MSc Projects 2013-14

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.

Canonical Correlation Analysis in the Study of Cerebral Interrelations with Systemic Variables: Application in Acute Brain Injured Patients.

Supervisors: Dr Ilias Tachtsidis and Dr David Highton

Brain functional near-infrared spectroscopy (or fNIRS) is a technique that uses non-invasive optical reflection measurements to monitor brain tissue haemodynamics and oxygenation by resolving the concentrations of oxygenated haemoglobin (HbO2), deoxygenated haemoglobin (HHb) and brain tissue oxygen saturation (or tissue oxygenation index (TOI)). For several years we have been using this technique to monitor brain tissue physiology and pathophysiology in acute brain injured patients in the neuro-intensive care unit. In addition to our fNIRS measurements we monitor intracranial pressure, blood pressure, arterial saturation and other systemic physiological variables. One of our main interests has been deciphering the relationship between the brain fNIRS measurements with: (1) other brain physiological measurements such as intracranial pressure and; (2) the systemic physiology such as blood pressure. The aim of this project is to use canonical correlation analysis (or CCA) to investigate the above interrelationships.
CCA is a statistical method that analyzes the interrelation between variables in multi-dimensional datasets. CCA can be seen as an extension to normal correlation analysis, in which the proximity between two multidimensional datasets, instead of vectors, is analyzed by means of canonical angles. CCA determines how strongly the variables in both datasets are related. It is also possible to determine which and how many of the independent variables explain most of the variation in the dependent dataset.
The student will be using a CCA toolbox that was developed in our lab with multidimensional data set that collected in the neuro-critical care unit from acute brain injured patients. The project will involve some development on the analysis methodology and will require from the student to analyse data and do some statistics. This project is mainly computational and will be suitable for a student with a general interest in monitoring brain physiology, fair knowledge of MatLab and signal processing methods.

Student: (Project available)

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: Dr Ilias Tachtsidis and Dr Aaron Oliver-Taylor

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: Lukas Beichert

Measuring blood flow and oxygenation on exposed spinal cord in a multiple sclerosis preclinical model.

Supervisors: Dr Ilias Tachtsidis and Dr Andrew Davies

Measuring the tissue blood flow and the oxygen levels for an organ allows us better understanding of the physiological status of that organ. This is important during pathology when disease can affect the organs tissue blood flow and oxygenation. For example in Multiple Sclerosis (MS) there is evidence to suggest that inflammation areas suffer from inadequate oxygenation. The oxygen shortage, termed hypoxia, is severe enough to cause two of the important problems in MS: loss of function (causing symptoms such as paralysis,) and damage to the cells that put the insulation (myelin) on the nerve fibres, resulting in demyelination. While the hypoxia is clearly important, we do not know whether it is due to a reduced blood flow, or an increased demand for oxygen, or both. To investigate this we propose to use novel state of the art photonic systems that can measure (i) blood flow changes by utilising laser Doppler technology and (ii) oxygenation by utilising near-infrared spectroscopy. The student will be using these photonic systems on the exposed rat spinal cord before and after inflammation has been induced. In particular the student at the beginning will be using a state of the art laser Doppler instrument from Moor Instruments to collect flow data on the exposed spinal cord and identify the physiological changes that MS induces. Following that the student will modify and use a recent developed broadband near-infrared spectrometer that will allow to measure oxygenation and metabolism as well.
This project is a mixture of development, experimentation and 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 UCL Department of Neuroinflammation that investigates multiple sclerosis (MS) with a broad range of research from basic neuroscience to clinical translation, including therapeutic trials.

Student: Panagiota Seferli

Investigating the measurement of brain tissue light scattering in traumatic brain injury patients.

Supervisors: Dr Ilias Tachtsidis and Dr David Highton

Monitoring the tight balance of brain blood flow, oxygen delivery and brain tissue metabolic rate is a major aim in patient diagnosis and care. A patient’s health is in great danger when there is a prolonged lack of oxygen delivery to meet the metabolic demand of the tissue; for example in traumatic brain injury. To achieve these measurements we have developed and use optical systems based in near-infrared spectroscopy technology that can resolve the major oxygen dependent chromophores (haemoglobin) by quantifying the changes in light absorption. Recently and as part of an on-going collaboration with Hamamatsu photonics (Japan) we have access to a state-of-the-art optical system that can quantify independently brain tissue light absorption and light scattering. These two independent light measurements are affected by different components and processes in the tissue. As part of this project the student will investigate how the light tissue scattering signal of the brain is affected and evolves in traumatic brain injury patients during hospitalisation. It is of particular interest the effect of brain oedema and brain ischaemia will have in light scattering. This project is a mixture of experimentation and signal/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 National Hospital for Neurology and Neurosurgery where patient’s recruitment will be done.

Student: Alison Tucker

Evaluation of inverse source modelling of the cortical evoked response in the anaesthetised rat in comparison with Electrical Impedance Tomography of fast neural activity.

Supervisors: Prof. David Holder and Dr 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. It could be used for imaging fast neural activity over milliseconds, which would constitute a major advance in neuroscience methods. Research into this is currently being undertaken in the anesthetized rat. The brain is exposed and recording is undertaken during repeated physiological stimulation of the sensory or visual systems. Recording is undertaken with a mat of 32 cortical electrodes about 6 mm square. EIT data is recorded at the same time as the brain’s own response as well as other data such as intrinsic optical imaging. Inverse source modelling of the EEG is a method in which the origin of electrical signals is calculated from boundary voltage signals – in this case, the evoked response signals recorded from the epicortical electrode array. In this project, inverse source modelling software from the SPM suite developed at UCL will be adapted for use with the EIT rat data. The results of source modelling will be compared with EIT data. Skills to be acquired: programming in Matlab, learning SPM, signal processing, and data analysis. All data will be provided by medical researchers. The project is suitable for a student with a background in physics, engineering or computing, or a medical student with computing experience and an interest in programming.

Student: (Project available)

Real-time implementation of an algorithm for removing artefact from the EEG in Electrical Impedance Tomography (EIT) of epileptic activity.

Supervisors: Prof. David Holder and Dr Gustavo Santos

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 exciting application lies in its use to image changes in the brain due to epileptic activity. In epilepsy, abnormal activity may occur in the form of seizures in which there is continuous abnormal activity lasting a minute or so. EIT could be used to provide a uniquely new method for imaging brain activity in such seizures which could be used in surgery for epilepsy. In order for this be realised, EIT needs to be recorded at the same time as EEG over several days in patients on a ward who have been specially brought in for observation. Both are recorded with about 20 electrodes glued to the scalp. Unfortunately, the EIT injects an artefact into the EEG signal. A method for removing this has been developed but it currently takes several minutes of post-processing off-line after the EEG has been acquired. As some clinicians need to see real-time EEG at the bedside as it is collected, it is desirable to run the cleaning algorithm in real time. The purpose of the project will be to implement and test real-time implementation of the algorithm. Initially, the student will read relevant background literature and become familiar with the algorithm. They will then develop a way to run it in real time, initially on a PC running in parallel with commercial EEG software. If this is not sufficiently fast, then other methods to speed up processing will be investigated, such as the use of a parallel Graphical Processing Unit added to the PC. Skills to be acquired : programming in Matlab, C or C++, signal processing, and biomedical instrumentation. The project is suitable for a student with a background in physics, engineering or computing, or a medical student with experience and an interest in programming.

Student: (Project available)

Electrical Impedance Tomography (EIT) of evoked physiological activity.

Supervisors: Prof. David Holder and Dr 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 or a team of students will evaluate two new EIT imaging systems, the Kyung Hee Mk 2.5 and the Swisstom, in a saline filled tank and compare their 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 backgrounds in physics, engineering, computing, or medicine.

Student: (Project available)

Evaluation of the performance of a new commercial Electrical Impedance Tomography system in liquid filled tanks.

Supervisors: Prof. David Holder and Camille Blochet

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. Applications include imaging during epileptic seizures or in acute stroke. Until this year, all EIT systems were individually built in research laboratories but now a commercial system, the Swisstom, has become available. The signals for producing EIT images are small and at the limit of detectability, especially for imaging in the head, and this system has not yet been evaluated for this application. The purpose of the project will be to determine the performance of this new system using anatomically realistic liquid filled tanks, printed to resemble the adult and neonatal human head with skull. It will be assessed both for differences over time as in epileptic seizures, and for “one-off” imaging, as for acute stroke. Technical factors, such as the pattern of applied currents may then be modified in order to improve the performance. The output will be a feasibility analysis of whether the system is suitable for these applications, with recommendations for any needed improvement. Skills to be acquired will include one or more of: medical image reconstruction; familiarity with EIT systems and their use; programming in Matlab; experimental design and data analysis. The project is suitable for a student with a background in physics, engineering, computing, or a medical student with an interest in learning technical skills and programming.

Student: (Project available)

Modelling patient throughput in a radiotherapy department

Supervisors: Dr. Adam Gibson and Dr Andy Chow

The radiotherapy department in a hospital is busy, with patients moving from room to room for different scans and tests, and returning for repeated treatment fractions. In this project, you will develop a computer model of patient flow through the department, which will allow us to predict the impact of changes to working practise on the efficiency of the department. The project will involve writing and developing a computer model, testing it against measured patient throughput and using it to predict the effect of new working practises.

Student: (Project available)

Artefact rejection in x-ray CT imaging

Supervisors: Dr. Adam Gibson and Dr Jenny Griffiths

Small, dense areas in CT images such as fillings or metal implants cause significant errors in the reconstructed image. CT images are used to plan radiotherapy treatments, but if the patient has a metal implant near the area being treated, the artefact in the image can lead to significant errors. You will write a computer program to reconstruct CT image and investigate ways to improve the image reconstruction to make it more tolerant of metal implants. This project will require mathematics and computer programming.

Student: Edward Woon

Automated neonatal monitoring

Supervisors: Dr. Adam Gibson and Dr Topun Austin

We have recorded about 2 weeks of multichannel monitoring data on 9 babies in intensive case. We have already showed that intelligent analysis of this data can identify adverse events. In this project, you wll carry out further analysis, to determine the analysis technique which maximises the sensitivity and specificity with which the system can identify adverse events. This project will require mathematics and computer programming.

Student: (Project available)

Acoustic emissions from a proton beam

Supervisors: Dr. Adam Gibson and Dr Ben Cox

We have a well-established research group in the area of photoacoustics. When tissue is illuminated with near infrared light, the blood vessels absorb energy, heat and expand, producing an acoustic wave which can be measured at the surface. In this project, you will investigate the extension of photoacoustics to detecting acoustic emissions from a proton beam in tissue. When protons are used for radiotherapy, energy is deposited in tissue and there is some evidence that acoustic waves can be generated. You will use existing software to determine the dose deposition from a proton beam, and then use that in a computer model of acoustic propagation to investigate the signal size for a range of different conditions. The outcome will be a recommendation as to whether this method could be used clinically. This project will require some mathematics and computer programming.

Student: (Project available)

Skin sparing in proton therapy

Supervisors: Dr. Adam Gibson and Catarina Viega

In standard photon radiotherapy, the radiation dose gradually increases over the first centimetre or so, meaning that the dose to the skin is reduced. Protons can be used for radiotherapy instead of photons, but their behaviour at very small penetration depths is less well understood. In this project, you will create a simple Monte Carlo model of proton transport in tissue and study the dose distribution in the skin. This will be supported by a review of the physics of proton propagation, to develop a thorough understanding of skin sparing in proton therapy and how it can be utilised in clinical practice. This project will require some mathematics and computer programming.

Student: Changju Kim

Investigation of sampling schemes for coded aperture based phase contrast computed tomography.

Supervisors: Charlotte Hagen and Dr. Alessandro Olivo

X-Ray Phase Contrast Imaging (XPCI) is a novel radiographic method that relies on refraction for contrast generation rather than on absorption. This has the implication of providing improved soft tissue contrast, an important feature urgently demanded in many biomedical disciplines. Until lately, XPCI has been restricted to synchrotron facilities due to demansing requirements on the x-ray source. Our group at UCL has developed a method for performing PC imaging also with conventional x-ray equipment.

The method, known as Coded Aperture (CA) XPCI has recently been combined with the concepts of computed tomography, solving the problem of overlapping structures in a single projection radiograph and making fully 3D image reconstruction possible. Tomographic data acquisition requires the rotation of the object by an angular range of at least 180 degrees. Due to the specific working principle of the CA XPCI method, an additional option for the horizontal scanning of the sample at every rotation angle exists.

The horizontal scanning can result in an improved spatial resolution in the reconstructed image. However, the relationship between the number of scanning steps and angular views, and the implication of both for the reconstructed image, has not yet been fully understood. This would be investigated by the student using already available, custom-built simulation software.

Besides being involved in the development of a new imaging method, the student would familiarize with the basic concepts of computed tomography and image reconstruction.

Some familiarity with the basics of data simulation and analysis using the software platform Matlab is necessary.

Student: (Project available)

Characterization of detector performance for x-ray phase contrast imaging.

Supervisors: Dr. Marco Endrizzi and Dr. 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.

Some familiarity with the basics of data acquisition and analysis are necessary.

Student: Project taken by UG student

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

Supervisors: Dr. Paul Diemoz and Dr. 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)

Biomechanical Modelling of the Breast for MRI to X-ray Mammography Registration.

Supervisors: Dr. John Hipwell and Dr. Thomy Mertzanidou

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. X-ray mammography and Magnetic Resonance Imaging are commonly used for detection and diagnosis on breast cancer, and radiologists often need to identify corresponding structures between them. As part of a previous European project, an image registration framework has been developed for this task in CMIC that uses a biomechanical model of the breast in order to simulate the large deformation that the breast undergoes during X-ray mammography acquisition. The aim of this project is to investigate further the influence of the various registration components on the final results, with a goal to improve the registration accuracy. More specifically, the focus will be on the biomechanical parameters that are used to build the breast models, aiming at producing models that represent more accurately the breast anatomy and also give more physically realistic deformations. The student will become familiar with breast biomechanical modelling, large compression simulations of soft tissue and their to application multi-modal image registration.

Student: Project is taken

Breast Cancer Risk Factors.

Supervisors: Dr. John Hipwell and Dr. Bjorn Eiben

The lifetime risk of being diagnosed with breast cancer in women is 1 in 9 and breast cancer is now the most common cancer in the UK with more than 44,000 women being diagnosed with breast cancer each year.

In the literature various factors have been reported that influence the probability of developing cancer. These include (1) Hormonal factors such as age of menarche and menopause, and age at which children are born, (2) environmental factors such as smoking, (3) family history and (4) known gene mutations. There is also an established link between the crude, manual measurement of ``breast density'' from X-ray mammograms and breast cancer risk.

This project will focus on developing a more accurate measurement of breast density using MR images rather than X-rays and investigating whether textural analysis of mammograms can be used to distinguish two cohorts of data: a set of normals and a set of women who are known to have developed breast cancer. This work is being performed in collaboration with epidemiologists at the London School of Hygiene and Tropical Medicine, and radiologists at Dundee and St Bartholomew's Hospital.

Student: Project unavailable

Characterising and Overcoming Susceptibility Artifacts in Mouse Brain MRI at 9.4 Tesla.

Supervisors: Dr. Karin Shmueli and Dr. 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 and understanding the sources of these susceptibility artifacts, and potentially developing and applying practical methods to overcome the artifacts to improve 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. 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. The project may also involve simulating, testing and evaluating potential artifact reduction methods such as passive shimming which involves introducing and positioning a diamagnetic substance (with negative susceptibility) so as to cancel some of the large magnetic field inhomogeneities that lead to artifacts.

You will use Matlab (or other software) to analyse MRI and CT data and calculate and simulate magnetic susceptibility and field maps. Experimenting on the 9.4 Tesla MRI system at the Centre for Advanced Biomedical Imaging (CABI), you may implement and evaluate the effectiveness of passive shimming for reducing susceptibility artifacts in the mouse brain.

Student: James Breen-Norris

Image-guided, adaptive proton therapy

Supervisors: Dr. Jamie McClelland and Catarina Veiga

University College London and its partner hospital are building a highly advanced clinical proton therapy (PT) facility, which will be operational in 2017. PT is an advanced form of radiotherapy (RT) capable of providing a dose map which conforms much better to the shape of the tumour and gives a lower dose to surrounding healthy tissue than conventional RT. However, a problem with PT, as with conventional RT, is accounting for changes to the patient during the course of therapy, which can last several weeks.
UCL, together with UCLH, has recently developed an advanced image registration based framework for studying the dosimetric effects of changes to the patient during therapy. This uses weekly cone-beam computed tomography (CBCT) scans acquired of the patient setup in the treatment position to image changes to the patient’s position and anatomy. Deformable image registration is used to map the patient’s anatomy and dose distributions between different scans. This method is now undergoing clinical studies at UCLH for patients undergoing conventional RT treatment. This project will investigate applying the same methodology to PT treatments where the dosimetric impact of changes to the patient has yet to be studied.
This project will require a basic understanding of RT and PT and how the treatment is planned and delivered, and the ability to analyse data and program in MATLAB. It is not essential the student has all these skills at the start of the project but they must be eager to learn them.

Student: Peter Bruton

Vision Based Registration For Laparosopic Surgery.

Supervisors: Dr. Matt Clarkson and Steve Thompson, Danail Stoyanov, Dave Hawkes

CMIC has a long-standing track record in many forms of image registration, and is now rapidly expanding its interests in image guided surgery. In conjunction with surgeons at the Royal Free Hospital, CMIC have recently developed a pre-clinical image guided laparoscopic surgery system for use in liver resection.

An MSc project is offered that would suit someone with a strong (preferably C++) programming background. Currently the position and orientation of the laparoscope is obtained via optical tracking of the distal end of the laparoscope. It has been shown that tracking with an NDI Certus provides a registration accuracy of approx 2mm and with an NDI Spectra provides a tracking accuracy of approx 4mm, validated using a known, fixed point. This may not be good enough for highly accurate applications. This project will investigate surface reconstruction, or purely vision based methods for improving the registration. Validation can be performed using a previously designed, custom made, liver phantom, and software integrated within the NiftyIGI system.

The interested candidate should contact Matt Clarkson in the first case, to chat about suitability. Furthermore, depending on the timing of the project, the exact objectives can be modified to suit the needs of the project at the present time.

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: Dr. 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)

Novel Measurement of Cardiac Output.

Supervisors: Dr. Julian Henty and Paul Burke

Cardiac output (CO) is measured routinely in both critically ill ICU patients and complex theatre procedures, but can be notoriously difficult to measure and only achieved with the use of minimally invasive techniques. It is performed using either oesophageal Doppler or lithium dilution injection.

In this project, we wish to investigate the effect of combining parameters such as conventional ECG and fast continuous pulse pressure measurement to determine stroke volume and hence CO.

The project will involve synchronous measurements of parameters (possibly requiring simple hardware) and performing real time processing of data using LabVIEW. Testing will be performed on a stationary bicycle in the lab and it may also be possible to compare with conventional theatre CO technology.

Student: (Project available)

Ultrasound modulated optical tomography: potential in clinical monitoring

Supervisors: Dr. Terence Leung and Samuel Powell

What is ultrasound modulated optical tomography (UOT)? It is an emerging technology that combines light and ultrasound to achieve a localised oxygenation measurement. Currently, clinical oxygenation measurement is often performed using near infrared (NIR) light alone, a technique known as diffuse optics. By incorporating ultrasound, UOT has been shown to improve the localisation of the oxygenation measurement, achieving a spatial resolution better than diffuse optics. The aim of this project is to investigate the potential of UOT in different clinical scenarios through a series of computer simulations using a software package developed in our lab over the past few years. This in-house software package allows the modelling of the interaction between ultrasound and light in a variety of 3D heterogeneous turbid media. The investigation will cover a range of tissue geometries and configurations of ultrasound transducer and optical source/detector. The results from this project will inform the design of an UOT probe to be used in the hospital. This project will suit a student who is interested in computer simulations, numerical modelling, ultrasound and optical technology.

Student: Kehao Wang

Designing and building a miniaturised Electroencephalography (EEG) system for use in multimodal imaging.

Supervisors: Danial Chitnis and Nick Everdelll

Electroencephalography (EEG) is a method of measuring the electrical activity of the brain using multiple electrodes placed on the scalp. EEG is widely used as a clinical instrument to assist in the diagnosis of several neurological conditions. In a conventional EEG system, the electrical activity on the scalp is captured by an electrode which consists of a high impedance probe with a high gain operational amplifier. The amplified signal is then digitized, interpreted and displayed on a PC. In order to achieve a higher spatial resolution, multiple channels are introduced which increases the amount of wiring needed and overall complexity of the system. Our research group is going to explore a new high density EEG system in which the analogue and digital processing is performed within the electrode itself, thus reducing this complexity. A novel mechanism will be used to transfer digital data from multiple electrodes via a digital bus to the PC. The student would be involved in the design and building of the EEG system including the novel electrodes. This project will include analogue circuit design, and state-of-the-art digital data capturing techniques. After the system is built, it will be tested, and the difference between the performance of the new system and a conventional EEG system will be investigated.

Student: (Project available)

Investigating methods for analysis of Diffusion Weighted Magnetic Resonance Images.

Supervisors: Dr Ivor Simpson and Dr Nicolas Toussaint and Dr Enrico De Vita

Diffusion weighted magnetic resonance imaging (DWMRI) has been demonstrated to be effective in describing the microstructure of the human brain in-vivo, and has been used in several clinical studies of neurodegenerative diseases. The choice of DWMRI sequence requires the tradeoff between spatial resolution and signal-to-noise ratio for a given scan time. As such, data acquired at different resolutions may provide complementary information that would allow an improved estimation of brain microstructure. To investigate this, a longitudinal cohort of healthy control data has been acquired at both a 2mm and 2.5mm spatial resolution.

The principal aim of this project is to develop a principled statistical framework to fuse DWMRI data acquired at two resolutions. Further work will involve investigating the benefits of this with respect to a longitudinal clinical study on patients suffering from Prion-induced neurodegenerative disease. This project will involve the student learning about DWMRI processing, probabilistic data modelling and statistical inference schemes. The student must be mathematically capable. Matlab skills are required as a minimum, but Python or C++ would also be beneficial.

Student: (Project available)

Simulating MRI of the human brain

Supervisors: Dr Ivana Drobnjak

Magnetic Resonance Imaging (MRI) is an extremely important non-invasive technique for “looking into” the human body. As such, it needs constant improvement and further development so that we can accurately and efficiently use it in its expanding range of applications from diagnosing breast cancers and brain tumors to Alzheimer’s disease and Multiple Sclerosis. MRI simulations are an invaluable tool in this process. They can predict variety of realistic situations and propose strategies for further development of MRI.

So far, a computational model of the MR image acquisition process was built, POSSUM, which uses a geometric definition of the object (brain), Maxwell’s equations (to model the magnetic field in the scanner), Bloch equations (to model the behaviour of the nuclear magnetisation). POSSUM forms a part of the FMRIB Software Library (FSL) that is a very widely used analysis package for FMRI (used in over 600 laboratories worldwide).

This project aims to use the current POSSUM software to make simulations of the patient motion in the scanner, and then apply it in order to look into the potential effect it can have on MR images. The project would involve running large scale software simulations in C++ language. The project would also involve learning about Magnetic Resonance Imaging.

Student: (Project available)

Looking into the brain using MRI simulations

Supervisors: Dr Ivana Drobnjak

In many brain diseases such as Alzheimer’s or multiple sclerosis, the microstructure of the brain changes and neurons change their size, density or organization. In order to be able to understand what is happening in the brain a way of “looking into” the brain is needed. However, measuring, non-invasively, the microstructure (e.g. the size of neurons) in a living brain is a big challenge.

In order to do so, we are using diffusion MRI, which is a magnetic resonance imaging (MRI) method that produces in-vivo images of biological tissues weighted with the local microstructural characteristics of water diffusion. Recent research showed that imaging strategy can be optimised to be neuron-size specific. For example, if we would like to image very big neurons (with big radiuses) we would use very low frequency diffusion gradients (magnetic fields in the scanner), while if those were very small neurons we would use high frequency ones.

This project focuses on understanding the relationship between the imaging sequence and the size of the neurons. Expressing the sequence in a parameterised form using, for example, frequency and magnitude of diffusion gradients, can do this. Finding analytical expression for the imaging protocols would significantly improve the way we image brain microstructure and could potentially benefit in diagnosing a wide range of diseases, in particular cancers and dementias.

Requirements for this project are: programming in MATLAB, some knowledge of MRI, and mathematics.

Student: (Project available)

Development of dynamic tissue-equivalent phantom for optical imaging of the brain, simulating rapid changes in blood volume in a superficial skin/scalp layer.

Supervisors: Jem Hebden and Nick Everdell

Optical imaging techniques are being developed at UCL as a means of imaging blood flow and oxygenation changes in the outer (cortical) regions of brain in response to certain stimuli and mental processing. Testing and evaluation of these optical methods and devices requires suitable objects with tissue-like optical properties, called phantoms. The aim of this project is to develop a phantom which mimics the optical properties of the head, including two dynamic features. First, it will consist of a solid slab (epoxy resin mixed with suitable scattering and absorbing substances) in which is inserted a discrete target containing a thermochromic pigment. The absorbing properties of the target is then reversibly changed by locally heating electrically. Second, a rotating solid layer with spatially-varying absorption above the slab will simulate rapid changes in scalp and skin. Following design and construction of the phantom, imaging experiments will be performed to verify the effectiveness of the phantom, using a so-called optical topography system developed at UCL. Further details about the imaging system can be found at:
www.ucl.ac.uk/medphys/research/borl/imaging/topography .
This project is most suitable for a student who enjoys building mechanical and/or electrical devices and has good manual skills.

Student: (Project available)

Image-guided biopsy and therapy for the diagnosis and treatment of prostate and liver cancers.

Supervisors: Dean Barratt

Contact supervisor Dean Barratt for further information.

Student: (Project available)

Reconstruction of three-dimensional confocal image volumes during minimally invasive procedures.

Supervisors: Adrien Desjardins and Danail Stoyanov

Description: confocal fluorescence microscopy (CFM) is an optical imaging modality that has recently received a great deal of interest for guiding minimally invasive procedures. UCL has recently acquired a CFM system that can acquire two-dimensional microscopic images of tissue in real time from the tip of a needle probe. Given that these images are acquired continuously as the needle probe is inserted into tissue, it should be possible to generate three-dimensional image volumes that could provide new insights about tissue architecture. This project has both experimental and computational objectives:
- Acquisition of two-dimensional image sequences in the context of motorised insertions through tissue phantoms
- Development of new algorithms for estimating the speed at which the probe is inserted, based on temporal correlations across the images
- Application of the algorithms to free-hand needle insertions with spatial resampling to obtain three-dimensional images

Skills required: familiarity with computational software such as Matlab. Previous optical and/or experimental experience would be welcome but is not required.

Student: (Project available)

Hand-eye Calibration for Image-guided Surgery

Supervisors: Danail Stoyanov

Description: Image-guided surgery and robotic assisted surgery can potentially improve oncological outcomes of interventional healthcare by superimposing multi-modal patient information (usually images) into once coordinate frame. This process is challenging because multiple devices are used and calibrating the different optical coordinate system in practice can be difficult and cumbersome. This project will investigate the calibration of individual devices (camera, optical tracker) and how these calibrations can be optimally combined for a practical system that can be used in the surgical theatre.
Skills required: familiarity with computational software such as Matlab (or C/C++). Good linear algebra knowledge. Background in hardware devices or image processing would be a bonus but is not essential.

Student: (Project available)

Correcting for head motion during PET/MR scanning

Supervisors: Kris Thielemans and David Atkinson

Background:

Positron Emission Tomography is an imaging modality that can provide functional information about processes in the body. However, structures in the brain are small compared to the PET resolution (~5mm). Results can therefore be affected by patient motion. Much research has been performed to correct for motion of the head. This is particularly important when comparing different conditions, potentially having different motion patterns. In multi-patient studies, it can also increase the power of a study.

On PET/MR scanners, several MR sequences are run while the PET is being acquired. It is possible to use information from these MR images to then correct the PET data. This works by using registration between the different MR images and passing the motion information to the PET image reconstruction. Alternatively, the PET data itself can be used to find any motion. This normally happens in a 3-stage process where PET data is first reconstructed without motion information, resulting images are registered, and the resulting motion information is then used during a second PET reconstruction.

Project aim:

investigate and compare the performance of MR-based vs. PET-based motion estimation to correct head motion during PET/MR scanning.

Requirements: Good computing skills. Familiarity with MATLAB or similar highly recommended.


References:
AJ Montgomery et al, “Correction of head movement in PET studies: a comparison of methods”, J. Nucl. Med. 2006 47: 1936-1944.
C Catana, "MRI-Assisted PET Motion Correction for Neurologic Studies in an Integrated MR-PET Scanner", ”, J. Nucl. Med., Vol. 52, No. 1. (2011), pp. 154-161, doi:10.2967/jnumed.110.079343

Student: (Project available)