MSc Projects 2011-12

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.

Clinical Translation of Gluco-CEST

Supervisors: Dr. Catherine Morgan and Prof. Xavier Golay.

Student: Christian Maass

In cancerous tissue, glucose uptake occurs faster than in healthy tissue, due to an increased metabolism of malignant tumours. Therefore, Positron Emission Tomography (PET) imaging of fluorodeoxyglucose (FDG), a glucose-equivalent molecule labelled with radioactive 18F, is the clinical standard for detecting malignant disease and monitoring treatment response. A disadvantage of PET however, is the use of ionising radiation. Magnetic Resonance Imaging (MRI) of 2-Deoxy-D-glucose uptake with 13C, has been attempted but has very poor sensitivity or requires access to a hyper-polarisation facility, and is therefore far from clinical use. Chemical Exchange Saturation Transfer (CEST) is a new MRI technique that has been shown to have good sensitivity to low concentrations of a simple glucose solution. We have recently applied Gluco-CEST in animal models of cancer and the results are very promising.

The aim of this project is to test and optimise the translation of Gluco-CEST into a clinical setting. The student will design and create a simple phantom suitable for our clinical 3T scanner. Optimising the scanning protocol for clinical use will be performed by: varying appropriate image acquisition parameters under the constraint of tolerable in vivo scan times; analysing the data (using existing software) for the different protocols; summarising and then presenting these results. Data will then be collected and analysed on the brain of healthy controls with the optimised clinical protocol, the organ in our body with the largest glucose metabolism. Dependant on the progress made, the student may also be involved in the acquisition, and processing of pilot data in cancer patients with gliomas.
The project would suit a student with a physics and / or computer science background. A basic knowledge of MRI, and some experience using Matlab, and imaging viewing and processing tools would be advantageous.

* This project is appropriate for Medical Image Computing stream students

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

Supervisors: Adrien Desjardins and Danail Stoyanov.

Student: Lumbani Munthali

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.

Wave and ray-optics approaches to x-ray phase contrast imaging.

Supervisors: Dr. Alessandro Olivo and Dr. Konstantin Ignatyev.

Student: (Project available)

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.
There are two basic ways of describing x-ray phase contrast imaging theoretically: one is rather rigorous and is based on Fresnel/Kirchoff diffraction integrals, whereas ray-optics offer a substantially simplified approach. The phase contrast group at UCL has developed software to simulate phase contrast images following both approaches. The student will be provided with this software and will use it to investigate under what set of conditions ray optics can be considered a satisfying approximation.
The student will gain skills in simulation methods, data analysis, image analysis and familiarize with the basic concepts of optics. Computing skills and a reasonably sound mathematical background are required.

Analysis of coded-aperture based x-ray phase contrast images of tumours in breast tissue.

Supervisors: Dr. Alessandro Olivo and Dr. Konstantin Ignatyev.

Student: Charalambos Kallepitis

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 in vivo station for x-ray phase contrast mammography at a synchrotron in Italy, despite the very limited number of patients that such station 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.

An Internet-Connected Ergometer with Virtual Races for therapy after Spinal Cord Injury

Supervisors: Prof Nick Donaldson

Student: Maureen Nyakaira

Conventional wisdom is that people with chronic spinal cord injuries will not recover. However from time to time it has been observed that individuals who have used electrical stimulation to exercise their paralysed muscle have improved. We want to do an experiment to see whether this improvement occurs when the person is trying to use their paralysed muscles during the stimulation. In preparation, we will develop a cycle ergometer that patients can use at home. A Portuguese engineering student will work on the mechanics of this system.
This project is about two other essential aspects. First, the subjects must be motivated, and for that we propose to use virtual races with a video display. These are sold as part of exercise equipment for able-bodied cyclists but must be adapted for our use. It will be important that these races remain challenging during months of regular exercise bouts. The second part is a method to record the exercise from sensors on the ergometer and make the data available to researchers. For example, by storing the data on a web page from where it can be downloaded.
The required skills will be in computers, audio-visual, programming and the internet.

* This project is appropriate for Medical Image Computing stream students

Computational investigation of optical spectroscopy algorithms to decouple optical measurements of brain oxygenation and metabolism.

Supervisors: Dr Ilias Tachtsidis and Tingting Zhu.

Student: Tushaar Madaan

Brain tissue near-infrared spectroscopy (or NIRS) is a technique that uses non-invasive optical head reflection measurements to monitor oxygenation by resolving the concentrations of oxygenated haemoglobin (HbO2) and deoxygenated haemoglobin (HHb); NIRS can also monitor changes in energy metabolism by resolving the redox state of cytochrome-c-oxidase (oxCCO). In order to resolve the three chromophores (HbO2, HHb and oxCCO) we employ a linear algorithm that relates the optical attenuation measurements with the concentrations of the chromophores scaled with their optical absorption characteristics and the total pathlength of light. This spectroscopic approach has the potential to cause crosstalk between the concentration measurements of the three chromophores – by crosstalk we mean a genuine change in a chromophore concentration inducing a spurious measured concentration change in another. The presence of this crosstalk artefact has been investigated by our group and others, both experimentally and using computational simulations. It has been suggested that “spectroscopic crosstalk” can be minimised by utilising a large number of optical wavelengths. However questions remain about: (1) wavelength combination; (2) wavelength resolution and bandwidth; (3) pathlength selection. This project requires the student to implement an optical spectroscopy algorithm based on previous work in our group and investigate the above questions using computational simulations. To enhance the simulations experimental data will also be available for analysis. This project is mainly computational and will be suitable for a student with a general interest in monitoring brain physiology and fair knowledge of MatLab.

* This project is appropriate for Medical Image Computing stream students

Identification and Quantification of Brain Tissue Haemodynamic Signals and Systemic Interference in Functional Near-Infrared Spectroscopy

Supervisors: Dr Ilias Tachtsidis and Dr. Heidrun Wabnitz.

Student: Na Yu

Brain tissue functional near-infrared spectroscopy (or fNIRS) is a technique that uses non-invasive optical head reflection measurements to monitor brain tissue haemodynamics by resolving the concentrations of oxygenated haemoglobin (HbO2) and deoxygenated haemoglobin (HHb). For several years we have been using the technique to monitor the brain haemodynamic changes secondary to brain neuronal activation during frontal lobe cognitive tasks (such as anagram solving). From these studies we demonstrated the ability of fNIRS to monitor brain function. However, we also identified certain limitations regarding the contamination of the brain fNIRS haemodynamic response from systemic originated changes such as blood pressure increases. As part of recent research collaboration with the Biomedical Optics department of Physikalisch-Technische Bundesanstalt (PTB) in Berlin, Germany we have performed a study in 14 adults using simultaneously a state of the art time-domain fNIRS system and multimodal systemic measurements (blood pressure, heart rate, breathing rate etc) during a word performing task. Preliminary results have identified systemic changes affecting the fNIRS measurements; however questions remain about the degree of interference. Further investigation is necessary and likely to include coherence analysis and/or independent component analysis and/or wavelet cross-correlation methods. 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.

* This project is appropriate for Medical Image Computing stream students

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 Nicola Robertson

Student: Esther Baer

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. Therapeutic hypothermia is an effective therapy for HI and is being introduced as standard of care in the UK. Although a significant advance in the treatment of asphyxiated babies, little is known about hypothermia’s effect on brain blood perfusion and metabolism following HI. This project is part of an exciting new 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 hypothermia’s effect through combination of magnetic resonance and optical (near infra-red) 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 both magnetic resonance and 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 this treatment in birth asphyxiated infants. This project would be suitable for a student with an interest in magnetic resonance and optical technologies, physiology/pathophysiology, brain tissue biochemistry; and will involve data processing and some statistical analysis.

* This project is appropriate for Medical Image Computing stream students

Distortion Correction of Echo-Planar Diffusion Images in MR using the Reverse Gradient Technique

Supervisors: Dr Maysam Jafar and Dr Maria Schmidt

Student: (Project available)

Aims:
The aim of this project is to implement the distortion correction algorithm in C++ so that it can be integrated into an image processing station linked to a clinical MR scanner.

Description:
In diffusion-weighted imaging (DWI) strong diffusion sensitization gradients often cause eddy-current related image distortion. This adds to the susceptibility induced distortions already seen on echo-planar (EPI) images due to the inherently low bandwidth per point in the phase-encoding direction. Geometric integrity is essential in clinical studies, where DWI is used in conjunction with T2-weighted (T2W) images for accurate diagnosis. The reverse gradient technique was originally proposed to correct for susceptibility induced artefacts in standard spin echo images. This technique requires the acquisition of two images of the same object under the same conditions except for the polarity of the frequency-encode line. This relies on the fact that if a second acquisition is performed under the same conditions except for the polarity of the frequency-encode gradient, then the spatial shifting of the signal in the second image will occur in the opposite direction.

Requirements:
A good background in C++ is essential. Experience using the ITK and boost libraries is highly desirable but not necessary. Experience in magnetic resonance is not required as sufficient training will be given.
Some travel to the Institute of Cancer Research (ICR) in Sutton/ Surrey will be required.

* This project is appropriate for Medical Image Computing stream students

Advancing knowledge of kidney function using Magnetic Resonance Imaging (MRI)

Supervisors: Dr David Thomas and Dr Isky Gordon

Student: Laxmi Muralidharan

Background:
MRI is non-invasive and does not use ionising radiation. It provides fine detail anatomical images of the internal organs. Recent advances in MRI have been directed towards functional assessment of the brain. Some of these techniques are now being evaluated to assess kidney function. Our unit has been undertaking research using MRI into kidney function for over 5 years.

Description:
Arterial spin labelling (ASL) is a non-invasive MR method for assessing the supply of blood (blood flow or perfusion) to an organ. ASL has been applied extensively to studies of the brain, but has so far had limited application in the body. Non-invasive repeatable measurements of renal perfusion could prove invaluable in early diagnosis and management of renal diseases. The aim of this project is to determine the robustness of ASL to measure renal perfusion in healthy kidneys, and to explore its potential use in the clinical environment. The technique for analysis of the perfusion MRI renal data has been developed recently and will now allow us to evaluate the potential of ASL in healthy volunteers before we apply it to patients with kidney disease.

From this project, the student will gain the following expertise:
• Detailed understanding of the structure and function of the kidney
• Working in a unit with a team of MRI experts (physicists, computer scientists, medical doctors and statisticians)
• Understanding of the physical and physiological basis of MRI
• Close supervision in the processing of the ASL data
• Ability to attend the regular seminars of the unit

In order to carry out this project, the student will be expected to:
• Use Matlab to generate quantitative perfusion maps from ASL data
• Analyse these maps to estimate the relative importance of acquisition parameters and post-processing steps (such as image realignment)
• Generate estimates of the accuracy and precision of ASL renal perfusion measurements, in order to assess the viability of clinical application of the technique.

All the work will be undertaken in the Imaging and Physics Unit at the UCL Institute of Child Health.

* This project is appropriate for Medical Image Computing stream students

Artefact rejection in x-ray CT imaging

Supervisors: Dr Adam Gibson and Dr Gary Royle

Student: Minal Modi and Laurence Moore

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 some mathematics and computer programming.

* This project is appropriate for Medical Image Computing stream students

Motion detection and analysis during seizure

Supervisors: Dr Adam Gibson and Dr Nick Everdell

Student:Jonathan Mayhew

It can be difficult to determine whether a seizure is epileptic in origin or not. It has been proposed that the appearance of movement during the seizure may help to distinguish between epileptic and non-epileptic seizures. In this project, you will build an accelerometer which can be placed on the arm during seizure and record arm movement. You will then analyse this arm movement to distinguish between different types of motion.

Modelling patient throughput in a radiotherapy department

Supervisors: Dr Adam Gibson and Dr Gary Royle

Student: (Project available)

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.

Diagnosing muscle disease with Electrical Impedance Spectroscopy

Supervisors:Prof. David Holder  and Hwan Koo.

Student: (Project available)

At present, electrophysiological diagnosis of different muscle diseases in undertaken with needle recording of the electrophysiological signals – this is termed “Electromyography” (EMG). The idea behind this project is to make recordings of electrical impedance to aid in this diagnosis using an Electrical Impedance Spectroscopy (EIS) system. This comprises a circular probe about 10 cm in diameter placed on the skin which makes multiple impedance measurements in a few seconds. The work will be to review relevant literature concerning muscle impedance studies, and test and calibrate the EIS system in the laboratory for this purpose. If successful, this will then be tested in a small number of patients and normal subjects with muscle disease in Prof. Holder’s clinic at UCH. 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, acquiring muscle impedance data. Skills to be acquired will include one or more of: biomedical instrument use and assessment, biophysical modelling. experimental design and data analysis. The project would be suitable for a single student or more than one working in a team, with a background in medical physics, engineering, or medicine.

Electrical Impedance Tomography (EIT) of evoked physiological activity

Supervisors:Prof. David Holder and Gustavo Santos.

Student: (Project available)

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. 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, preferably, team of 2 or 3, with a backgrounds in physics, engineering, computing, or medicine.

* This project is appropriate for Medical Image Computing stream students

Imaging in acute stroke with time difference Electrical Impedance Tomography : a simulation study.

Supervisors:Prof. David Holder and Dr. Ana Plata.

Student: (Project available)

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 changes in the brain during acute stroke over time as the brain pathology evolves. EIT has the unique potential to provide a bedside imaging method for this purpose which would alert medical staff to a deterioration and so lead to improvements in treatment. Unfortunately, imaging over hours may be obscured by fluctuations in baseline impedance due to movement and changes in electrode properties. The project will be to undertake a computer simulation study of the feasibility of imaging changes in stroke with real data from clinical studies of the fluctuations in baseline. The student will first be trained in background literature, and use of simulation and image reconstruction. They will then simulate the expected changes during a worsening stroke and then see if it is possible to produce acceptable images with the addition of realistic noise and boundary voltage drifts. If time permits, an investigation will be made into signal processing methods which could improvement of the signal-to-noise ratio and so image quality. Skill to be acquired : medical image reconstruction and computer simulation, data and statistical analysis and signal processing. The project would be suitable for a student with a background in physics, engineering, computing, or medicine.

* This project is appropriate for Medical Image Computing stream students

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 Jacek Zienkiewicz.

Student: (Project available)

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 prcoessing will be investigated, such as the use of a parallel Graphical Processing Unit added to the PC. Skills to be acquired:  Skills to be acquired will include : 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.

* This project is appropriate for Medical Image Computing stream students

Contrast and Signal-to-noise ratio in x-ray phase contrast imaging

Supervisors:Dr. Alessandro Olivo and Dr. Konstantin Ignatyev.

Student: Taken by UG student

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.
The classic way of classifying detail visibility in an x-ray image is based on the concepts of contrast and signal-to-noise ratio (SNR). These quantities are somewhat based on the typical characteristics of a conventional, absorption-based x-ray image, in which the part of the image occupied by the detail of interest presents a lower or higher intensity with respect to the background. The different nature of phase contrast images requires a critical revision of these classic quantities. The student will be provided with a number of images of the same samples taken with absorption and phase contrast methods. He/she will investigate ways of improve/update the definitions of contrast and SNR in order to make them suitable to this new imaging modality. The ultimate aim will be to carry out a quantitative comparison between image quality provided by conventional absorption and phase contrast methods.

The student will gain skills in data analysis, image analysis and familiarize with some of the basic concepts of medical imaging. Basic computing skills and some familiarity with statistical analysis are required.

* This project is appropriate for Medical Image Computing stream students

Implementation of de-blurring routines in Digital Tomosynthesis

Supervisors:Prof. Robert Speller and Dr. Caroline Reid.

Student: (Project available)

Conventional projection radiography is the most widely implemented method of x-ray screening methods, be it mammography, security screening of passenger luggage or quality control of industrial products. This method can be limited, firstly, through effective ‘stacking’ of objects in a projection image resulting in flattened images from which it is difficult to discriminate objects and, secondly, distortion of image information due to variations in x-ray absorption properties of imaged structures, for example, difficulty in distinguish between a thin sheet of strong absorber and a thick slab of weak absorber. These effects can lead to important information from the screening process being overlooked. It is proposed that this may be overcome through the use of digital x-ray tomosynthesis, an imaging technique currently attracting much attention in the medical imaging field. Tomosynthesis is a refinement of conventional geometric tomography methods where a finite number of projection images are acquired at varying orientations of the x-ray tube around the imaged object, from which a 3D image of the object is created. From these 3D images retrospective reconstruction is used to create focussed 2D slice images of an arbitrary number of planes through the object. It is proposed this method will be performed on objects moving on a conveyor system, leading to the name ‘On-belt Tomosynthesis’ (ObT). Typically in tomosynthesis, a set of slice images are generated from the summation of a set of shifted projection images acquired at different orientations of the tube. This is referred to as the Shift-and-add (SAA) reconstruction. This SAA reconstruction takes into consideration the fact that the projection of objects at different heights above the detector will be dependent on the relative heights of the objects above the imaging plane. While one benefit of the SAA image reconstruction method is the small computing power required to run the algorithms, the resulting images are heavily affected by blurring as they contain images from every plane of the imaged object; one plane in focus and all the others smeared on top. Code for the SAA image reconstruction method has been developed. This project aims to implement a number of de-blurring routines, implemented on top of the SAA image reconstruction method, have been developed to remove artefacts and improve the reconstructed image quality. Test procedures will then be developed to assess the relative image quality of the reconstructed images. This project would be suitable for a student with an interest in computer programming and will involve mathematics.

* This project is appropriate for Medical Image Computing stream students

Intelligent CT

Supervisors:Prof. Robert Speller and Dr. Peter Munro.

Student: Maria Rouchota and JENNIFER PATRICIA THOMPSON

X-ray and gamma ray imaging are still the most frequently carried out examinations despite the concern for radiation burden. Recently the UCL Radiation Physics Group developed a technique capable of reducing dose without a loss of image quality – the technique is called I-ImaS. This technique adjust the imaging conditions on-the-fly to suit each local region in the object being imaged. The technique was designed for planar imaging but now we wish to extend this to CT. A project to investigate the I-ImaS_CT concept. Last year a student demonstrated that the concept is viable for dose reduction but did not really study the steering algorithm that should be used to control exposure. Furthermore only one type of imaging task was studied. The project requires phantoms to be built, software to be written and many experiments carried out using the X-Tek CT system. This is an u/g merging into an MSc project.

* This project is appropriate for Medical Image Computing stream students

"RadiCal" - a new concept in detector development

Supervisors:Prof. Robert Speller and Dr. Konstantin Ignatyev.

Student: KONSTANTINA VIOLAKI

Many monitoring devices of radioactivity exist but none can identify the direction in which the radiation source exists without the use of a collimator. Recently a new device has been suggested to overcome this problem – the RadICal detector. The RadiCal concept could be tested both experimentally and by the use of modelling techniques. The route that will be taken will depend upon the student’s interests. Experimental work will be undertaken with existing equipment and modelling can be adapted from existing codes. The project will to test the feasibility of the concept and attempt to optimise the design for different applications.

X-ray performance characterization of a novel x-ray detector

Supervisors:Dr. Anastasios Konstantinidis and Prof. Robert Speller.

Student: Not Available

The aim of the project is to characterize the performance of a novel digital x-ray detector which consists of one of the largest area Complementary Metal-Oxide-Semiconductor (CMOS) sensors in the word and has been designed for a range of medical x-ray applications such as mammography, x-ray diffraction for breast biopsy, breast tomosynthesis, general radiography etc. From the characterization of the detector we can fully understand how it works and what we can do to improve it's performance. Practically, the evaluation is made in terms of the signal and noise transfer of the detector, namely the modulation transfer function (MTF) and noise power spectrum (NPS) respectively. The combination of the above parameters results in the detective quantum efficiency (DQE) which shows the signal-to-noise ratio (SNR) transfer of the detector. To calculate the above parameters, specific x-ray images need to be processed using an already developed MATLAB software.
Skills required: experimental measurements, MATLAB programming

A study of fluid distribution in absorbent materials using microCT

Supervisors: Prof Alan Cottenden and Dr Gary Royle.

Student: (Project available)

MicroCT is a variant on x-ray computer tomography in which three-dimensional images of small volume samples can be produced with high spatial resolution. This project will involve investigating the potential of microCT for measuring the distribution of water in absorbent materials of the kinds used in medical products such as incontinence pads and wound dressings. The hope is that the technology will provide new insights into material / fluid interactions which will ultimately lead to more effective products.

* This project is appropriate for Medical Image Computing stream students

How much spilled coffee can you mop from a carpet using a kitchen towel?

Supervisors: Prof Alan Cottenden and MIhaela Soric.

Student: Cristina Bogatu

Description: Theory says that partitioning of fluid between two porous absorbent materials in contact with each other depends on how their capillary pressures vary with their saturation (how wet they are): fluid flows until their capillary pressures are equal. This is what determines how much spilled coffee you can mop from a carpet with a kitchen towel and – more important, medically - the partitioning and distribution of liquid between the different layers in products such as incontinence pads and wound dressings, which is important in drawing and storing fluid away from the skin. This project will involve using a porosimeter to measure the capillary pressure as a function of saturation of a range of fabrics; using the porosimetry data for pairs of fabrics to predict how fluid will distribute between them; and running experiments – such as blotting water from carpet samples – to determine how well the theory predicts the reality. Interestingly, theory predicts that the equilibrium distribution of fluid between two fabrics depends on which one starts out the wettest. The project will be primarily experimental and will be based at the UCL Archway campus (by Archway tube station).

Predicting the absorption capacity of absorbent materials as a function of pressure

Supervisors: Prof Alan Cottenden and MIhaela Soric.

Student: Nick Zafeiropoulos

Description: Conventionally, the absorption capacity of the porous absorbent fabrics that are widely used in such medical products as incontinence pads and wound dressings is measured by pouring water into samples held under load until saturation is reached. This is laborious and yields results only for the specific pressures selected. The project will involve investigating a potentially better approach. In such fabrics, the fluid is held between the fibres and so absorption capacity is determined by the void volume fraction of the fabric. For a give fabric this, in turn, depends on how compressed the structure is which, in turn, depends on the applied pressure. The plan is to use Archimedes principle to determine the fibre volume fraction of some example materials, and compression tensometry to measure fabric thickness as a function of pressure. These data will be combined to predict the absorption capacity of the fabrics as a function of pressure, and the prediction checked experimentally against the conventional method for example pressures. The project will be primarily experimental and will be based at the UCL Archway campus (by Archway tube station).

Predicting the retention capacity of absorbent materials using porosimetry

Supervisors: Prof Alan Cottenden and MIhaela Soric.

Student: (Project available)

Description: One of the key factors determining the efficacy of the absorbent materials used in medical products such as wound dressing and incontinence pads is their ability to retain fluid against the pull of gravity. The most direct way of doing this is to measure the equilibrium saturation profile in an initially fully saturated vertical strip of fabric, once the excess fluid has drained under gravity. However, this is a laborious and time-consuming measurement to make. Theoretically, the same distribution could be generated by measuring the capillary pressure of the fabric as a function of saturation using retreating porosimetry. This project will involve validating theory against experiment for some example materials. The project will be primarily experimental and will be based at the UCL Archway campus (by Archway tube station).

Using infrared light to investigation the absorption properties of nonwoven felts used in medical applications

Supervisors: Prof Alan Cottenden and Prof Jem Hebden.

Student: (Project available)

Description: Absorbent nonwoven felts are used in a number of medical applications, notably incontinence pads and wound dressings. It is a major objective of the Continence and Skin Technology Group to establish a better understanding of how fluids interact with absorbent materials and build mathematical models which will enable the development of more effective medical products. This project will use an infrared device to investigate the absorption properties of nonwoven felts by mapping the distribution of fluid in them under a number of equilibrium (e.g. retention under gravity) and dynamic (e.g. horizontal and vertical wicking) experimental configurations. Data from the new device will be compared with both experimental data using other techniques and predictions based on existing mathematical models. The project will be primarily experimental and based at the UCL Archway campus (by Archway tube station).

A project to develop a quality assurance program for photogrammetric face scanners

Supervisors: Dr Cliff Ruff and Dr. Liu Ming.

Student: (Project available)

Within the UCLH NHS Trust we have recently taken delivery of two stereo photogrammetric surface scanners designed to capture face shape. It is normal procedure when taking delivery of new patient imaging systems to carry out a full acceptance check procedure to ensure that the device performs to specification, and to instigate an ongoing program of performance checking and calibration. In order to carry out these two functions we will need to design an appropriate series of tests.

We intend to construct a number of test objects in our workshop and the student will be tasked with using these objects to test both the hardware and software of the scanning systems and to develop and specify a set of tests to form an ongoing quality assurance program.

Pre-processing of CT images for cranioplasty planning

Supervisors: Dr Cliff Ruff and Dr. Liu Ming.

Student: (Project available)

The cranioplasty unit of the medical physics department in UCLH provides the NHS with over 150 custom built cranioplasty plates per year. A number of the patients who require plates to be made have existing repairs that need to be removed from the CT images before the new plate can be designed to custom fit the patients skull. The repairs of generally made of a denser material than the bone they replace.

* This project is appropriate for Medical Image Computing stream students

At present the pre-existing repairs are removed by hand, the aim of this project is to develop software that automatically removes the pre-existing repairs from the CT images, as such some programming experience, preferably in C++, is required.

Creation of a contact network for a Hospital for infection control purposes

Supervisors: Dr Paul Ganney

Student: (Project available)

Description: There has been much work done in recent years on using network models to simulate the transmission of infection. Some of this (Ancel Meyers, Ganney) has looked at mapping a particular setting, rather than using randomly generated networks. The aim of this project is to take the methods described in Ganney's PhD Thesis (2011, Hull) and apply them in two ways to the UCLH. The first is questionnaire-based, replicating the work undertaken by Ganney. The second is using UCLH incidence data to generate a contact network. The final part of the project will be a comparison of the two models and an attempt to explain any discrepancies that may exist.

Skills: Computing is necessary. A background knowledge of infection propagation would be useful, but is not essential.

Numerical Modelling of Ultrasound Propagation in Poroelastic Media using Pseudospectral and k-Space Methods

Supervisors: Dr Ben Cox and Mark Thompson (University of Oxford).

Student: (Project available)

A poroelastic medium is one that consists of a solid skeleton with fluid pores within it. Bone is a clear example, although many tissue types can be modelled as poroelastic on some scale. This project will use spectral methods (numerical methods based on the Fast Fourier Transform) to develop a numerical model of ultrasound propagation in such media. The starting point will be Biot two-phase model extending the technique to more complex multi-phase models if sufficient progress is made. Some experience with tensor partial differential equations, a good understanding of numerical methods, in particular the Fast Fourier Transform, and confident Matlab or C++ programming skills will be required.

* This project is appropriate for Medical Image Computing stream students

Numerical Modelling of the Detection of Photoacoustic Waves by an Atomic Force Microscope

Supervisors: Dr Ben Cox and Bart Hoogenboom (London Centre for Nanotechnology).

Student: (Project available)

Atomic force microscopes can be used to detect the displacements in the surface of a material as ultrasound waves reflect from it, but it is not yet known if the low acoustic amplitudes typically generated by the photoacoustic effect can be detected in this way. The aim of this project is to develop a numerical model to predict the signals that might be measured in such an arrangement, and determine under what conditions it may be achieved in practice. If this is successful, a second part of the project could look at how such measurements might best be used to form an image of the sample being studied. A good understanding of partial differential equations, numerical methods and confident Matlab or C++ programming skills are essential requirements.

* This project is appropriate for Medical Image Computing stream students

Numerical Modelling of Ultrasound Propagation Through Anisotropic Media

Supervisors: Dr Ben Cox and Bradley Treeby (Australian National University)

Student: SIDDHARTH MEHTA

It is often assumed that the speed of ultrasound propagation through biological tissue is isotropic (does not depend on direction). This is not quite true for some tissues (bone, cartilage) especially at high frequencies. This project will develop a model of acoustic propagation taking into account sound speed anisotropy, and explore the resulting effects on the wavefield. A good understanding of partial differential equations, Fourier transforms, numerical methods and confident Matlab or C++ programming skills are essential requirements.

* This project is appropriate for Medical Image Computing stream students

Developing a Nonlinear Image Reconstruction Algorithm for Contrast-Enhanced Photoacoustic Imaging

Supervisors: Dr Ben Cox and Paul Beard.

Student: SARAH ZAMAN

The acoustic waves in conventional photoacoustic imaging are assumed to propagate linearly as the acoustic pressures are so low. The image reconstruction algorithms are therefore all linear. As new contrast agents are developed with high values of optical absorption, the early part of the propagation may be nonlinear. This project will investigate the use of time reversal as a nonlinear reconstruction algorithm, and determine whether this is likely to enhance image quality in practice. A good understanding of partial differential equations, Fourier transforms, numerical methods and confident Matlab or C++ programming skills are essential requirements.

* This project is appropriate for Medical Image Computing stream students

Investigation into Staircasing Errors in Numerical Ultrasound Models

Supervisors: Dr Ben Cox and Peter Munro (UCL/University of Western Australia).

Student: (Project available)

Most numerical models of broadband ultrasound propagation use a regularly spaced mesh of points at which to calculate the field. Because the material properties are also defined at these points, curved interfaces between two materials (between soft tissue and bone, for example) are represented by a ‘staircase’ of square-edged steps. This can result in errors in the calculated field. One solution is to use an irregular mesh, but for some techniques (such as k-space methods) the regular mesh is integral to the technique. The aim of this project is to investigate the degree of error this introduces into the solution by comparing simulations using a pseudo-spectral k-space acoustic model with analytical results in various cases. A good understanding of partial differential equations, Fourier transforms, numerical methods and confident Matlab or C++ programming skills are essential requirements.

* This project is appropriate for Medical Image Computing stream students

Modelling of Reverberant Cavities and Impedance Boundary Conditions within the Pseudospectral Framework

Supervisors: Dr Ben Cox and Bradley Treeby (Australian National University).

Student: (Project available)

Pseudospectral and k-space models can accurately model how ultrasonic waves are reflected and transmitted at boundaries between different tissue types as long as the differences in sound speed and density are small (a few percent). When the difference is large, for example between soft tissue and bone, the errors can be significant. This project will investigate whether it is possible to directly impose a numerical ‘impedance boundary condition’ at an arbitrarily defined surface in models of this type, what sort of errors arise, how severe they are, and whether they can be overcome. If successful, this will be extended to modelling reverberant cavities using this approach. A good understanding of partial differential equations, Fourier transforms, numerical methods and confident Matlab or C++ programming skills are essential requirements.

* This project is appropriate for Medical Image Computing stream students

External Controller for Dog Bladder Fullness Monitor (DBFM)

Supervisors: Tim Perkins and Prof Nick Donaldson.

Student: BEIQIONG WANG

Like humans, pet dogs also occasionally sustain Spinal Cord Injury (SCI) due to accident or disease. The resulting paralysis often results in bladder incontinence. Currently we have a number of these paralysed and incontinent dogs with bladder emptying implants, in the same style as we have used for humans for the last 30 years. However, it would be a great help to the owners of such dogs if there was an indication of the dogs’ bladder fullness, so the owners know when to take their pet out and activate the bladder emptying device. The implanted part of the DBFM is mainly a multiplexed 16 channel electro-neurogram (ENG) amplifier. The external controller, which communicates with the implant through intact skin via inductive loops, needs to be designed. It will involve:
1. A microcomputer with a display, keyboard, alarm sounder and a USB port by which it may be programmed by a PC.
2. Programme to first provide channel selection commands to the implanted amplifier. Two out of the sixteen channels must be selected.
3. On receiving response from the implant, the controller must accept serial implant data at some 400k bits per second (kbs) and store this away for further processing and or output it in analogue form to an oscilloscope. The data rate corresponds to the 2 selected channels sampled at 15kHz at 12 bits per channel, with allowance for start, stop and parity bits.
4. The controller programme must also provide for a display of channels used, neural spike rate, the bladder fullness that spike rate corresponds to and for an alarm to be sounded when the bladder is too full. It must further respond to the keyboard for channel selection etc.
5. The controller programme should only occupy a small part of processor time to allow for real time neural spike analysis to be programmed when the required data processing is known.
Initially the controller would provide the signals necessary to determine what data processing is needed. The skills needed for the project include digital and analogue circuit design and microcomputer programming.

MSc Project in Imaging Microstructure of the Brain: PROJECT 1

Supervisors: Dr Ivana Drobnjak and Prof Daniel Alexander.

Student: Tejas Pendse

Measuring microstructure parameters of brain tissue, such as axon radius, in vivo is a challenge in diffusion MRI. Recent studies showed [Xu MRM09, Kiruluta ISMRM08] that using pulse sequences with oscillating or chirped diffusion-gradient pulses may provide better sensitivity to microstructure features than the typically used pulsed-gradient spin-echo (PGSE) [Alexander MRM08] or stimulated-echo (STEAM) [Avram JMR04] sequences, both of which use a rectangular diffusion-gradient pulse.

However, diffusion-gradients are usually very strong (can go as high as is |G|=0.08T/m on clinical scanners) and when allowed to oscillate on high frequencies they could potentially create significant gradient-related artifacts such as eddy-currents, gradient-nonlinearities or gradient-inhomogeneities. In order to achieve reliable and robust diffusion–weighted images it is necessary to investigate the extent to which these gradient-related artefacts can affect MR images.

To this end, 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 extend the current POSSUM software to make simulations of gradient-related artefacts, and then apply it in order to look into the potential effect these artefacts can have on MR images. The project would involve finding fast, accurate and efficient numerical/analytical solutions to a set of ODE’s and then implementing those in C++ language into the already existing software environment. The project would also involve learning about Magnetic Resonance Imaging.

* This project is appropriate for Medical Image Computing stream students

MSc Project in Imaging Microstructure of the Brain: PROJECT 2

Supervisors: Dr Ivana Drobnjak and Prof Daniel Alexander.

Student: Gastao Cruz

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.

Prostate cancer histology analysis

Supervisors: Prof Daniel Alexander.

Student: (Project available)

This project is part of a larger effort to devise a new imaging technique for diagnosing and grading prostate cancer.  Currently patients have to undergo a transrectal biopsy (not very nice!) to allow clinicians to grade cancer tumours and thus decide on the best treatment.  An effective non-invasive imaging technique that can discriminate grades of tumour reliably would make a major difference by avoiding such biopsy. However, current imaging techniques, such as MRI, do not provide sufficient discrimination to replace the invasive procedure.  We are currently constructing models of the cellular architecture of tumours of different grades by digitizing biopsy slides and analyzing structural differences.  Such models will inform new MRI techniques with greater sensitivity to differences between tumours.  This project will work with pathologists to build up a database of biopsy slides, implement algorithms to learn differences between different grades and start to construct the models required for the next steps. Skills: computational.

* This project is appropriate for Medical Image Computing stream students

MRI of prion diseases

Supervisors: Prof Daniel Alexander.

Student: Seyedeh A Setoodegan

Prion diseases, like Creutzfeld-Jacob disease (mad-cow disease), are devastating neurodegenerative conditions with no known treatment.  Patients show hallmark anomalous signal changes in diffusion weighted MRI, but the pathology that gives rise to these changes remains unclear.  We have access to uniquely rich MRI data sets from several patients that allow us to test hypotheses about the source of the signal changes.  The project will involve implementing various candidate models that potentially explain where the signal changes arise from and comparing them computationally to see which explain the data the best.  This will give insight into the mechanisms of disease and help clinicians make accurate prognoses about how the disease will progress.  As disease-modifying treatments start to become available, early diagnosis enhanced by new imaging techniques based on these models will be a critical component in disease treatment and management. Skills: computational.

* This project is appropriate for Medical Image Computing stream students

Progression of Alzheimer's disease

Supervisors: Prof Daniel Alexander.

Student: (Project available)

This project extends some recent ideas on modelling the progression of Alzheimer's disease.  The timecourse of the disease is very complex with a variety of events, pathologies, symptoms and changes in patient state.  In recent years, the international neurology community has pulled together to acquire imaging data from very large cohorts of patients in order to understand better what the sequence of events is and how it varies over the population.  This project looks at some of that data using new computational models to see what new features of the disease we can reveal through a novel computational approach. Skills: computational

* This project is appropriate for Medical Image Computing stream students

Learning to recognize multiple sclerosis

Supervisors: Prof Daniel Alexander.

Student: (Project available)

This project uses machine learning algorithms to try to classify multiple sclerosis (MS) patients very early in the disease.  At first presentation, it is very difficult to determine whether a patient showing MS symptoms will completely recover or is at the start of a trajectory towards physical decline and disability.  We have unique access to historical data sets combining measurements from first presentation with subsequent clinical history.  This offers the possibility to learn the relationship between measurements at first presentation and subsequent progression.  This project will take the first steps to determine how well we can predict patient outcome from first presentation using state of the art machine learning techniques. Skills: computational

* This project is appropriate for Medical Image Computing stream students

Investigating the potential of resting state fMRI to predict brain injury after neonatal hypoxia-ischaemia

Supervisors: Dr David Thomas and Dr Alan Bainbridge.

Student: Reem Ahmad

Background:
Despite many recent advances in medical technology and health care, significant risks are still associated with the birth process. For the baby, a traumatic birth can result in insufficient oxygen reaching the brain for a brief period (known as neonatal hypoxia-ischemia (HI)), and this can have severe and lasting effects on brain development. At UCLH, there is a team of researchers dedicated to understanding the mechanisms underlying the pathological processes associated with HI, with the aim of using this knowledge to develop new therapies and reduce the subsequent damage. This project will involve working with that team to investigate the potential of a new brain imaging method, which may reveal novel information about the state of the brain in the hours following a hypoxic-ischaemic episode.
Description:
Functional MRI (fMRI) is a widely used non-invasive imaging method used for studying neuronal activations related to the presentation of cognitive stimuli (e.g. visual or auditory events). More recently, fMRI acquisitions of the brain have been performed to study the natural fluctuations of activity in the so-called ‘resting state’. The functions of the resting state networks are still unclear, but it is likely that they emerge in parallel with the development of related cognitive functions. This project will involve the acquisition of resting state fMRI data on a well-established piglet model of birth hypoxia using a 9.4T MR scanner, and analysing the data to look for changes in the resting state networks related to brain injury.
This project will involve some experimental work (helping with the data acquisition) but will mainly involve working on appropriate ways to analyse and interpret the resting state fMRI data. A reasonable level of mathematics and computational skills will be required.

* This project is appropriate for Medical Image Computing stream students

Photoacoustic Image Reconstruction Using L-Shaped Detector Arrays

Supervisors: Dr Ben Cox Prof Paul Beard.

Student: (Project available)

Planar detection arrays have been used successfully for photoacoustic imaging. However, as the sensitivity of the devices and the signal-to-noise ratio improves it becomes clear that image artefacts appear due to the limited size of the detectors. One way to combat this is to use two perpendicular planar detectors in a V or L formation. Kunyansky has recently proposed reconstruction algorithms applicable to cases such as this. The project will have three objectives: to code up one of Kunyansky’s algorithms in 3D using Matlab, to test it with data simulated using k-Wave and to compare the images obtained with those from a single planar detector, and to compare the results with another image reconstrucion algorithm based on time-reversal. A good understanding of partial differential equations, Fourier transforms, numerical methods and confident programming skills are essential requirements.

* This project is appropriate for Medical Image Computing stream students

Can Cortical Thickness Measures Provide a Useful Diagnosis Tool For Dementias?

Supervisors: Dr. Matt Clarkson Dr. Sebastien Ourselin.

Student: (Project available)

Cortical thickness measurement has been proposed as a bio-marker of disease progression, and has been used to study the effects of Alzheimer's disease, semantic dementia, posterior cortical atrophy and other dementias. Typically, a disease specific pattern of thinning has been observed over the surface of the brain. However, to-date methods have focussed on cross-sectional or longitudinal studies using a large number of subjects, where typically patients are compared as a group to healthy controls. Consequently these methods take a long time (days) to run.
The Centre for Medical Image Computing has developed methods for atlas building, image registration, segmentation, cortical thickness measurement and visualisation. This project will investigate whether thickness measurements performed on a single subject, and compared with a known average for healthy controls can be a useful diagnostic tool. The aim is to assemble a pipeline of tools to measure cortical thickness that can be done in a computationally feasible time, and test whether the resultant map of thickness gives any benefit to a neurologist. If beneficial, the pipeline may be optimised for speed, and potentially integrated within CMIC's NiftyView platform.
This project will require significant computing expertise such as C++ programming, familiarity with Unix/Linux, the ability to write shell scripts (bash/csh) and an understanding of registration and segmentation (see course MPHYGB06 - Information Processing In Medical Imaging).

* This project is appropriate for Medical Image Computing stream students

3D Ultrasound Imaging of the Prostate for Guiding Biopsy and Minimally-invasive Cancer Interventions

Supervisors: Dr. Dean Barratt and Yipeng Hu

Student: David Chen

Prostate cancer is now the most common cancer in men in the UK, North America, and many parts of Europe. Ultrasound imaging is used routinely in hospitals for guiding the placement of transrectal needles during prostate biopsy, a procedure in which prostate tissue samples are collected for subsequent histological analysis for the presence of cancer. Ultrasound is also used widely for guiding minimally-invasive cancer treatments, such a brachytherapy, where small radioactive seeds are implanted into the prostate. In practice, however, accurately guiding needles or seeds to the desired location is challenging using conventional ultrasound scanners, which only provide two-dimensional, cross-sectional views of the prostate. 3D ultrasound imaging offers a potential solution by allowing an image of the entire prostate (and surrounding structures) to be produced. This is useful because it allows needles, seeds, and other therapy delivery instruments to be located in three dimensions. 3D images are also required in order to accurately register (i.e. align) ultrasound images with MR images, which enable tumours to be identified much more reliably than ultrasound images. The aim of this project is to develop an intraoperative method for acquiring and visualising 3D images during minimally-invasive surgical procedures, and to validate its accuracy using physical prostate models ("phantoms") that can be imaged using ultrasound and MRI.

This project will involve software development (in Matlab and/or C/C++) and simple hardware interfacing. The project will be carried out using equipment available in a state-of-the-art image-guided interventions laboratory.

*This project is appropriate for Medical Image Computing stream students

Registration of MR and CT Images for Image-guided Radiotherapy Treatment of Prostate Cancer

Supervisors: Dr. Dean Barratt and Yipeng Hu

Student: Elaine Damato

Prostate cancer is now the most common cancer in men in the UK, North America, and many parts of Europe. External beam radiotherapy is a standard treatment for prostate cancer that involves the delivery of a series of radiation doses (called fractions) to the prostate gland. Conventionally, the entire prostate is treated, but recently there has been significant clinical interest in new therapy approaches in which the radiation dose is concentrated on a single (dominant) tumour to reduce the risk of side-effects through "collateral damage" to nearby structures, such as the rectum, nerves, and bladder. Implementing this approach relies on the ability to precisely locate the tumour at the treatment planning stage and within the radiotherapy system at the start of each fraction. This is complicated by the fact that prostate tumours are generally not visible in CT scans used for treatment planning and dosimetry, or in on-board x-ray images used for patient positioning during therapy. For this reason, there is a need for methods for registering (i.e. aligning) information on tumour location from MR images with CT and x-ray images to enable tumour targeting both at the planning stage and during therapy. In this project, the accuracy of a marker-based technique for MR-CT image registration will be evaluated, which involves aligning markers implanted into the prostate. Special attention will be paid to assessing the affects of prostate deformation between MR and CT images, and devising methods for overcoming this.

This project will involve image analysis and software development in Matlab.

*This project is appropriate for Medical Image Computing stream students

Accuracy Requirements for Prostate Needle Biopsy using Computer Modelling

Supervisors: Dr. Dean Barratt and Yipeng Hu

Student: This project is no longer available

Prostate cancer is now the most common cancer in men in the UK, North America, and many parts of Europe. Prostate biopsy is a very common diagnostic procedure in which a number (typically 6-20) of small tissue samples are collected use a biopsy needle inserted through the rectal wall. Recently, 3D image guidance techniques have been developed that potentially allow samples to be obtained at precise locations, but the required accuracy for such systems and the impact of localisation errors on the grading of prostate cancer (measure by a so-called Gleason grade, which is closely correlated to the cancer aggressiveness) are both poorly characterised. The aim of this project will be to investigate these issues using computer modelling and 3D reconstructions of whole-mount histological specimens.

The project will require programming in Matlab and involve close collaboration with surgeons at UCLH.

*This project is appropriate for Medical Image Computing stream students

Vessel-based Image Registration for Guiding Surgical Interventions

Supervisors: Dr. Dean Barratt and Yipeng Hu

Student: Ajith Kuruvita

Blood vessels – arteries and veins – are often clearly identifiable in images and many imaging techniques are now available to specifically image vascular structures. The ubiquity of blood vessels in medical images makes them very useful for image registration (i.e. image alignment) as vessels often clearly visible in different images of the same organ. However, soft-tissue deformation and variations in the grey-level brightness of blood vessels within and between such images make vessel-based registration a challenging problem in the general case, especially for realtime applications, such as surgical guidance.

This project will focus on developing algorithms for rapid, automatic registration of blood vessels with a particular emphasis on applications in image-guided brain and liver surgery. The initial aim will be to investigate the theory and application of scale-space approaches for enhancing and segmenting blood vessels. Then, a algorithm for registering blood vessels (and other tubular structures) will be developed, building on previous work carried out by CMIC researchers that extends a new method called Coherent Point Drift (CPD).

This project will involve image processing in Matlab and be suitable for a student who is comfortable with mathematics.

*This project is appropriate for Medical Image Computing stream students

Investigating differences in the cortical folding pattern of Alzheimer's disease and healthy controls

Supervisors: Dr. Andrew Melbourne and Dr. Sebastien Ourselin

Magnetic resonance imaging (MRI) is a fundamental part of the clinical assessment of patients with suspected Alzheimer's disease. Cohort studies of changes to the structure of the brain as observed on MRI provide predictive markers of disease progression, influencing diagnosis and treatment. One such marker is the cortical surface pattern which is known to lose its characteristic convolutions with increasing disease severity and tissue atrophy.

This project will refine and extend segmentation software developed at the UCL Centre for Medical Image Computing applied to a large cohort of healthy controls, cognitively impaired and Alzheimer's patients. Subsequent quantitative analysis will reveal how useful the cortical folding pattern is as a predictive biomarker of functional decline and the results are likely to be suitable for publication in an academic journal.

This project requires significant computing ability and a sound mathematical background.

*This project is appropriate for Medical Image Computing stream students

Accounting for sliding motion in deformable image registration

Supervisors: Jamie McClelland and Marc Modat.

Student: (Project available)

Accounting for sliding motion in deformable image registration
Sliding motion can sometimes occur between adjacent parts of the internal anatomy, e.g. between the lungs and the ribs during respiration. Most deformable registration algorithms constrain the deformation to be smooth and continuous and so do not permit sliding motion to occur. In the past few years there have been a number of algorithms proposed that can incorporate sliding motion into deformable registrations. In this project the student will implement one or more of these approaches (most likely as part of the NiftyReg registration software developed in CMIC), and will assess the sliding motion registrations on lung CT data. There will be the opportunity to conduct some original research as part of this project which may result in a conference or journal publication.
This project will require very strong computing and programming skills and a good understanding of the registration algorithms and the underlying mathematics.

*This project is appropriate for Medical Image Computing stream students

4D deformable image registration

Supervisors: Jamie McClelland and Marc Modat.

Student: (Project available)

There are a number of medical imaging applications which require several 3D volumes acquired at different points in time to be registered to a common reference volume in order to assess the motion and deformation that occurs over time. One such application is to register 4DCT volumes acquired at different points in the respiratory cycle to aid with Radiotherapy planning. The standard approach to this is to perform several separate 3D registration, but there have been a number of recent proposals to perform 4D registrations which attempt to register all of the volumes simultaneously and can produce better results due to the temporal constraints that can be placed on the registrations. This project will involve implementing one or more of the methods for performing 4D registrations (most likely as part of the NiftyReg registration software developed in CMIC), and assessing the methods on 4DCT scans of lung cancer patients and other data. There will be the opportunity to conduct some original research as part of this project which may result in a conference or journal publication.
This project will require very strong computing and programming skills and a good understanding of the registration algorithms and the underlying mathematics.

*This project is appropriate for Medical Image Computing stream students

Statistical analysis of 3D skin surface motion due to respiration

Supervisors: Jamie McClelland and David Hawkes.

Student: IMAN AKBARI

It is well known that there can be substantial variation in the ways that individuals breath. This includes deep breathing, shallow breathing, breathing predominantly with the chest, breathing predominantly with diaphragm, and others. The different types of breathing can affect the way in which internal structures move due to breathing, which can be very important Radiotherapy planning and delivery and many other applications where respiratory motion is an issue (e.g. image acquisition). This project will utilise statistical methods such as PCA, as well as more advanced manifold learning methods, to analyse 3D skin surface data of individuals breathing in different ways and to try and parameterise the different ways of breathing. There will be the opportunity to conduct original research as part of this project which may result in a conference or journal publication.
This project will require good computing and mathematical skills and a good understanding of or willingness to learn about statistical methods including manifold learning.

*This project is appropriate for Medical Image Computing stream students

Monitoring the effects of High Intensity Focused Ultrasound (HIFU) Therapy with acousto-optics

Supervisors: Dr. Terence Leung and Dr. Dean Barratt

Student: Lebina Shrestha

High Intensity Focused Ultrasound (HIFU) is a relatively new non-invasive technique which exploits focused ultrasound to heat up and destroy tissue. HIFU is not currently approved for widespread clinical use, but is being evaluated as a treatment for prostate cancer at UCLH and is used in other centres as a treatment for liver and kidney tumours. Current methods for monitoring HIFU-induced heating include ultrasound and MR thermometry, but both of these methods are limited in their accuracy and in the information they provide about true tissue damage (cell death). We are currently developing a technique known as Acousto-Optics to monitor tissue changes within the treatment zone by detecting changes in colour and texture. In this technique, near infrared light is used to illuminate the tissue and surrounding tissues. Light that passes through the treatment zone where HIFU is targeted is modulated because HIFU introduces particle displacements and changes in optical refractive index. By measuring the diffused modulated light, information about the colour and texture of the tumour, which will change during the course of the treatment, can be derived.
This project will involve:
(1) becoming familiar with the operation of a state-of-the-art programmable ultrasound scanner;
(2) implementing methods for quantifying tissue changes using ultrasound and Acousto-Optic techniques; and
(3) performing measurements and experiments on biological samples.

This project will require skills in scientific measurement and basic computer programming using Matlab and/or C/C++.

*This project is appropriate for Medical Image Computing stream students

Kinect-Based Breast Volume Estimation

Supervisor: Dr. Dan Stoyanov

Student: Annamrie Atiba

Accurately estimating breast volume is important for determining correct implant size in reconstructive breast surgery. Traditional techniques for measuring breast volume are prohibitively expensive and time consuming and hence implant size is usually not judged metrically. With the emergence of low-cost sensors like Microsoft’s Kinect there is an opportunity to estimate the breast surface shape, model the breast geometry and find the correct breast volume easily within the clinical setting. The project will involve programming computer vision and computational techniques in C++ to analyse images obtained from the Kinect device. Background in programming is required and some knowledge of 3D graphics programming and geometry principles would be beneficial.

*This project is appropriate for Medical Image Computing stream students

Surgical Instrument Tracking for Robotic Surgery

Supervisor: Dr. Dan Stoyanov

Student: (Project available)

Tracking the surgical instruments in keyhole surgery and robotic assisted surgery is important for understanding surgical technique and metrically measuring skill levels and learning curves. Computer vision techniques potentially offer a software solution for tool tracking based only on the analysis of video acquired form the surgical endoscope camera. By combining low-level image processing and vision operators with geometric instrument models and statistical models of instrument behaviour it is potentially possible to build a fully automated system for monitoring the 2D/3D position of the tools. The information this system will provide has implications in surgical safety, skill understanding and training methodologies. This project will require programming in Matlab/C++ and would benefit from knowledge of image processing and/or techniques such as Hidden Markov Modelling (HMM).

*This project is appropriate for Medical Image Computing stream students

Kinect-Based Hand Tracking for Surgical Skill Assessment

Supervisor: Dr. Dan Stoyanov

Student: SHUANG MA

The assessment of surgical skills and actions is an important element of surgical training for advanced procedures using keyhole surgery. Various systems for tracking proximal and distal ends of the laparoscopic tools have been proposed but they are all expensive, intrusive and do not provide models of the surgeon’s hands and their motion. Inexpensive scanners such as Microsoft’s Kinect have enable the recognition and model fitting of whole body and limb models onto range sensor data. This can potentially be used for monitoring a model of the surgeon’s hand and fingers during instrument manipulation. The focus of the project will be to use the Kinect device to obtain the position and orientation of hands in the surgical theatre for monitoring the difference between experienced and novice surgical gestures. This project will require programming in C++ and would benefit from experience in image processing or computer vision.

*This project is appropriate for Medical Image Computing stream students

Adaptive Meshing for Diffuse Optical Tomography

Supervisor: Professor Simon Arridge

Student: (Project available)

DOT in the brain involves modelling light propogation in detailed head model using Finite Element Methods, which is computationally intensive. One important aspect is to adapt the mesh of the head so that computational effort is not wasted in parts where the signal is negligably small. It is relatively easy to refine a mesh where error is high, but more difficult to coarsen the mesh where the error is small. In addition, the mesh should be adapted differently dependent on the source distribution. In principle a parallel computing strategy can be developed where different meshes are used in parallel for different aspects of the forward and inverse problems.

This project will suit someone interested in scientific computing, visualisation and parallel computing
*This project is appropriate for Medical Image Computing stream students

Optical Tomography with Compressive Sampling

Supervisor:Professor Simon Arridge

Student: (Project available)

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*This project is appropriate for Medical Image Computing stream students

Ultra-Weak Variational Methods for Ultrasound Modelling

Supervisor: Professor Simon Arridge

Student: (Project available)

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*This project is appropriate for Medical Image Computing stream students

Time Series Analysis Methods in Optical Topography

Supervisor: Professor Simon Arridge

Student: (Project available)

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*This project is appropriate for Medical Image Computing stream students

Segmentation of the colon in CT colonography

Supervisors: Dr. Jamie McClelland, Holger Roth, Tom Hampshire, and Prof. David Hawkes

Student: (Project available)

Virtual colonoscopy (VC) is becoming a more prevalent method for screening colorectal cancer using images from Computed Tomography (CT). It allows imaging the patient's entire colon non-invasively in contrast to traditional endoscopic colonoscopy. Inflating the colon during the scans enables straight-forward automated labelling (segmentation) of the air inside the colon. However, in cases where the colon is insufficiently insufflated (collapsed) or fluid remains are present, extracting an accurate segmentation of the colon becomes more challenging. Here, labelling of just the remaining air inside the colon will result in disconnected colon segments.
A potential method to overcome this limitation would be the segmentation of the colon wall which would allow to join disconnected segments together. This is especially important for subsequent algorithms such as automated registration of CT scans and for visualisation of the colon surface.
This project will allow you to work as a team member of the virtual colonoscopy group at the Centre for Medical Image Computing (lead by Prof. David Hawkes). In order to tackle this problem, the creation of a software solution based on the state-of-the-art technology will be necessary. The requirements are good knowledge of C++ and/or MatLab. A good understanding of mathematics and a motivation to learn about level set methods will be helpful.
*This project is appropriate for Medical Image Computing stream students

*This project is appropriate for Medical Image Computing stream students

SIMEX for MR imaging of brain microstructure

Supervisors: Dr. Gary Hui Zhang

Student: (Project available)


This project aims to develop a technique towards clinical MR imaging of brain microstructure. It is within the broader aim of the Microstructure Imaging Group at UCL to develop non-invasive imaging techniques for assisting disease diagnosis. The particular MR imaging modality that we use is known as the diffusion MRI. It acquires images, known as the diffusion-weighted images, that encode information about tissue microstructure. This is often precisely the information that doctors seek to diagnose a disease but require a biopsy to collect. However, biopsy procedures are painful and, more important, can often be prohibitive due to their significant risk to a subject. Accessing this information non-invasively opens the door for diagnosing diseases early before they cause irreversible damages to the patients.

However, there is a particular challenge for clinical applications of this technique: noise. MRI images are generally contaminated with noise and this is especially true for diffusion-weighted images. The presence of noise renders the information derived from MRI less accurate, potentially introducing systematic errors, known as the bias. Being able to quantify and remove this bias is critical for generating accurate information for follow-on analysis. The aim of this project is to develop such a technique for reducing noise-induced bias in tissue microstructure estimates. Specifically, we will investigate a modern statistical method known as SIMEX: the SIMulation and Extrapolation method, which provides an estimation of the bias by adding synthetic noise to the measured data. The project will evaluate the feasibility of SIMEX as a viable approach to derive more accurate tissue microstructure estimates from diffusion-weighted images.

*This project is appropriate for Medical Image Computing stream students

Super-resolution for diffusion MRI

Supervisors: Dr. Gary Hui Zhang

Student:: Alexandra Tobisch


This project aims to develop a super-resolution technique for diffusion MRI. Diffusion MRI is a magnetic resonance imaging technique at the very foundation of recent advancement in imaging microstructure of tissues non-invasively. The technique enables us to understand the structural basis of normal and abnormal brain functions in living human beings. However, one limitation is that the images acquired with this technique are limited in its spatial resolution, typically of 2x2x2 mm3 in voxel size. Compared to the typical T1-weighted images, which have voxels of size 1x1x1 mm3, the resolution is 8 times lower, preventing the imaging of fine brain structures. Improving spatial resolution is essential for accessing such finer detail. The challenge is that doing so by directly acquiring higher resolution data is currently infeasible, due to the excessively long scan time required. The alternative is super-resolution, which refers to a family of techniques that reconstruct a high resolution image from a small set of lower resolution ones. Because the set of lower resolution images can be acquired with modest increase in the scan time, super-resolution makes an attractive avenue to explore. The aim of this project is to develop such a technique that is applicable specifically to diffusion MRI.

*This project is appropriate for Medical Image Computing stream students.