) is a common disease, which has been identified as the fourth leading cause of mortality and morbidity within the USA with projected global numbers rising to the fifth and third most common cause of morbidity and mortality, respectively, by the year 2020. It is a progressive non-reversible disease, which is characterised by airflow limitation and obstruction. It is a highly complex, multi-dimensional disease consisting of three main subtypes; chronic bronchitis, chronic bronchiolitis and emphysema. Spirometry is the current accepted gold standard for the assessment and staging of COPD. However, a limitation of spirometry is that it produces a single, global measure, which ignores the complexities and the underlying spatial progression of COPD. This is motivating the use of imaging techniques and the development of novel quantitative tools for the analysis of COPD. Deformable image registration is the process of finding the transformation, which aligns two images into the same reference frame. Registration of chest images is a challenging task. This is due to the large non-linear deformation seen during the respiration process, the sliding motion of the lung with respect to the ribcage in addition to the local intensity changes resulting from to the variation of tissue density as a result of the breathing process. Registration of the lung sees various clinical applications notably in radiotherapy and more recently in the study of COPD. The main aim of this project lies in the investigation and development of state of the art techniques for achieving accurate lung registration. Work will build upon recent developments at the Centre for Medical Image Computing and will be based on the NiftyReg registration package. Various routes may be undertaken to improve the registration. This includes investigating the limits and optimal parameters of the current algorithm, employing a feature-based step to guide the deformable registration and investigating new regularisation schemes to deal with the sliding boundary conditions. This project will requires strong mathematical, computing and programming skills, in addition to a willingness to learn about registration algorithms and the underlying mathematics. This project may lead to a conference publication upon successful completion.

Student: (Project available)

Critical Assessment of Low-Cost MRI Solutions in Africa

Supervisors: Karin Shmueli and Priti Parikh

Magnetic Resonance Imaging (MRI) has been in clinical use for over 20 years in the most developed countries in the world. The aims of this project are to investigate the prevalence and clinical use of MRI in African countries. Many clinical settings in these countries are resource challenged, underscoring a need for the development of low-cost MRI solutions. Do these exist? Are they local and sustainable? What is the level of demand for clinical MRI? Which companies, if any, are working in this sector in Africa? Is there sufficient local training of skilled technicians and operators? The student will be expected to investigate these and related questions. Ideally they will critically assess the advantages and disadvantages of potential low-cost MRI solutions using medical physics (and development) principles. The student will be expected to communicate effectively with international contacts to gather information.

Student: Larisa Sequeira

Characterising and Modelling Susceptibility Artifacts in Mouse Brain MRI at 9.4 Tesla

Supervisors: Karin Shmueli and Simon Walker-Samuel

Magnetic Resonance Imaging (MRI) at high magnetic field strengths (e.g. 9.4 Tesla) is advantageous as it provides greater signal. However, image artifacts due to air-tissue magnetic susceptibility differences worsen as the field strength increases. Geometric distortion and signal drop-out artifacts caused by large magnetic field inhomogeneities around the ear-canals are a particular problem for gradient-echo MRI of the mouse brain at 9.4 Tesla.
This project will focus on characterising these susceptibility artifacts and modelling their sources with the goal of improving preclinical MRI in mouse models. Susceptibility artifacts can be characterised using MRI by mapping the local magnetic fields in the mouse brain and analysing the field inhomogeneities (i.e. field gradients). MRI magnetic field maps have been acquired in several mice and one aim is to combine these to create an ‘average’ field map to allow us to predict common susceptibility artifacts. We may also be able to gain insight into the source of these artifacts by building a map of the underlying soft tissue and bone susceptibilities that contribute to the non-uniform magnetic field distribution and modelling the resulting magnetic field distribution. Comparing the modelled field map with the ‘average’ measured field map will improve our understanding of susceptibility artifacts in the mouse.
You will use Matlab (or other software) to analyse MRI data acquired on the 9.4 Tesla MRI system at the Centre for Advanced Biomedical Imaging (CABI). You will calculate, process and simulate magnetic susceptibility and field maps as well as comparing measured and modelled field maps. Insights from this work will provide a valuable foundation for developing techniques to reduce susceptibility artifacts in the mouse brain at 9.4 T.

Student: Rosie Goodburn

Assessment of dose in diffraction imaging

Supervisors: Jennifer Griffiths and Yi Zheng

X-ray diffraction has the ability to differentiate between healthy and diseased breast tissues. This could lead to more accurate and faster diagnosis of breast cancer. In order to further it’s translation into the clinic, two key questions must be addressed:
1. What is the additional dose to the patient?
2. What are the structures in the tissue that the diffraction is identifying?
These questions form the basis of two student projects in radiation physics.
This project will involve modeling the dose to the patient expected if x-ray diffraction were to be used in addition to standard x-ray mammography. You will need to understand the physics of coherent scatter to accurately model the scatter created in a patient, and consider the dose needed to create a signal suitable for diagnosis. A survey of detectors that could be used for x-ray diffraction and the signal to noise ratio of the data will need to be taken into account.
This project would suit a student with confidence in coding – your first job will be to identify the most suitable way to model the system.
Simple experiments in the laboratory can be used to validate your model.

Student: (Project available)

The role of collagen types and structures in breast tissue

Supervisors: Jennifer Griffiths and Yi Zheng

X-ray diffraction has the ability to differentiate between healthy and diseased breast tissues. This could lead to more accurate and faster diagnosis of breast cancer. In order to further it’s translation into the clinic, two key questions must be addressed:
1. What is the additional dose to the patient?
2. What are the structures in the tissue that the diffraction is identifying?
These questions form the basis of two student projects in radiation physics.
This project will involve a thorough survey of the types of tissues, especially collagen, found in healthy and diseased tissues, and their structures. You will need to assess the literature to find out the expected diffraction signals from the tissue types. You will produce a thorough review of the tissue types, particularly different collagens, and their signals before designing the ideal diffraction geometry required to measure these diffraction signals.

Student: (Project available)

Investigation on extremely thin silicone rubber coatings on shaped silicon substrates

Supervisors: Nick Donaldson and Tim Boretius

Silicone rubber is one of the best encapsulants for implanted medical devices due to its outstanding properties: physiological indifference, excellent resistance to biodegradation and age, and also proven biocompatibility. Furthermore, it is permeable to most gasses while acting as ion barrier, which makes it very suitable to protect electronic components or metallic structures which would otherwise corrode. However, silicone rubber is conventionally used as a rather thick film (>50µm) and, hence, its application becomes limited with the decreasing dimensions of modern electrode designs. Within our new project CANDO, we are developing brain probes for epilepsy treatment that could profit from an extremely thin encapsulating layer of silicone. Thus, the aim of this project is to investigate how thin silicone rubber can actually be deposited and which techniques seem feasible to achieve a 3-dimensional and homogenous coating. The prospective student will learn about biomedical encapsulants, moulding and coating techniques and CAD software usage. A background in mechanics is preferential but not mandatory because we wish to test the idea of using an adapted centrifuge.

Student: Project taken

Measurements of Arterial Compliance using Pulse Wave Propagation Velocity

Supervisors: Julian Henty and Terence Leung

The arterial pulse wave is a mechanical wave which travels from the heart to peripheral sites in the arterial tree. The delay between the onset of this wave (the QRS spike of the ECG) and another point on the arterial tree, such as the finger, may be measured with simple three-lead ECG recording and with photoplethysmography (PPG) using a pulse oximeter. The measured delay is used to determine the velocity of this wave (the pulse wave propagation velocity, PWPV).
In this project, we wish to attempt to correlate simple variables (such as food/liquid intake and blood pressure) with the PWPV. Most of the hardware for this project is already assembled and therefore only some basic software in LabVIEW is required (training will be given) before experiments can begin.

Student: (Project available)

Development of a computer program for the automatic extraction of information from IR images towards the identification of core temperature

Supervisors: Pilar Garcia Souto and Adam Gibson

During the outburst of infectious diseases, e.g. SARS in 2003, the Influenza A pandemic in 2009, and the Ebola in 2014, core temperature screening was used to detect individuals with fever with the aim of isolating infected individuals, which could help to contain the spread of such infectious diseases. In high transit places such as airports and hospitals, infrared (IR) thermometry has been used for screening as it is a relatively easy to use, quick and non-invasive. This method estimates the core temperature from measurements of heat (in form of infrared) being emitted off a person’s skin at a given location. However such devices are subjected to a high error due to its incorrect use by personnel at the airport, who would have had poor or little training. Typical mistakes are the incorrect identification of the area of interest within the body, or the erroneous interpretation of the measurements. Therefore it is necessary to develop a robust and reliable device that can automatically identify the area of interest and extract the relevant information in real time. The student undertaking this project will develop a computer program that identifies given locations of the head of a person in an IR image such that the temperature at those locations can be extracted. A sample of IR images will be provided for testing. The aims to be achieved are: (1) Research of current systems used for IR images analysis and relevant image processing techniques for the identification of given locations within the head, e.g. forehead, eyes or ears; (2) develop computer software for automatic extraction of data from IR images as to guarantee equal marker sizes, temperature standard deviation, etc.; and (3) comparison of manually and automatically extracted data. This project is suitable for someone with computer programming and image processing knowledge. MATLAB is preferred but other programming languages can also be considered. Statistical knowledge would be useful for this project.

Student: (Project available)

Development of a non-invasive core temperature measurement method for mass screening based on IR images of the body.

Supervisors: Pilar Garcia Souto and Adam Gibson

The practise of monitoring core temperature on individuals in high transit places such as airports and hospitals has arisen, especially during infectious outbursts such as SARS and Influenza A pandemic in 2003 and 2009 respectively. Mass screening of core temperature was employed with the aim of isolating infected individuals (using fever as a primary symptom), which could help to contain the spread of such infectious diseases. Infrared (IR) thermometry has been used in this kind of screening. It is a relatively easy to use, quick and non-invasive method which gives an estimate of core temperature by measuring the amount of heat (in form of infrared) being emitted off a person’s skin. Several body sites have been studied, such as forehead. However current devices are not accurate and therefore give only a false sense of security, which is more dangerous. The aim of this project is to develop a more robust and reliable non-invasive device/method for the identification of core temperature, for which the student will have access to experimental data already collected for that purpose. The student would gain knowledge of IR technology used in biomedical applications, particularly for non-invasive core temperature estimation. The objectives would be: (1) extraction of data from IR images using Fluke software; (2) identification of skin locations with highest correlation with the core temperature; (3) create a model relating core temperature and skin temperature to be used for a new core temperature screening device. Statistical knowledge would be very useful for this project.

Student: (Project available)

A programmable controller for an electrical stimulator

Supervisors: Anne Vanhoest and Elliott Magee

Electrical stimulation can be used to inhibit certain reflexes or painful responses. This is called neuromodulation, and it is used in a growing number of medical applications. One of these is the treatment of incontinence, which affects a surprisingly large fraction of the adult population, in the UK and worldwide. In this project, a programmable controller for a commercial electrical stimulator will be designed, built and tested, as well as a gui to program it. The commercial stimulator we will use has an optional digital input that can be used to turn it on-off, though in stand alone mode the user simply switches it on-off manually. The aim is to provide a small, light-weight and portable device, to connect to the stimulator, to control the on-off periods over a day. The device will also have a series of minor features, such as recording events, and, if possible, sending usage data wirelessly to a remote server. As it is to be used by clinical scientists, nurses or patients, it must be very user friendly, preferably with a gui for programmation, and data visualisation. This project will be developed in collaboration with Dr Sarah Knight, of the London Spinal Cord Injury Research Centre, who will provide feedback on useability and desired features.

Student: Nikitas Sayegh

Pedal force measurements with a recumbant tricycle

Supervisors: Anne Vanhoest and Nick Donaldson

Paralysed people can propel a recumbant trycicle using electrical stimulation to activate their leg's muscles, as seen in this short video. The power they produce, however, is significantly lower than that of an able-bodied cyclist. To study the differences between the two groups, we have instrumented a tricycle to measure the forces exerted on the pedals, which we can now monitor as analog signals. Our setup also provides us with an analog signal indicating the position of the pedals. In this project, the student will write a graphical user interface to acquire both signals (on a PC), display them live in an intuitive fashion and save them. The tricycle is located on the Stanmore campus of UCL, on the grounds of the Royal National Orthopaedic Hospital. This gives us access to patients and clinicians whom we will consult to evaluate the useability of this GUI. If this first phase is completed rapidly, the project may be extended in two directions. 1. Further development of the setup to include electrical stimulation synchronised to the cycling motion. 2. A small scale study of a few able-bodied cyclists, looking at a) the influence of using a metronome to impose a cadence vs free cycling, b) the position of the cyclist and c) the effect of orthoses to fix the legs to the pedals.

Student: (Project available)

Development of a skin perfusion imaging technique using a consumer grade (GoPro) digital camera

Supervisors: Terence Leung and Ashish Shetty, Sam Chong and Janice Tsui

At every heartbeat, blood is pumped from the heart to the rest of the body via arteries, causing them to pulsate. These arterial pulsations lead to a slight change in skin colour, not visible to naked eyes but can be captured by a series of images taken by a digital camera. By detecting the periodicity of the colour change, one can derive the heart rate accordingly. In this project, not only the periodicity but also the amplitude and phase of the arterial pulsations will be calculated on a pixel-by-pixel basis, forming an image which shows the distribution of arterial pulsations on the skin in 2D, a technique known as Photoplethysmographic (PPG) Imaging. These images will potentially allow clinicians to identify a series of pathological conditions related to vascular diseases and skin perfusion including the viability of diseased tissue (e.g. diabetic foot) and the onset of migraine. The images will be captured by a GoPro digital camera, a specialist camera often used in sports photography with a high resolution and high frame rates (up to 240 fps). The role of the student is to optimise the quality of the perfusion images by experimenting with different kinds of illumination (colours and angles) and settings (ISO, white balance etc.) of the camera. The student will also need to improve a Matlab program that analyses raw images and converts them into a perfusion image. The student will be able to work with our clinical collaborators and test the technique with patients from the Pain Management Centre at the National Hospital for Neurology and Neurosurgery, and the Division of Surgery & Interventional Science at the Royal Free Hospital. This project will suit a student who is interested in biomedical optics, digital photography, computer programming and healthcare technology. An example of perfusion images can be found here:

Student: (Project available)

Photoacoustic imaging of embedded absorbers

Supervisors: Erwin Alles and Adrien Desjardins

In medical photoacoustic imaging, pulsed light is shone onto tissue and converted into heat due to absorption. The resulting localised pressure increase propagates through the tissue as an acoustic wave, and can be recorded and reconstructed into an image. As with optical methods, the wavelength dependence of the absorption coefficient can be exploited to study tissue composition.
When an absorbing structure is embedded in a non-absorbing background, the photoacoustic spectrum closely resembles its absorption spectrum. However, due to the limited bandwidth of typical ultrasound detectors, only the boundaries of the structure are visible in a reconstructed image. This suggests that, in the case of an absorbing structure surrounded by a different absorbing medium, the photoacoustic spectrum observed at the boundary should resemble the difference between the absorption spectra across the boundary. This effect has previously been observed in experiments on ex vivo nerve tissue (containing water and fat), where the photoacoustic spectrum mainly resembled the absorption spectrum of fat alone.
The aim of this project is to investigate the effect described above, both numerically and experimentally (using a clinical ultrasound scanner), in controlled phantoms. This way, the hypothesis that this effect is caused by bandwidth limitations can be tested. In addition, the generality of the effect can be explored, which might lead to a better understanding of and novel applications of photoacoustic imaging, and to the improved visibility of certain absorbers in photoacoustic images. As the project contains both a computational and an experimental part, some experience with Matlab and experimentation would be advantageous.

Student: (Project available)

Image quality transfer

Supervisors: Daniel Alexander and TBC

This project uses machine learning to improve the quality and
resolution of medical images acquired from standard devices by
learning and projecting image structure from high quality data from
specialist devices. We have access to a data set of very high quality
brain images from a unique bespoke MRI scanner. The aim is to learn
from those images to improve reconstruction of more every-day data
routinely acquired in hospitals. The project builds on an ongoing
collaboration with Microsoft Research and uses random forest
regression with diffusion tensor imaging.

Student: (Project available)

Models of dispersion in capillary networks

Supervisors: Daniel Alexander and TBC

The project will construct and evaluate mathematical and computational
models of the MRI signal from water molecules in blood flowing through
capillary networks. It contributes to the broader research program of
the Microstructure Imaging Group:
Diffusion-weighted MRI holds great promise as a non-invasive
diagnostic probe in cancer imaging. Blood flow in capillaries (the
intra-voxel incoherent motion or IVIM effect) is a significant
confound that complicates its practical application. However,
modelling the effect potentially allows us to exploit
diffusion-weighted imaging in cancer more effectively and may even
provide additional information that is currently discarded. The
project takes the first steps towards this by constructing candidate
mathematical models and validating them against computer simulations.

With Becky Shipley (UCL Dept. Mechanical Engineering)

Student: (Project available)

Image-based diagnosis of multiple sclerosis

Supervisors: Daniel Alexander and TBC

This project will use classification techniques from machine learning
to make diagnoses of multiple sclerosis from brain images. UCL's
institue of neurology has large databases of images from
multiple-sclerosis patients and normal controls that we can use to
train classifiers. More accurate diagnosis from imaging will have a
direct impact on quality of life of these patients by expediting
decisions about which treatments they should receive. Previous work
has managed to get around 80% correct diagnosis using machine learning
techniques, but the features the algorithms learn from are manually
defined so expensive to obtain. This project will look at learning
from much more directly obtainable image features aiming for a truly
automated computer assisted diagnosis system.

With Olga Ciccarelli from UCL's Institute of Neurology

Student: (Project available)

Stochastic optimization inspired by biological foraging models

Supervisors: Daniel Alexander and TBC

Stochastic optimization techniques, such as simulated annealing,
genetic algorithms, self-organizing migratory algorithms (SOMA), and
differential evolution, avoid problems of local minima that confound
gradient descent algorithms. They are often the only viable
techniques for minimizing high dimensional functions with complex
topology. Many of these algorithms maintain a population of candidate
solutions, which migrate over the search space in various ways. For
example, the SOMA algorithm uses an analogy of herding cattle, which
migrate towards rich areas of pasture. The precise mechanism of the
migration determines the efficiency of the algorithm and the SOMA
strategy proves consistently effective in a diverse range of problems.
The aim of this project is to test a range of alternative biologically
inspired migration strategies to improve the efficiency of the search.

Student: (Project available)

Cancer grading from histology

Supervisors: Daniel Alexander and TBC

This project aims to automate the process of cancer grading using
image processing, computer vision and machine learning techniques.
The standard way to diagnose cancer, and to determine how malignant a
particular tumour is, is through biopsy and histology: a small piece
of tumour is extracted from the patient; a pathologist looks at it
under a microscope and decides, from the cellular make-up of the
tissue, the nature of the tumour and thus the appropriate treatment.
We will aim here to automate the pathologist's process of grading the
cancer from the microscope image. We have lots of data to train and
test classifiers. A key part of the project however will be to
determine what are the most important image features required to
maximize classification performance.

With Laura Panagiotaki from UCL's Centre for Medical Image Computing

Student: (Project available)

Modelling neurological disease progression

Supervisors: Daniel Alexander and TBC

This project works on generative modelling, machine learning and image
processing techniques to construct a model of how neurological
diseases, such as Alzheimer's disease and other forms of dementia,
progress within the brain. From serial MRI scans of patient brains,
we can measure the effect of the disease in different brain regions by
determining volume changes. We have an on-going collaboration with
UCL's Dementia Research Center, who have been collecting image data
from patients for many years, giving us unique access to large data
bases from which to estimate these models. Detailed maps of how the
disease progresses in the brain over time will provide new insight
into how the disease works and help develop treatments in the long
term. The project will use optimization, Bayesian estimation, and
some new computational modelling techniques; implementation will be in

Student: (Project available)

Breast Cancer Tumour Segmentation from Ultrasound Imaging

Supervisors: John Hipwell and Peter Wijeratne

Breast cancer is the most common type of cancer worldwide; 50,000 women were diagnosed in 2010 with invasive breast cancer in the UK alone. The lifetime risk of being diagnosed with breast cancer in women is 1 in 9. The primary treatment for breast cancer is surgery but chemotherapy is commonly used in advance of surgery to shrink the tumour. Unfortunately not all cancers will respond to chemotherapy, so many women will suffer the unpleasant side-effects of chemotherapy but ultimately gain little or no benefit from the treatment. There is evidence however, that the stiffness of the region immediately surrounding the tumour (as measured via shear-wave elastography) may be able to distinguish between patients who will respond to chemotherapy, from those who won't. We are currently investigating this hypothesis but in order to do so we need a reliable and robust, automated (or semi-automated) method to extract the tumour region from B-mode ultrasound images. This project will therefore investigate appropriate segmentation methods, building on existing methods where available and developing new algorithms using computational methods such as those available in the Insight Toolkit ( This project would suit a student interested in medical imaging computing and keen to develop their c++ programming skills.

Student: (Project available)

Development of ultrasound imaging phantoms for minimally invasive procedures

Supervisors: Adrien Desjardins and Wenfeng Xia

Ultrasound imaging is increasingly used to guide minimally invasive procedures in the human body. This imaging modality has many advantages, such as being real-time and non-ionising, but it can be challenging to interpret images and to plan an optimal path to the tissue target. There is an urgent need for better training tools for a wide range of patient anatomies. This project will involve creating an ultrasound phantom for percutaneous (needle-based) procedures. In particular, it will involve computational and experimental investigations of paraffin-based phantoms. The project will include an investigation of the optical properties of these phantoms to assess their potential for photoacoustic imaging, a modality complementary to conventional ultrasound imaging. The project will be performed in close collaboration with a Consultant Anaesthetist at the University College Hospital. The student should have an interest in experimental studies, data analysis, and the application of physics and engineering concepts to medicine. Prior experience with ultrasound would be useful but it is not required.

Student: (Project available)

Validation of Registration in Laparoscopic Liver Surgery

Supervisors: Matt Clarkson and Steve Thompson, Johannes Totz

Liver cancers can be removed using key-hole liver surgery which results in less post operative pain than traditional open surgery. Currently only around 10% of cancers can removed by key hole surgery because of the difficulty in identifying and dividing blood vessels within the liver. The Centre for Medical Image Computing (CMIC) has developed an image guided laparoscopic surgery system that uses a model of patient anatomy, derived from pre-operative CT images, aligned with the view seen through a stereo laparoscope. This enables an augmented reality display whereby information such as tumours, and critical blood vessels can be overlaid on the laparoscopic video, enabling more patients to benefit from key hole surgery.
A critical part of deploying a system clinically is to understand and validate the accuracy of image alignment. This project will involve using CMIC’s NifTK software platform to perform validation experiments to assess the accuracy of tumour locations, using phantom and porcine data. The validation results should lead to a publication. The student will have the opportunity to contribute to the development of the system as we undertake our first trials in humans.

Student: (Project available)

Head support and movement AID for ALS patients

Supervisors: Pilar Garcia Souto and Nick Donaldson

Amyotrophic Lateral Screrosis (ALS) is a neurodegenerative disorder characterized by weakness and atrophy of muscles as a result of the degeneration of upper and lower motor neurons. One of the most famous cases is Stephen Hawking. From early stages of the ALS, patients experience difficulties to support the weight of their heads even if seated, which has a major effect in their autonomy. This project aims to design a collar to support the weight of the head and give stability while allowing and facilitating side to side movements of the head. The study involves the collection of data to characterize the motion of the head relative to the trunk while walking and seating using motion capture, and the design (and possible manufacture and testing) of a suitable neck collar.

Student: (Project available)

Control of mechanical strength of fibrin-based biomaterials for Mesenchymal Stem Cell differentiation

Supervisors: Julian Dye and TBC

(1 or 2 project students)
Fibrin as a biomaterial offers potential for promoting angiogenesis and rapid cellular integration. Recently, a strategy to extend the efficacy of fibrin as a biomaterial for dermal reconstruction has been developed. Specifically, a porous composite of fibrin & alginate, cross-linked by glutaraldehyde, has been developed. The material supports rapid vascularisation and integration, and can be used as a synthetic dermal replacement with a split-thickness skin overgraft for single step surgical reconstruction of full thickness skin-loss. However, it is limited by its intrinsically low mechanical strength and rapid plasminolysis.
The aim of the project is to study the effect of cross linking and nano-scale fibrin fibre organisation on the micro and macro-scale mechanical properties of the materials. The practical work will therefore involve:
1. Synthesis of biomaterials with varied cross-link density, measured by spectrophotometry, FTIR and DSC.
2. Mechanical characterisation of the materials at nanoscale, using AFM, and macroscale using tensiometry. There is also scope for viscoelastic rheometric analysis.
3. Biological characterisation of cell viability, proliferation and differentiation, using alamar blue or MTS staining & expression of Ki67 & alpha-smooth muscle actin.
The outcomes will be:
1. An understanding of the extent to which mechanical strength of fibrin-alginate type biomaterial can be controlled through manufacture cross-linking,
2. The relationship between nano-scale and macro scale tensile strength
3. Gain insights into the effect of mechanical properties on cell differentiation and proliferation behaviour.
There will be scope to develop the project according to specific interests of the student, including the possibility of accommodating 2 students on complementary aspects of the project.

Student: (Project available)

Optimisation of a Laparoscopic Photoacoustic Imaging Probe

Supervisors: Adrien Desjardins and Wenfeng Xia

Photoacoustic imaging is a new modality that involves generating ultrasound in tissue with pulsed or modulated light. It provides information about the molecular composition of tissue that is complementary to the structural information provided by conventional ultrasound imaging. This modality has strong potential to be used in a clinical environment to guide minimally invasive procedures. Laparoscopic procedures in particular may benefit significantly from photoacoustic imaging guidance, as certain critical structures such as blood vessels and the bile duct are not always clearly visible with current techniques.
This project involves a modelling study to optimise a laparoscopic photoacoustic imaging probe using stochastic simulations of photon trajectories. The probe will be based on a commercial laparoscopic ultrasound imaging probe that is modified to include diffusers and reflectors for delivering light to tissue. Depending on the simulation outcomes, 3D printing may be used to create the probe.
The research will be conducted in close collaboration with clinicians from the Royal Free Hospital in London.
A background that includes one of the following topics: numerical simulation, ultrasound imaging, and photoacoustic imaging, is desirable but not required. A strong interest in medical imaging is essential.

Student: (Project available)

Cancer therapy: a sticky solution.

Supervisors: Paul Southern and Quentin Pankhurst, Richard Jackson

Treating cancer is hard, and we need new and improved weapons to add to our arsenal. Localised heating is one such weapon, which can easily be used alongside existing treatments like chemotherapy and radiotherapy. This project focuses on an exciting new type of localised heating, magnetic hyperthermia, which makes use of the natural ability of some magnetic nanoparticles to remotely heat up when placed in a rapidly switching applied magnetic field. In particular, we will address the problem of getting magnetic nanoparticles to ‘stick’ at the tumour site. What we will investigate is the use of a viscous gel-like material that will remain at the target site and only disperse after heating has taken place. We will therefore be making an entirely new type of magnetic hyperthermia agent. The student on this project will be expected to work in a multidisciplinary environment working alongside physicists, chemists and engineers, and will have access to a wide range of characterisation techniques including SQUID magnetometry, TEM, infra-red thermal imaging and magnetic hyperthermia.

Student: (Project available)

Numerical analysis of a mechano-biological model in wound contraction and the role of angiogenesis in soft-tissue regeneration.

Supervisors: Vasileios Vavourakis and Ryo Torii

Wound healing occurs in an ordered sequence of cellular interactions that involves endothelial cells, keratinocytes and fibroblasts. Three phases of wound healing with distinctive biochemical profiles have been identified, namely hemostasis and inflammation, proliferation, and maturation. The above phases are regulated by a complex network of interacting cytokines, chemical growth factors and their cellular receptors. Additionally, mechanical factors such as mechanical stress and blood supply play an important role in the system. A good supply of oxygen, nutrients and constituents is necessary for cell proliferation and growth, which is achieved by the formation of capillaries during angiogenesis.
The main objective of this MSc project is to develop an innovative deterministic mathematical model in order to elucidate the biological mechanisms behind wound contraction and the associated physiological healing processes, as well as better understand the particular influence of neovascularisation in tissue reconstruction. A key methodological innovation will be to couple a mechano-sensitive biochemical wound healing and wound contraction model that also accounts for the formation of new capillaries. The results of the proposed mathematical model could possibly be used to provide an explanation to pathological wound healing, such as in the case of neoadjuvant radiotherapy or chronic diabetes patients. Along these lines, we plan to utilize the developed computational framework to post-operative image data of breast cancer patients in order to validate the model as well as investigate its efficacy in real-life cases.
The proposed project is suitable for a student with an interest in continuum solid mechanics, with particular emphasis in soft-tissue mechanics, and in computer methods in Bioengineering. This will involve the extension of the existing numerical Finite Element framework developed by the supervisors, while the student will also be trained in various pre- and post-processing commercial and open-source software tools. A solid background in mathematics and physics, as well as knowledge in C/C++ programming languages is essential, while experience in mathematical modelling is a plus.

Student: (Project available)

Monte Carlo modelling of X-ray diffraction

Supervisors: Robert Speller and Nick Calvert

Tissue diffraction using X-rays can potentially play an important role in the diagnosis of disease. We are currently studying how this technique can best be applied in the detection of early breast cancer. However, to support our experimental studies we are developing modelling techniques. This project is to look at using GEANT4 (a Monte Carlo based programme) to include diffraction data on tissues. It requires an interest and some experience in computing. The eventual aim will be to see if we can reproduce our experimental results.

Student: (Project available)

Microstructure imaging of neuronal tissues

Supervisors: Ivana Drobnjak and TBC

Nerve injuries can be debilitating for patients, resulting in long term loss of sensation and movement control and the possibility of chronic pain. A variety of microsurgical approaches are used to repair damaged nerves, however there are difficulties associated with monitoring and quantifying the extent of nerve regeneration following injury. Current techniques tend to be limited to indirect measures of response to mechanical stimulation, and predictions based on crude estimates of neuronal growth rate.
This project is part of a larger study that aims to use a cutting-edge microstructure imaging (MI) techniques to visualize in-vivo and non-invasively the growth of the neurons. MI techniques involve a combination of Diffusion MRI, mathematical tissue models and computational methods to assess neuronal growth.
The project will focus on using MI techniques on an in vitro bioengineered nerve tissue model, and quantifying neuronal regeneration using microstructure indices such as axonal diameter, density and orientation. The student will assist in growing and doing histology of the bioengineered nerve tissue, and will run analysis of the imaging data in order to retrieve the microstructural information.
The project requires some experience in Matlab programming. The student will gain skills in computational modelling, data analysis and various tissue engineering and microscopy techniques. They will also get familiar with some basic concepts of Diffusion MRI and computational modelling.

Student: (Project available)

Wearable, wireless optical imaging system

Supervisors: Nick Everdell and Danial Chitnis, and Rhys Williams

The Biomedical Optics Research Laboratory (BORL) is developing a wearable, wireless optical imaging system. This is a device that uses near-infrared light to image the brain (for more details click here). It will produce real-time video of brain activity, with a wide range of potential applications, both clinical and consumer based. This project involves the mechanical design of the optical array that will be applied to the scalp. We will use a design package such as Autodesk Inventor, employing 3D printing and laser cutting technology to prototype the designs. This is very much a hands-on project, and will suit a student with previous practical experience.

Student: (Project available)

Radioisotope mapping using RadICAL

Supervisors: Robert Speller and George Randall

RadICAL is a detector system designed for localising radioactive isotopes. Several versions have been designed and built and this project is to enhance the sensitivity by making studies of the best way to couple the active scintillation element of the detector to the photodetector. This can be approached in several ways and will depend upon the skills of the student. A Monte Carlo modelling approach could be used or just experimental measurements. Different designs would need to be tested and this will require some building of equipment probably using the facilities available in Institute of Making. Testing would be undertaken in the Radiation Physics labs using radioactive isotopes. There might also be the option of testing the designs with a scintillator capable of mixed field detection. This would be relevant in estimating neutron fields in proton therapy.

Student: (Project available)

Phantom design for x-ray diffraction evaluation in breast cancer

Supervisors: Robert Speller and Christiana Christodoulou

The aim of this work is to design, test and manufacture phantoms for breast cancer research with a range of different materials of interest and study their diffraction profiles in energy dispersive X-ray diffraction. This will involve the student to use equipment in Make Space for fabricating the experimental phantoms.

Student: Corey Drakes

Novels method of breast cancer diagnosis using X-Ray diffraction

Supervisors: Nik Vassiljev and Robert Speller

A novel method of breast cancer diagnosis using X-Ray diffraction technique is currently being developed at UCL. A new wide area pixelated detector called DynAMITe (Dynamic range Adjustable for Medical Imaging Technology) has been designed and built for this purpose. It provides the possibility of obtaining mammography images and is sensitive enough for detecting diffraction pattern.
The first step of our method involves taking a large area transmitted X-Ray image of a biological sample (or a phantom). After a certain point or area of the sample is classified as suspicious it is moved into the centre of the beam and aligned with a collimator which in case significantly reduces the X-Ray beam cross section. The subsequent measurement then allows to obtain diffraction information from this specific point and can be used to classify tissue type as healthy or malignant.
Currently the system is controlled by a number of different applications separately (image acquisition, temperature controller, stepper motors, etc.). This project will involve developing a software package that will integrate the steps described above into a single application and thus greatly reduce the complexity of operation adding some basic data analysis which will produce results in real time valuable for diagnostician. This project is suitable for a student with strong interest in computer programming and system design.

Student: (Project available)

Algorithm for estimation of degree of pronation at the subtalar joint.

Supervisors: Marta Betcke(CMIC) and Dave Hawkes (CMIC), Andy Goldberg (RNOH)

The foot and ankle are complex structures made up of more than 30 bones. Each of the bones are connected by a complex network of ligaments and controlled by numerous muscles. The complexity of the structure reflects the complexity of activity for motor function (delivering propulsive loads and absorbing impact loads), and control (dynamic stability and balance) during walking, running, jumping and standing on the flat and uneven surfaces. Pronation or collapsing foot arches is a common problem affecting perhaps 50% of the population and indeed, many sports manufacturers nowadays have developed special shoes for athletes that pronate.
The objective of this project is to develop a scientific methodology and an algorithm for measuring the degree of pronation that takes place at the subtalar joint (a complex joint in the foot) from cone beam CT data collected while the patient is standing using a state of the art CurveBeam pedCAT scanner. The student will closely collaborate with clinician at RNOH, who will provide data, and assist in its analysis. The student will develop a tool chain to extract the information from the pedCAT data, including integration of a suitable segmentation algorithm, developing a mathematical method for measuring the degree of the overlap of the calcaneus and talar bones, and its implementation for integration in the petCAT software. We believe that results could be publishable in a high quality peer reviewed journals.

Student: (Project available)

Fabrication and testing of electrodes for Electrical Impedance Tomography of brain and nerve function.

Supervisors: David Holder and TBC

EIT is a novel medical imaging method, with which images of the electrical impedance of the head can be produced with a box about the size of a paperback book, laptop and EEG electrodes on the head. It is portable, safe, fast and inexpensive. The supervisor’s research has been to develop its use in imaging functional activity in the brain. One possible use could be to image increases in impedance which occur during fast neural activity over milliseconds; the impedance of nervous tissue falls by about 1% as ion channels open. We have recently demonstrated that EIT could be used for this purpose in imaging activity in the cerebral cortex in the brain, using an array of electrodes placed on exposed brain. We are also developing its use for imaging activity in individual nerves, using a cuff of electrodes wrapped around the nerve.
To achieve this we are using miniature electrode arrays with 30-60 electrodes 0.6 mm in diameter. These are fabricated using laser cutting of stainless steel foil on a backing of silicone rubber. The research requires the use of mats with different geometries and closer spacing. The project will be to make and test new designs with more closely spaced electrodes, as this will give a better spatial resolution in the EIT images.
To start with, the student will learn how to use CAD software to produce geometric plans for the laser cutter, and then electrode fabrication in a clean room. They will then observe different studies in the group and design and test different geometrical designs and sizes.
Skill to be acquired : Electrode fabrication using laser cutters, CAD software, experimental design.

Student: (Project available)

Feasibility of using Electrical Impedance Tomography (EIT) to image fast neural activity in sympathetic ganglia in humans.

Supervisors: David Holder and Kirill Aristovich

EIT is a novel medical imaging method, with which images of the electrical impedance of the head can be produced with a box about the size of a paperback book, laptop and EEG electrodes on the head. It is portable, safe, fast and inexpensive. The supervisor’s research has been to develop its use in imaging functional activity in the brain. One possible use could be to image increases in impedance which occur during fast neural activity over milliseconds; the impedance of nervous tissue falls by about 1% as ion channels open. We have recently demonstrated that EIT could be used for this purpose in imaging activity in the cerebral cortex in the brain, using an array of electrodes placed on exposed brain.
This project will be to investigate the feasibility of using EIT in a similar mode to record functional activity in sympathetic ganglia. This are small groups of nerve tissue typically about a cm across, which relay autonomic activity in human subjects. They occur along the spinal cord. Their activity could be measured using a catheter tip placed nearby with a convex outward facing array of longitudinal electrodes placed around the catheter and EIT.
In the project, the student will initially review the literature on the histology and function of these ganglia. They will then construct a Finite Element Model of the ganglia and catheter and model whether such measurements are likely to be feasible. It time permits, this will be supported by measurement in a saline filled bath or animal studies.
Skills to be acquired: Students will spend time in the lab in Medical Physics at UCL learning relevant methods Skills to be acquired will include Finite Element Modelling software; programming using Matlab; Electrical Impedance Tomography; experimental design and data analysis.
The project is suitable for a single student.

Student: (Project available)

Electrical Impedance Tomography (EIT) of evoked physiological activity.

Supervisors: David Holder and Kirill Aristovich

EIT is a novel medical imaging method, with which images of the electrical impedance of the head can be produced with a box about the size of a paperback book, laptop and EEG electrodes on the head. It is portable, safe, fast and inexpensive. The supervisor’s research has been to develop its use in imaging functional activity in the brain. One possible use could be to image increases in blood volume which occur over some tens of seconds during normal brain activity, such as during the standard clinical techniques of stimulation of the visual system by flashing lights or the somatosensory system by mild electrical stimulation at the wrist. Such imaging can already be performed by fMRI (functional MRI); the advantages of EIT are that similar images could be acquired with portable much less expensive technology which would increase its availability. EIT data has been collected in these situations before and led to a landmark publication in which reliable single channel data were observed but, unfortunately, the data was too noisy to form into reliable images. Since then, the electronics and imaging software have been improved – for example, we can now collect images at multiple frequencies whereas before they were only collected at one. This gives greater opportunities to reduce noise.
To start with, the student will evaluate a new EIT imaging system, the Swisstom, in a saline filled tank and compare its performance with an older UCLH EIT system. If time permits, then students will work together to collect EIT data during repeated evoked activity in about 10 healthy volunteers, and then will help produce images using Matlab code written for this purpose. Digital photos will be taken around the head, and then photogrammetric software will be used to localise their positions. Images will be reconstructed using an MRI of the patient’s head, which needs to be converted to a Finite Element model with software for segmenting medical images and meshing them. The accuracy of these images will be compared with similar studies using fMRI.
Skills to be acquired: Students will spend time in the lab in Medical Physics at UCL learning relevant methods and analysing the data, and some time in Prof Holder’s department at UCH, learning how to collect evoked responses using scalp electrodes. Skills to be acquired will include one or more of: medical image reconstruction; photogrammetric software use; medical image segmentation and meshing software; EEG electrode placement and use; experimental design and data analysis.
The project would be suitable for a single student or a team of 2 or 3, with a backgrounds in physics, engineering, computing, or medicine.

Student: (Project available)

Optimising postprocessing of cerebral Arterial Spin Labelling MRI data

Supervisors: Enrico De Vita and TBC

Arterial spin labelling (ASL) is a non-invasive MRI method for measuring tissue perfusion. The method has been used most widely in the brain to measure cerebral blood flow (CBF), but its application has so far been mostly restricted to research studies also due to limited availability of the sequence on clinical scanners. Recently a consensus white paper has been published recommending a common strategy for acquisition and a simplified data quantification approach. The main MR scanner manufacturers are also starting to provide product ASL sequences. It is thus very likely that ASL sequences will be increasingly used in clinical settings.
One of the open questions regarding ASL data is how to process the data in order to get the best estimate of cerebral blood flow, as it appears that different strategies are used at different sites/institutions and in some cases user intervention is called for before quantitative or semiquantitative maps can be generated.
As part of a European collaboration project (COST) we have put together a group of data consisting of several datasets from each of 10 separate sites where different acquisition sequences are used. These data differ in both labelling module (different ways of introducing perfusion weighting (e.g. pulsed or pseudo continuous ASL) and acquisition module (e.g. sequential 2D or 3D acquisitions) as well as other sequence details (e.g. application of background suppression to reduce signal from non-perfused tissue).
It is common experience that acquisitions performed on healthy, young, cooperating volunteers, can provide high quality, highly reproducible datasets, with low occurrence of motion artefacts. Data from patients with dementia, epilepsy or other conditions are in contrast often characterised by low reproducibility or corrupted by involuntary motion.
The main aim of this project is to evaluate and compare a number of motion correction algorithms and outlier rejection strategies, explore how these 2 steps interact and how they should be applied/combined to achieve the best estimate of CBF from each available dataset, as well as a measure of the reliability of this estimate.
This project will involve the student learning about ASL MRI data acquisition, quantitation and processing as well as methods for statistical comparisons. The student must be mathematically capable. Matlab skills are required as a minimum, experience in shell scripting, Python or C++ could also be beneficial. Freely available software will be used, including, but not limited to, FSL (, SPM (<>), Niftyreg (

Student: (Project available)