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  1.   1.  Current Projects
    1.   1.1  EPRSC. “Learning MRI and histology image mappings for cancer diagnosis and prognosis”. 15/12/2017 – 14/12/2020.
    2.   1.2  EPRSC Fellowship. “Non-invasive MRI biomarkers for Oncology”. 1/07/2016 – 30/06/2021.
    3.   1.3  EPRSC. “A biophysical simulation framework for magnetic resonance microstructure imaging”. 1/04/2016 – 31/03/2019.
    4.   1.4  EPSRC Doctoral Prize Fellowship. “Quantitative diffusion MRI for prostate cancer grading”. 1/01/2016 – 01/01/2018.
    5.   1.5  Prostate Cancer UK. “CombIning advaNces in imagiNg with biOmarkers for improVed diagnosis of Aggressive prosTate cancEr - INNOVATE”. 1/01/2016 - 31/12/2017.
    6.   1.6  Microsoft Research PhD Studentship. “Image Quality Transfer”. 1/10/2015 – 28/09/2019.
    7.   1.7  NIH. “Structure and Function of the Placenta from Implantation to Delivery. A Next Generation MRI Approach”. 17/09/2015 - 31/08/2018.
    8.   1.8  EU Horizon 2020. “A Clinical Decision Support system based on Quantitative multimodal brain MRI for personalized treatment in neurological and psychiatric disorders (CDS-QUAMRI)”. 1/09/2015 – 01/09/2020.
    9.   1.9  Magnetic Resonance Imaging in Multiple Sclerosis (MAGNIMS). “To explore imaging signature of multiple sclerosis phenotypes”. 1/07/2015 – 01/07/2016.
    10.   1.10  EPRSC. “National Facility for In Vivo MR Imaging of Human Tissue Microstructure”. 1/06/2015 – 31/05/2020.
    11.   1.11  EPSRC. “Anatomy driven brain connectivity mapping”. 1/06/14 – 31/05/17.
    12.   1.12  MRC. “In vivo microstructural neuroimaging in infants at risk of developing neurocognitive delay or neurobehavioural disorders”. 1/04/14 – 31/03/19.
    13.   1.13  UCL CS Research Excellence Studentship. "Automatic analysis of histology with machine learning". 10/03/2014 — 01/09/2017.
    14.   1.14  UCL SLMS Grand Challenge Studentships project 2013. “Parcellation of the human cortex using quantitative MRI”. 1/10/2013—30/09/2016.
    15.   1.15  EPSRC Breast cancer project. “Medical imaging markers of cancer initiation, progression and therapeutic response in the breast based on tissue microstructure”. 1/01/13 – 31/12/15.
    16.   1.16  UCL SLMS Grand Challenge Studentship project. "Development and validation of machine learning techniques to facilitate diagnosis and predict prognosis in patients with Multiple Sclerosis". 16/11/12-15/11/15
  2.   2.  Completed Projects
    1.   2.1  CMIC Platform Grant (pump-priming Award). “Enabling multi-site high precision spinal cord MRI”. 01/06/2017 – 01/06/2018.
    2.   2.2  UCL SLMS Grand Challenge Studentships project 2012. “Axonal density as MR imaging biomarker: from bench to bedside”. 1/10/2012—30/09/2015.
    3.   2.3  EPSRC Brain connectivity project. “Robust graph analysis of brain connectivity”. 1/06/2012—31/05/2015.
    4.   2.4  Leverhulme Trust Fellowship. "New imaging techniques to reveal the relationship between brain tissue microstructure and brain function". 1/05/2012-31/12/2015.
    5.   2.5  EPSRC Healthcare Partnerships Project. "Axon and myelin damage assessed using advanced diffusion imaging: from mathematical models to clinical applications". 1/09/2011-31/08/2014.
    6.   2.6  EPSRC Program grant. “Intelligent Imaging: Motion, Form and Function Across Scale”. 1/06/2010—31/05/2015.
    7.   2.7  EPSRC Leadership Fellowship. "Direct measurements of microstructure from MRI." EP/E007748. 1/10/2008—30/09/2014.
    8.   2.8  European Commission (Future and Emerging Technologies program). “CONNECT: Consortium of NeuroImagers for the Non-invasive Exploration of Brain Connectivity and Tractography”. 1/01/2010—31/10/2012.
    9.   2.9  International Spinal Research Trust (ISRT). "Spinal Cord Diffusion Imaging: Challenging Characterization and Prognostic Value'' Natalie Rose-Barr Studentship. 1/10/2008--30/9/2011.
    10.   2.10  EPSRC. "Monte Carlo random effects modelling in diffusion MRI: a new window on microstructure and white matter architecture.'' EP/G025452/01. 1/10/2008--30/9/2011.
    11.   2.11  EPSRC CASE Studentship with GSK. 28/9/2008--27/9/2011.
    12.   2.12  EPSRC Doctoral Prize (PhD+). “Differentiating grades of brain tumour with DW-MRI”. 1/01/2011-1/01/2012.
    13.   2.13  EPSRC. "Monte-Carlo simulation framework for diffusion MRI.'' EP/E056938/1. 1/9/07--31/8/10.
    14.   2.14  NHS, Frenchay Hospital. "The optimisation of diffusion imaging to quantify fluid flow within the central nervous system'' 1/1/2008--31/12/2009.
    15.   2.15  EPSRC CASE Studentship. 1/10/2005--31/9/2008.
    16.   2.16  EPSRC. "Multiple-fibre reconstruction in diffusion MRI''. GR/T22858/01. 1/1/2005--31/12/2007.
    17.   2.17  EPSRC EngD studentship (Sortex). 1/11/2004--31/10/2007.
    18.   2.18  EPSRC EngD studentship (Philips Medical Systems). 1/10/04--30/9/07.
    19.   2.19  EPSRC Fast-stream grant. "Registration of DT MRI''. GR/R13715/01. 1/2/2001--31/1/2004.
    20.   2.20  EPSRC/DTI E-science intiative. 1/10/2001--31/3/2002.

1.  Current Projects

1.1  EPRSC. “Learning MRI and histology image mappings for cancer diagnosis and prognosis”. 15/12/2017 – 14/12/2020.

  • Personnel: Francesco Grussu, Thomy Mertzanidou, Eleftheria Panagiotaki, Daniel Alexander.
  • Summary: This project aims to exploit recent advances in machine learning to address acute problems in cancer management – most directly prostate cancer. We will obtain data relating i) MRI and histology images, and ii) histology and patient outcome. In combination, these support a two-step learning and estimation process: from MRI to histological features; and from histological features to patient prognosis. Such mappings can provide invaluable new information for clinical decision making, as well as guide the design of maximally informative future MRI protocols. The summary of the project is on the EPSRC website here.

1.2  EPRSC Fellowship. “Non-invasive MRI biomarkers for Oncology”. 1/07/2016 – 30/06/2021.

  • Personnel: Eleftheria Panagiotaki
  • Summary: This project develops innovative Magnetic Resonance Imaging (MRI) methods to reveal new non-invasive markers of cancer pathology. In particular the research programme develops biophysical models of tumour tissue that support non-invasive estimates of key characteristics of tissue cellular architecture including those that currently guide diagnosis, grading, and treatment assignment through classical histology. The summary of the project is on the EPSRC website here.

1.3  EPRSC. “A biophysical simulation framework for magnetic resonance microstructure imaging”. 1/04/2016 – 31/03/2019.

  • Personnel: Gary Zhang, Simon Walker-Samuel, Karin Shmueli, Daniel Alexander.
  • Summary: This project extends the diffusion simulation in Camino to include a range of new effects including flow, permeability, and susceptibility. It makes the first steps towards developing multi-modal MR tissue models and next-generation imaging techniques that exploit them. The summary of the project is on the EPSRC website here.

1.4  EPSRC Doctoral Prize Fellowship. “Quantitative diffusion MRI for prostate cancer grading”. 1/01/2016 – 01/01/2018.

  • Personnel: Andrada Ianus.
  • Summary: In prostate cancer, the tissue structure changes significantly as the tumour becomes more aggressive. The aim of this project is to develop advanced diffusion MRI techniques, which are sensitive enough to provide a non-invasive classification of tumour grades. The work involves the design of better acquisition sequences, as well as microstructural models for data analysis that capture the changes in cellular architecture.



1.5  Prostate Cancer UK. “CombIning advaNces in imagiNg with biOmarkers for improVed diagnosis of Aggressive prosTate cancEr - INNOVATE”. 1/01/2016 - 31/12/2017.

  • Personnel: Hayley Whittaker (PI), Shonit Punwani, Laura Panagiotaki, Elisenda Bonet-Carne, David Atkinson, Edward Johnston, Dave Hawkes, Daniel Alexander et al.
  • Summary: INNOVATE is a prospective single centre cohort study in 365 patients which aims to improve the early detection for the aggressive prostate cancer and to increase the MRI diagnostic performance. MIG’s role focuses on testing whether the performance of the multi parametric MRI can be improved by the addition of VERDICT. VERDICT is an advanced diffusion-weighted MRI technique that was designed to capture the main microstructural properties of cancerous tissue. It uses a biophysical model to characterise tissue microstructure. Here is the clinical study record detail website.
  • Related Publications:
    • Panagiotaki E, Chan RW, Dikaios N, Ahmed HU, Urol F, Callaghan JO, Freeman A, Atkinson D, Punwani S, Hawkes DJ, Alexander DC. Microstructural Characterization of Normal and Malignant Human Prostate Tissue With Vascular , Extracellular , and Restricted Diffusion for Cytometry in Tumours Magnetic Resonance Imaging. Invest Radiol. 2015;50(4):218–27.
    • Johnston EW, Pye H, Bonet-Carne E, Panagiotaki E, Patel D, Galazi, Heavey S, Carmona L, Freeman A, Trevisan G, Alexander D.C et al. INNOVATE: A prospective cohort study combining serum and urinary biomarkers with novel diffusion-weighted magnetic resonance imaging for the prediction and characterization of prostate cancer. BMC Cancer 31 Oct 2016 .
    • Bonet-Carne E, Daducci A, Panagiotaki E, Johnston E, Stevens N, Atkinson D, Shonit Punwani S, Alexander DC. Non-invasive quantification of prostate cancer using AMICO framework for VERDICT MR. Intl Soc Mag Reson Med 24. Singapore; 2016.



1.6  Microsoft Research PhD Studentship. “Image Quality Transfer”. 1/10/2015 – 28/09/2019.

  • Personnel: Ryutaro Tanno (UCL), Aurobrata Ghosh (UCL), Daniel Alexander (UCL), Antonio Criminisi (MSR).
  • Summary: This project aims to develop an image reconstruction technology called image quality transfer into a working software tool cutting across several applications. The technique learns a model of the low-level structure of an image ensemble from high quality data sets that are expensive to obtain and uses the model to enhance reconstruction from sparse data that is more practical to acquire. The figure shows a demonstration using MRI data from the human connectome project (HCP) and a simple regression model using random forests to learn a mapping from low-resolution to high-resolution image patches. The idea has important potential in that domain. First, it potentially reveals subtle information hidden in low-resolution images providing more sensitive markers of disease. Second, it potentially supports exploitation of large bodies of historical data from heterogeneous acquisition protocols, which hugely increases statistical power in population or phenotype studies.
  • Related Publications:

1.7  NIH. “Structure and Function of the Placenta from Implantation to Delivery. A Next Generation MRI Approach”. 17/09/2015 - 31/08/2018.

  • Personnel: Paddy Slator, Daniel Alexander, Jana Hutter (KCL), Jo Hajnal (KCL), Mary Rutherford (KCL).
  • Summary: Many pregnancy complications stem from placental abnormalities. This project aims to develop tools which assess placental development in the earliest stages of pregnancy. We are developing diffusion-weighted MRI techniques, similar to the NODDI and VERDICT methods, to extract detailed microstructural information from in-vivo human placentae. More details here: http://placentaimagingproject.org/project/.



1.8  EU Horizon 2020. “A Clinical Decision Support system based on Quantitative multimodal brain MRI for personalized treatment in neurological and psychiatric disorders (CDS-QUAMRI)”. 1/09/2015 – 01/09/2020.

  • Personnel: Francesco Grussu, Enrico Kaden, Daniel Alexander, Claudia Wheeler-Kingshott
  • Summary: The objective of this project is the development of a clinical decision support system for neurological and psychiatric disorders that is based on multimodal quantitative magnetic resonance imaging, advanced feature extraction and multi-parametric classification. To that the quantitative analysis of structural, functional and metabolic MRI data (11 modalities) shall be fully integrated into a single software framework for the first time; support of large data, interoperability and access for non-expert users shall be enabled and a machine learning based classification module shall be developed. The quantification and feature extraction algorithms for metabolic, perfusion, diffusion and functional imaging shall be enhanced to access the full information content of the data independent of vendor specific scan protocols as required for future use in diagnostics, stratification and monitoring of patients.

1.9  Magnetic Resonance Imaging in Multiple Sclerosis (MAGNIMS). “To explore imaging signature of multiple sclerosis phenotypes”. 1/07/2015 – 01/07/2016.

  • Personnel: Arman Eshaghi, Alan Thompson, Daniel Alexander, Frederik Barkhof, Olga Ciccarelli.
  • Summary: Clinicians classify patients with multiple sclerosis (MS) into different phenotypes according to clinical presentation, qualitative MRI and their changes. MS phenotypes are the cornerstone for treatment and rehabilitation strategies, however, clinical classification of patients is crude. We aimed to use a large MRI dataset from different European Multiple Sclerosis Centres (part of MAGNIMS) in London, Rome, Siena, and Amsterdam to explore signatures of different MS phenotypes. Computational modelling of disease progression (such as brain shrinkage) can achieve objective classification of patients. In this project Institute of Neurology works closely with the POND/MIG group.



1.10  EPRSC. “National Facility for In Vivo MR Imaging of Human Tissue Microstructure”. 1/06/2015 – 31/05/2020.

  • Personnel: Derek Jones (Cardiff), Richard Bowtell (Nottingham), Geoff Parker (Manchester), Karla Miller (Oxford), Krish Singh (Cardiff), Daniel Alexander (UCL), Hywel Thomas (Cardiff), Flavio Dell'Acqua (KCL), Mara Cercignani (Sussex), Richard Wise (Cardiff).
  • Summary: This equipment grant puts in place the National Microstructure Imaging Facility in Cardiff's CUBRIC centre. The device has very high magnetic gradient field strength (up to 300mT/m) enabling exquisite sensitivity to tissue microstructure. It supports the development of future microstructure imaging techniques putting them in place for when this kind of hardware becomes more widespread in the future.

1.11  EPSRC. “Anatomy driven brain connectivity mapping”. 1/06/14 – 31/05/17.

  • Personnel: Aurobrata Ghosh, Gary Zhang, Daniel Alexander, Stam Sotiropoulos, Saad Jbabdi, Tim Behrens
  • Summary: This project is a collaboration with the FMRIB in Oxford and combines cutting-edge microstructure imaging with prior knowledge learned from histology to bypass inherent limitations in current tractography algorithms. Here is the project outline on the EPSRC website.
  • Selected Publications:
    • Aurobrata Ghosh, Daniel C. Alexander, and Hui Zhang. Crossing versus Fanning: Model Comparison Using HCP Data. Proceedings of Computational Diffusion MRI MICCAI Workshop, October 2015, Munich, Germany.

1.12  MRC. “In vivo microstructural neuroimaging in infants at risk of developing neurocognitive delay or neurobehavioural disorders”. 1/04/14 – 31/03/19.

  • Personnel: Serena Counsell, Jo Hajnal, David Edwards, Paul Aljabar, Gary Zhang, Daniel Alexander.
  • Summary: This adapts the latest brain microstructure imaging techniques coming out of MIG for perinatal imaging and exploits the techniques to predict downstream functional deficits.

1.13  UCL CS Research Excellence Studentship. "Automatic analysis of histology with machine learning". 10/03/2014 — 01/09/2017.

  • Personnel: Joseph Jacobs, Laura Panagiotaki, Daniel Alexander.
  • Summary: This project aims to explore methods for automatic and quantifiable analysis of histological images. Specifically, we intend to develop novel machine learning and pattern recognition for detecting and grading prostate cancer in histology. These methods could then be used to enable computer-aided diagnosis of prostate cancer in histology or for validation of non-invasive imaging modalities.
  • Selected Publications:
    • Jacobs JG, Panagiotaki E, Alexander DC. Gleason Grading of Prostate Tumours with Max-Margin Conditional Random Fields. MICCAI Workshop on Machine Learning in Medical Imaging 2014.
    • Jacobs JG, Johnston E, Freeman A, Patel D, Rodriguez-Justo M, Atkinson D, Punwani S, Brostow G, Alexander DC, Panagiotaki E. Histological validation of VERDICT cellularity map in a prostatectomy case. Intl Soc Mag Reson Med 24. Singapore; 2016.

1.14  UCL SLMS Grand Challenge Studentships project 2013. “Parcellation of the human cortex using quantitative MRI”. 1/10/2013—30/09/2016.

  • Personnel: Tara Ganepola, Zoltan Nagy, Daniel C. Alexander and Martin Sereno.
  • Summary: The project aims to develop a novel technique for in-vivo cortical parcellation. The approach is based on probing cytoarchitectonic variations in grey matter regions using diffusion MRI and consists of optimising three main aspects of the pipeplie. i) Image acquisition in order to maximise information gain between grey matter regions ii) Methods for describing the underlying tissue microstructure captured in HARDI data and validation of these approaches using ex-vivo data. iii) Determination of appropriate classification methods for this application. The project is a collaboration between the Neuroimaging Group at the Department of Cognitive, Perceptual and Brain Sciences (UCL) and MIG and is supervised by Prof. Martin I. Sereno and Prof. Daniel C. Alexander.
  • Selected Publications:
    • Zoltan Nagy, Daniel C. Alexander, David L. Thomas, Nikolaus Weiskopf, Martin I. Sereno (2013). Using high angular resolution diffusion imaging data to discriminate cortical regions. PLos One Vol.8 .
    • Zoltan Nagy, Tara Ganepola, Martin I. Sereno, Nikolaus Weiskopf, Daniel C. Alexander. Combining HARDI datasets with more than one b-value improves diffusion MRI-based cortical Parcellation. Proceedings of the joint annual meeting ISMRM-ESMRMB 2014, p.0800.

1.15  EPSRC Breast cancer project. “Medical imaging markers of cancer initiation, progression and therapeutic response in the breast based on tissue microstructure”. 1/01/13 – 31/12/15.

1.16  UCL SLMS Grand Challenge Studentship project. "Development and validation of machine learning techniques to facilitate diagnosis and predict prognosis in patients with Multiple Sclerosis". 16/11/12-15/11/15

  • Personnel: Viktor Wottschel, Daniel Alexander and Olga Ciccarelli.
  • Summary: This projects aims to predict diagnosis and prognosis of MS patients from previously aquired MRI data sets. Supervised machine learning classifiers will be explored and optimised to extract information from multi-centre MRI data and create a general model applicable to previously unseen patients. The project is part of a collaboration between the NMR Research Unit at the Insitute of Neurology and MIG and is supervised by Dr Olga Ciccarelli and Prof. Daniel C. Alexander.
  • Selected Publications:
    • Olga Ciccarelli, et al. (2012). Predicting clinical conversion to multiple sclerosis in patients with clinically isolated syndrome using machine learning techniques, ECTRIMS, No. 113.
    • Viktor Wottschel, et al. (2013). Prediction of second neurological attack in patients with clinically isolated syndrome using support vector machines. Proceedings of the international workshop on PRNI 2013.

2.  Completed Projects

2.1  CMIC Platform Grant (pump-priming Award). “Enabling multi-site high precision spinal cord MRI”. 01/06/2017 – 01/06/2018.

  • Personnel: Francesco Grussu, M. Jorge Cardoso, Claudia A. M. Gandini Wheeler-Kingshott, Daniel C. Alexander.
  • Summary: The aim of this project is to adapt recent advances made in the post-processing of diffusion MRI data of the brain to the particular challenges of quantitative MRI (qMRI) of the spinal cord, such as diffusion and myelin imaging. We aim to demonstrate, for the first time, the relevance of combining state-of-the-art noise and Gibbs ringing removal to enhance the reproducibility and the sensitivity to pathology of multi-modal spinal cord qMRI. For this purpose, multi-vendor, multi-modal qMRI data of the spinal cord acquired with cutting edge sequences implemented in clinical scanners will be analysed. The project is a collaboration between MIG, TIG and the UCL Queen Square MS Centre.

2.2  UCL SLMS Grand Challenge Studentships project 2012. “Axonal density as MR imaging biomarker: from bench to bedside”. 1/10/2012—30/09/2015.

  • Personnel: Francesco Grussu, Torben Schneider, Hui Zhang, Daniel C. Alexander and Claudia A. M. Wheeler-Kingshott.
  • Summary: the project aims to take advanced diffusion MRI methods for microstructure quantification from the development stage to the clinical application in the spinal cord. The work consists of two main goals: i) appropriate acquisition protocols are to be designed and tested for clinical settings at 3T; ii) the novel diffusion MRI indices are to be compared to histological correlates, in both healthy and multiple sclerosis spinal cord samples, for validation. The project focusses on the optimisation and validation of Neurite Orientation Dispersion and Density Imaging (NODDI) and is part of a collaboration between the NMR Research Unit at the Department of Neuroinflammation (UCL Insitute of Neurology) and MIG. It is supervised by Dr Claudia A. M. Wheeler-Kingshott and Prof. Daniel C. Alexander.
  • Selected Publications:

2.3  EPSRC Brain connectivity project. “Robust graph analysis of brain connectivity”. 1/06/2012—31/05/2015.

  • Personnel: Fani Deligianni, Chris Clark, David Carmichael, Daniel Alexander, Jon Clayden
  • Summary: This project develops techniques for modelling functional and structural connectivity of the brain using diffusion MRI and EEG. It uses graph analysis to extract salient features and develops statistical techniques for performing group studies of graph-based metrics. Here is the project outline on the EPSRC website.

2.4  Leverhulme Trust Fellowship. "New imaging techniques to reveal the relationship between brain tissue microstructure and brain function". 1/05/2012-31/12/2015.

  • Personnel: Ivana Drobnjak
  • Summary: This projects aims to develop advanced non-invasive imaging techniques to enable the parallel study of brain microanatomy and function in living humans. The optimisation methods developed in project EP/E007748 produce novel pulse sequences with complex gradient waveforms virtually impossible to devise without the novel algorithmic approach. Phase 1 of this project will implement, refine and validate those sequences on standard clinical MRI systems. Phase 2 will apply the novel sequences to study the microstructure-function relationship in specific neuroscience experiments. The ageing brain will be the principal model for this research, but the new methodology can also be readily applied to study diseases (e.g. Alzheimer’s, Multiple Sclerosis), effects of treatment, or cognitive development.
  • Selected Publications:

2.5  EPSRC Healthcare Partnerships Project. "Axon and myelin damage assessed using advanced diffusion imaging: from mathematical models to clinical applications". 1/09/2011-31/08/2014.

2.6  EPSRC Program grant. “Intelligent Imaging: Motion, Form and Function Across Scale”. 1/06/2010—31/05/2015.

  • Personnel: Andrada Ianus, Eleftheria Panagiotaki, Daniel Alexander
  • Summary: This Program Grant is a collaboration between UCL-CMIC, King's College London and Imperial to target clinical information obtainable from imaging devices directly without necessarily reconstructing images as an intermediate step. Prof Dave Hawkes (CMIC director) leads the project. MIG's role is modelling normal and pathological tissue structure in the prostate and developing imaging techniques to probe them non-invasively.
  • Selected Publications:

2.7  EPSRC Leadership Fellowship. "Direct measurements of microstructure from MRI." EP/E007748. 1/10/2008—30/09/2014.

  • Personnel: Bernard Siow, Ivana Drobnjak, Matt Hall, Enrico Kaden, Tingting Wang, Simon Richardson, Daniel Alexander

2.8  European Commission (Future and Emerging Technologies program). “CONNECT: Consortium of NeuroImagers for the Non-invasive Exploration of Brain Connectivity and Tractography”. 1/01/2010—31/10/2012.

  • Personnel: Gary Zhang, Daniel Alexander

2.9  International Spinal Research Trust (ISRT). "Spinal Cord Diffusion Imaging: Challenging Characterization and Prognostic Value'' Natalie Rose-Barr Studentship. 1/10/2008--30/9/2011.

  • Personnel: Torben Schneider, Daniel Alexander, Claudia Wheeler-Kingshott.
  • Summary: Magnetic Resonance Imaging has shown to be very useful in evaluating several aspects of spinal cord injury (SCI) but traditional MRI has limited predictive value because it cannot readily distinguish important fine structural detail within the cord. Diffusion MRI is a very promising technique that is sensitive to microstructural tissue characteristics but it has yet to be applied to the spinal cord. In this project we investigate the potential of existing methods like diffusion tensor imaging and develop new techniques that are more sensitive to specific tissue characteristics like axon diameter and density to help understanding the underlying structural changes in SCI. With new SCI treatment strategies on the horizon, this technique will provide novel biomarkers for measuring therapy outcome and will be of great significance in monitoring the success of future clinical trials.
  • Selected Publications:
    • Schneider T, Wheeler-Kingshott CAM, Alexander DC. (2010). In-vivo estimates of axonal characteristics using optimized diffusion MRI protocols for single fibre orientation. 13th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI2010).
    • Schneider T, Alexander DC, Wheeler-Kingshott CAM. (2010). Optimized diffusion MRI protocols for estimating axon diameter with known fibre orientation. International society for magnetic resonance in medicine (ISMRM). Stockholm. p 1561.

2.10  EPSRC. "Monte Carlo random effects modelling in diffusion MRI: a new window on microstructure and white matter architecture.'' EP/G025452/01. 1/10/2008--30/9/2011.

  • Personnel: Martin King, Daniel Alexander, David Gadian, Chris Clark
  • Summary: Microstructure imaging is hard, because sensitivity of MRI measurements to interesting features of tissue microstructure, such as axon diameter or the size of cancer cells, is low, particularly in measurements from clinical MRI scanners. This project uses advanced statistical techniques to pool information from similar image voxels and thus increase sensitivity to the subtle changes we look for in microstructure imaging. Early stages of the project use random effects modelling for increasing the accuracy of estimates of crossing fibre estimates. Later stages will use the same approach for mapping axon diameter and density in live human subjects.
  • Selected Publications:

2.11  EPSRC CASE Studentship with GSK. 28/9/2008--27/9/2011.

  • Personnel: Gemma Morgan, Daniel Alexander
  • Summary: Schizophrenia is a severe psychiatric disease which affects approximately 1% of the population. Both structural and functional changes to the brain have been implicated in the disease, although we still have a very limited understanding of what these changes are. Previous structural studies have used DTI to measure changes (often conflicting!) in FA and MD, but these indices are vague and non-specific. In this project, we aim to identify new imaging biomarkers which directly relate to the underlying microstructure and develop techniques for mapping the microstructural changes in schizophrenia in a clinical setting.
  • Selected publications:
    • Morgan GL, Zhang H, Whitcher B and Alexander DC. (2010). A spatial variation model of white matter microstructure. Workshop on Computational Diffusion MRI, Medical Image Computing and Computer-assisted Intervention (MICCAI)
    • Morgan GL, Zhang H, Whitcher B and Alexander DC. (2011). A Bayesian framework for modelling the regional variation of white matter microstructure. Medical Image Understanding and Analysis

2.12  EPSRC Doctoral Prize (PhD+). “Differentiating grades of brain tumour with DW-MRI”. 1/01/2011-1/01/2012.

  • Personnel: Eleftheria Panagiotaki, Daniel Alexander
  • Summary: The EPSRC Doctoral prize supports the award of fellowships of up to one year's duration. The scheme allows students to advance their PhD research as fellows. The motivation for this project has been the replacement of biopsy with non-invasive imaging to determine brain tumour grade. An accurate classification of the brain tumour grade is imperative as it determines the treatment plan for the patient. However, the standard classification comes from histopathology after the traumatic and uncomfortable biopsy which carries its own risks of mortality. The aim of this research has been to develop models of the diffusion-weighted (DW-MRI) signal in brain tumours ultimately to enable non-invasive differentiation between low and high grades. The plan for this one year was to identify a parsimonious parametric model relating all important microstructure indices, such as cellularity and mitotic index, to the signal. As proof of concept, the project investigated animal models of cancer and brain tumour biopsy samples which reflect the clinical condition.
  • Selected Publications:

2.13  EPSRC. "Monte-Carlo simulation framework for diffusion MRI.'' EP/E056938/1. 1/9/07--31/8/10.

  • Personnel: Eleftheria Panagiotaki, Matt Hall, Daniel Alexander
  • Summary: A frequent difficulty with new imaging methodologies, particularly imaging in highly complicated structures, is that the lack of a ground truth with which to validate results. This project involves constructing a Monte-Carlo simulation framework for diffusion MRI which is flexible and capable of synthesising data from highly detailed environments. The simulation generates trajectories for a population of diffusing spins in a known environment that restricts their motion as well as simulating the action of an appropriate MR pulse sequence in order to directly generate diffusion attenuated signals.
  • A significant part of this project has involved constructing highly detailed triangle-mesh substrates from Confocal Laser Microscopy images of tissue from biological samples. We acquire a stack of images which are then processed into a mesh of a few hundred-thousand triangles which are then imported into the simulation and used to generate signals. We can then compare the simulated signals the results of putting the same sample the MRI scanner. This gives us confidence that the simulation is a capable of closely reproducing data from a complex sample.
  • The simulation has been used in many studies of new analysis methods, including several of the other projects on this page. In addition to triangle meshes, it can use cylinders of different radii and packings (ordered and disordered) and is capable of simulating an almost unlimited set of pulse-sequence waveforms.
  • Selected Publications:

2.14  NHS, Frenchay Hospital. "The optimisation of diffusion imaging to quantify fluid flow within the central nervous system'' 1/1/2008--31/12/2009.

  • Personnel: Hubert Fonteijn, Daniel Alexander
  • Summary: The delivery of drugs to the brain via normal ways (i.e. intraveneously) is complicated because of the Blood-Brain Barrier. An alternative to intraveneous delivery is Convection Enhanced Delivery (CED). In CED a catheter is placed directly into the brain from which drugs are infused. Drugs either fill up the volume locally, or follow white matter paths to reach more distant brain regions. This might for instance be one or several tumour sites. CED can only be used when we know beforehand where the drugs will end up. The goal of this project is to develop accurate simulations of CED. We use DWI-derived parameters, such as fibre direction, as an input for these simulations and assess their accuracy. We also work on infusion planning algorithms, which determine what the optimal infusion site is given a Region of Interest.
  • Selected Publications
    • Fonteijn, H.M., Woodhouse, M., White, E., Gill, S.S., Alexander, D.C. Determining infusion sites for Convection Enhanced Delivery using probabilistic tractography. Presented at Computational Diffusion MRI workshop, Medical Image Computing and Computer Assisted Intervention (MICCAI), Beijing, 2010

2.15  EPSRC CASE Studentship. 1/10/2005--31/9/2008.

  • Personnel: Tony Shepherd, Daniel Alexander
  • Summary: This project developed an interactive segmentation system for outlining regions of interest in images. The application of interest was lesion contouring in medical imaging, but the system has broader potential application. The key technological advance was to devise statistical shape models that do not require shapes with distinctive and consistent features. Experiments demonstrate that modelling the shapes of lesions in this way reduces the interaction time required by users outlining regions of interest.
  • Selected Publications:

2.16  EPSRC. "Multiple-fibre reconstruction in diffusion MRI''. GR/T22858/01. 1/1/2005--31/12/2007.

  • Personnel: Kiran Seunarine, Matt Hall, Daniel Alexander

2.17  EPSRC EngD studentship (Sortex). 1/11/2004--31/10/2007.

  • Personnel: Chris Senanayake, Daniel Alexander
  • Summary: This project developed methods to identify colours of objects in CCD images independent of lighting conditions. It had applications in the food industry.
  • Selected Publications:
    • Senanayake,C., Alexander,D.C. (2007). Colour transfer by feature-based histogram registration. British Machine Vision Conference, 1, 429-438

2.18  EPSRC EngD studentship (Philips Medical Systems). 1/10/04--30/9/07.

  • Personnel: Shahrum Nedjati-Gilani, Daniel Alexander

2.19  EPSRC Fast-stream grant. "Registration of DT MRI''. GR/R13715/01. 1/2/2001--31/1/2004.

  • Personnel: Kathleen Curran, Daniel Alexander
  • Summary: This project studied techniques for registration of diffusion tensor images. Registration of these images is complicated by the orientational information at each image voxel, which must be preserved through warps of the image. The TMI 2001 paper below provides algorithms for preserving orientation through image warps. Later papers describe how we can exploit that information for improving image matching and registration.
  • Selected Publications:

2.20  EPSRC/DTI E-science intiative. 1/10/2001--31/3/2002.

  • Personnel: Phil Cook, Chris Parker, Anthony Steed, Daniel Alexander.
  • Summary: This project built an interactive visualization for diffusion tensor imaging data in an immersive virtual environment. Users view and navigate around the data within a virtual reality CAVE and can collaborate with other users viewing the same data set at remote locations. The system has potential for education and remote consultation. It was demonstrated at the opening of the National E-science Centre in April 2002.
  • Selected Publications:
    • Steed,A., Alexander,D., Cook,P., Parker,C. (2003). Visualizing Diffusion-Weighted MRI Data Using Collaborative Virtual Environment and Grid Technologies. Theory and Practice of Computer Graphics, IEEE Computer Society

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