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University College London
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020 354 95528 (65528)
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Miscellaneous Information

As a Lecturer at University College London within the Translational Imaging Group, I lead the development of advanced quantitative imaging techniques for fetal and neonatal development. With clinical colleagues I design computational techniques and implement MRI studies to answer research questions in the neonatal and adult brain and I am developing novel 3T techniques for fetal imaging at University College Hospital.

 

Quantitative Neuroimage Analysis - NiftyFit

logo_v1.pngMy recent work has begun to develop software to allow advanced quantitative image analysis for the unified measurement of intrinsic tissue properties such as the axon-myelin diameter g-ratio [2014:2,2016:1]. Specifically my recent work has been applied to quantitative multi-component T2 relaxometry to measure tissue T2 and myelin water fraction [2015:4,2014:2,2014:11,2013:3] (with Zach Eaton-Rosen); multi-shell, multi-compartment diffusion imaging for quantitative tissue analysis of the preterm brain in both the preterm and young-adult period [2015:4,2015:6,2015:7,2014:2,2014:3] (with Eliza Orasanu and Michael Hutel) and arterial spin labelled (ASL) imaging [2015:4,2014:4] to analyse the cerebral blood flow (with David Owen). The ASL measurement is also supported by distinct algorithms for tissue T1 measurement. The supporting multi-parametric software is available as an open-source package available here.

Imaging of the Extremely Preterm Brain - The Preterm Imaging Project

pip.pngImprovements in neonatal care have reduced the lowest gestational age at which survival after premature birth becomes a clinical reality, however the prevalence of disabling conditions in survivors remains high. Many conditions are thought to be related to perinatal white matter injury affecting subsequent neurological maturation often investigated by postnatal MRI. This has stimulated efforts to develop MR markers of outcome to guide mitigating treatment or therapy. My recent research has begun to develop, in close collaboration with imaging physicists and clinical colleagues, multi-modal imaging biomarkers of future functional brain development in at-risk very preterm babies (those born at less than 32 weeks completed gestation) and to extend this work to preterm adolescents. I have helped establish the MR imaging protocols for the longitudinal, multi-modal SPARKS funded University College Hospital preterm development project (with Eliza Orasanu and Zach Eaton-Rosen) and the MRC funded EPICure@19 project which has a substantial multi-modal neuroimaging component. Initial publications are now in progress. In particular I have published recent papers investigating the macroscopic cortical folding pattern at term equivalent age [2014:7] and begun investigating multi-modal imaging of infant myelination and white matter maturation on MR spectroscopy, Diffusion Weighted MR and T2 relaxometry [2014:1, 2014:6, 2013:1, 2016:1]. With this work I hope to investigate micro and macroscopic structural brain changes in the context of improved energetic and functional efficiency. My immediate future work will combine these results and investigate new measurements of multi-modal, multi-scale structural efficiency and compare these with separate functional neuropsychological data. This work will aid prediction of long term functional outcome and further establish the sequence of human brain growth and development at this very young age.

Image Registration and Motion Correction

Previously I have worked on motion correction and pharmacokinetic modelling applied to dynamic contrast enhanced MRI in the liver and breast [2014:9, 2014:10, 2011:1, 2007:1]. Some of these algorithms are availble in the NiftyReg software package. Subject movement within this type of scan can be detrimental to subsequent analysis of tissue pharmacokinetics, but motion correction is made difficult by the indistinguishability of contrast change and motion artefacts [2008:1]. In this area my work focuses on image registration techniques that are able to correct motion in the presence of contrast change. This work was funded by the EPSRC Grants GR/T 20434/0 and EP/E031579/1 and the European 7th Framework Program, HAMAM, ICT-2007.5.3.

Teaching

IMG_20160224_160232.jpgMy key contributions to teaching in the department are continued organisation of the flagship Medical Physics CDT Critical Review of Key Papers in Biomedical Imaging course (Previously the CMIC/CABI DTP Journal Club) from 2009-present, developed to assess students in key research skills such as presentation, critical reading and writing. I was the key organiser of the Information Processing in Medical Imaging MSc course from 2010-2014 preparing and giving lectures, coursework and setting/marking of the final examination before handing over to Marc Modat in November 2014, and I also contribute significant coursework to the Computational Modelling in Biomedical Imaging course (2011-present) which has lead to its own publications [Dingwall 2016]. In addition I prepare a number of lectures and examination for the undergraduate Physics of the Human Body Medical Physics MSci course (2011-2015) and introduced Raspberry Pi based teaching and research-led coursework to the undergraduate Computing in Medicine Medical Physics MSci course, (2015-present).

 

Biography

I obtained a 1st Class MSci Physics degree from Imperial College London in 2005 followed by a PhD from UCL three years later investigating motion correction strategies in contrast-enhanced magnetic resonance imaging (MRI). I am now working within the Centre for Medical Image Computing at University College London investigating the use of quantitative MRI in establishing the links between preterm birth and subsequent neuro-developmental outcome in babies born very preterm.

Publications

Journal articles in bold. Papers with a significant supervisory role in blue.

2017
[1] Andrew Melbourne, Rosalind Pratt, Magdalena Sokolska, David Owen, Alan Bainbridge, David Atkinson, Giles Kendall, Jan Deprest, Tom Vercauteren, Anna David, and Sebastien Ourselin. Decide: Diffusion-relaxation combined imaging for detailed placental evaluation. Accepted for ISMRM, 2017.
[2] Michael Hutel, Andrew Melbourne, David Thomas, Jonathan Rohrer, and Sebastien Ourselin. Sparse network analysis of individual resting-state bold-fmri. Accepted for ISMRM, 2017.
[3] Michael Hutel, Andrew Melbourne, Joanne Beckmann, Jonathan Rohrer, Neil Marlow, and Sebastien Ourselin. Resting-state network patterns in extremely preterm born young adults. Accepted for ISMRM, 2017.
[4] Michael Hutel, Andrew Melbourne, Dave Thomas, Jonathan Rohrer, and Sebastien Ourselin. Retrospective independent component estimation of respiratory and cardiac artefact residuals (ricercar) in bold-fmri. Accepted for ISMRM, 2017.
[5] David Owen, Andrew Melbourne, Magdalena Sokolska, David L Thomas, Jonathan Rohrer, and Sebastien Ourselin. Bayesian experimental design for multi-parametric t1/t2 relaxometry and diffusion. Accepted for ISMRM, 2017.
[6] D. Owen, A. Melbourne, D. L. Thomas, J. Beckmann, J. Rohrer, N. Marlow, and S. Ourselin. Adrimo: Anatomy-driven modelling of spatial correlation to improve analysis of arterial spin labelling data. Accepted for ISMRM, 2017.
2016
[1] Catherine J. Scott, Jieqing Jiao, Andrew Melbourne, Brian Hutton, Jonathan M. Schott, and Sebastien Ourselin. incorporated pharmacokinetic modelling of pet data with reduced acquisition time: Application to amyloid imaging. In MICCAI, 2016.
[2] David Owen, Andrew Melbourne, David Thomas, Enrico De Vita, Jonathon Rohrer, and Sebastien Ourselin. Optimisation of arterial spin labelling using bayesian experimental design. In MICCAI, 2016.
[3] Zach Eaton-Rosen, Andrew Melbourne, M. Jorge Cardoso, Neil Marlow, and Sebastien Ourselin. Beyond the resolution limit: diffusion parameter estimation in partial volume. In MICCAI, 2016.
[4] Eliza Orasanu, Pierre-Louis Bazin, Andrew Melbourne, Marco Lorenzi, Herve Lombaert, Nicola Robertson, Giles Kendall, Nicolaus Weiskopf, Neil Marlow, and Sebastien Ourselin. Longitudinal analysis of the preterm cortex using multi-modal spectral matching. In MICCAI, 2016.
[5] Andrew Melbourne, Rosalind Pratt, David Owen, Magdalena Sokolska, Alan Bainbridge, David Atkinson, Tom Vercauteren Giles Kendall, Jan Deprest, Anna L. David, and Sebastien Ourselin. Placental image analysis using coupled diffusion-weighted and multi-echo t2 mri and a multi-compartment model. In Perinatal, Preterm and Paediatric Image Analysis Workshop (PIPPI), 2016.
[6] Andrew Melbourne, Zach Eaton-Rosen, Eliza Orasanu, Joanne Beckmann, David Atkinson, Neil Marlow, and Sebastien Ourselin. Analysis of brain tissue volume and composition in an extremely-preterm born adolescent cohort. In Perinatal, Preterm and Paediatric Image Analysis Workshop (PIPPI), 2016.

[7] Eliza Orasanu, Andrew Melbourne, M. Jorge Cardoso, Herve Lombaert, Giles S. Kendall, Nicola J. Robert- son, Neil Marlow, and Sebastien Ourselin. Cortical folding of the preterm brain: a longitudinal analysis of extremely-preterm born neonates using spectral matching. Brain and Behavior, In Press, 2016.

[8] Melbourne, A.; Eaton-Rosen, Z.; Orasanu, E.; Price, D.; Bainbridge, A.; Cardoso, M. J.; Kendall, G. S.; Robertson, N. J.; Marlow, N. & Ourselin, S. Longitudinal development in the preterm thalamus and posterior white matter; MRI correlations between Diffusion Weighted Imaging and T2 relaxometry. Human Brain Mapping, 2016, Epub.
[9] Andrew Melbourne, Nicolas Toussaint, David Owen, Ivor Simpson, Thanasis Anthopoulos, Enrico De Vita, David Atkinson, Sebastien Ourselin. NiftyFit: A software package for multi-parametric model-fitting of 4D magnetic resonance imaging data. NeuroInformatics 2016. Epub.
[10] Andrew Melbourne, Eliza Orasanu, Zach Eaton-Rosen, Manuel J Cardoso, Joanne Beckmann, Lorna Smith, David Atkinson, Neil Marlow, and Sebastien Ourselin. Analysis of brain volume in a 19 year-old extremely- preterm born cohort. Accepted for ISMRM, 2016.
[11] Andrew Melbourne, Zach Eaton-Rosen, Eliza Orasanu, Joanne Beckmann, Alexandra Saborowska, David Atkinson, Neil Marlow, and Sebastien Ourselin. Perfusion and diffusion in the extremely preterm young adult thalamus. Accepted for ISMRM, 2016.
[12] Andrew Melbourne, Eliza Orasanu, Zach Eaton-Rosen, Joanne Beckmann, Alexandra Saborowska, David Atkinson, Neil Marlow, and Sebastien Ourselin. Characterizing microstructure and shape of the extremely preterm 19 year-old corpus callosum. Accepted for ISMRM, 2016.
[13] Andrew Melbourne, Enrico De Vita, John Thornton, and Sebastien Ourselin. Coupled fitting of t2 relax- ometry and multi-shell diffusion weighted image data. Accepted for ISMRM, 2016.
[14] Eliza Orasanu, Andrew Melbourne, Zach Eaton-Rosen, David Atkinson, Joshua Lawan, Joanne Beckmann, Neil Marlow, and Sebastien Ourselin. Local shape analysis of the thalamus in extremely preterm born young adults. Accepted for ISMRM, 2016.
[15] Eliza Orasanu, Andrew Melbourne, Zach Eaton-Rosen, David Atkinson, Alexandra Saborowska, Joanne Beckmann, Neil Marlow, and Sebastien Ourselin. Cortical folding patterns in extremely preterm born young adults. Accepted for ISMRM, 2016.
[16] Eliza Orasanu, Andrew Melbourne, Marc Modat, Marco Lorenzi, Herve Lombaert, Zach Eaton-Rosen, Nicola Robertson, Giles Kendall, Neil Marlow, and Sebastien Ourselin. Mapping longitudinal white matter changes in extremely preterm born infants. Accepted for ISMRM, 2016.
[17] Zach Eaton-Rosen, Andrew Melbourne, Eliza Orasanu, Joanne Beckmann, Nicola Stevens, David Atkinson, Neil Marlow, and Sebastien Ourselin. White matter alterations in young adults born extremely preterm: a microstructural point of view. Accepted for ISMRM, 2016.
[18] Michael Hutel, Andrew Melbourne, David Thomas, Jon Rohrer, and Sebastien Ourselin. The hidden heart rate in the slice-wise bold-fmri global signal. Accepted for ISMRM, 2016.
[19] Michael Hutel, Andrew Melbourne, David Thomas, Jon Rohrer, and Sebastien Ourselin. An overcomplete and efficient ica for bold-fmri. Accepted for ISMRM, 2016.
[20] Sjoerd B. Vos, Andrew Melbourne, John S. Duncan, and Sebastien Ourselin. Intracellular volume fraction estimation in vivo in single and crossing fibre regions. Accepted for ISMRM, 2016.
[21] I. Huen, J. Beckmann, Y. Suzuki, M. Zuluaga, A. Melbourne, M. van Osch, D. Atkinson, S. Ourselin, N. Marlow N., and X. Golay. Measurement of bolus arrival time and velocity in circle of willis using dynamic mr angiography. Accepted for ISMRM, 2016.
[22] Enrico De Vita, Andrew Melbourne, Marie-Claire Porter, David L Thomas, Sebastian Ourselin, Tarek Yousry, Xavier Golay, Rolf Jager, Simon Mead, and John S Thornton. Arterial spin labelling perfusion measurements in prion disease: relation with restricted diffusion. Accepted for ISMRM, 2016
[23] Dingwall, N.; Chalk, A.; Martin, T. I.; Scott, C. J.; Semedo, C.; Le, Q.; Orasanu, E.; Cardoso, J. M.; Melbourne, A.; Marlow, N. & Ourselin, S. T2 relaxometry in the extremely-preterm brain at adolescence Magnetic Resonance Imaging, 2016, 2016 May;34(4):508-14
[24] Lehmann, M.; Melbourne, A.; Dickson, J. C.; Ahmed, R. M.; Modat, M.; Cardoso, M. J.; Thomas, D. L.; Vita, E. D.; Crutch, S. J.; Warren, J. D.; Mahoney, C. J.; Bomanji, J.; Hutton, B. F.; Fox, N. C.; Golay, X.; Ourselin, S. & Schott, J. M. A novel use of Arterial Spin Labelling MRI to demonstrate focal hypoperfusion in individuals with posterior cortical atrophy: a multi-modal imaging study Journal of Neurology, Neurosurgery & Psychiatry, 2016, Accepted for Publication
[25] Eaton-Rosen, Z.; Melbourne, A.; Cardoso, M. J.; Bainbridge, A.; Kendall, G. S.; Robertson, N. J.; Marlow, N. & Ourselin, S. Fitting parametric models of diffusion MRI in regions of partial volume. SPIE Medical Imaging, 20166

2015

[1] E. Orasanu, A. Melbourne, M. Lorenzi, M. Modat, H. Lombaert, Z. Eaton-Rosen, G. S. Kendall, N. J. Robertson, N. Marlow, and S. Ourselin. Tensor Spectral Matching of Diffusion Weighted Images. Workshop on Spectral Analysis in Medical Imaging (SAMI), MICCAI, 2015.
[2] I. Huen, J. Beckmann, Y. Suzuki, M.A. Zuluaga, A. Melbourne, M.J.P. van Osch, D. Atkinson, S. Ourselin, N. Marlow, and X. Golay. Does extreme prematurity affect adult brain vessel compliance? a preliminary mri study. In Proceedings of Brain, 2015.
[3] I. Huen, J. Beckmann, Y. Suzuki, M.A. Zuluaga, A. Melbourne, M.J.P. van Osch, D. Atkinson, S. Ourselin, N. Marlow, and X. Golay. Does extreme prematurity affect adult brain vessel compliance? In Proceedings of ESMRMb, 2015.
[4] Melbourne, A.; Eaton-Rosen, Z.; Owen, D.; Cardoso, J.; Beckmann, J.; Atkinson, D.; Marlow, N. & Ourselin, S. Measuring cortical neurite-dispersion and perfusion in preterm-born adolescents using multi-modal MRI . MICCAI, 2015 Volume 9351 pp 72-79.
[5] M.J. Cardoso, M. Modat, R. Wolz, A. Melbourne, D. Cash, D. Rueckert, S. Ourselin. Geodesic Information Flows: Spatially-Variant Graphs and Their Application to Segmentation and Fusion. IEEE Trans. Medical Imaging, Volume:PP Issue:99 DOI:10.1109/TMI.2015.2418298.
[6] Zach Eaton-Rosen, Andrew Melbourne, Eliza Orasanu, M. Jorge Cardoso, Alan Bainbridge, Giles S. Kendall, Nicola J. Robertson, Neil Marlow, and Sebastien Ourselin. Longitudinal measurement of the developing grey matter in preterm subjects using multi-modal MRI. Neuroimage, 2015, 111, 580-589.
[7] Sjoerd B. Vos, Andrew Melbourne, Gary Zhang, John S Duncan, and Sebastien Ourselin. The effect of white matter perfusion on diffusion MRI based microstructural tissue models. Accepted for publication in ISMRM, 2015.
[8] Eliza Orasanu, Andrew Melbourne, M. Jorge Cardoso, Marc Modat, Andrew M. Taylor, Sudhin Thayyil, Sebastien Ourselin. Brain volume estimation from post-mortem newborn and fetal MRI. NeuroImage: Clinical. 6: 438–444, 2014.
[9] Isgum, I.; Benders, M. J. N. L.; Avants, B.; Cardoso, M. J.; Counsell, S. J.; Gomez, E. F.; Gui, L.; Huppi, P. S.; Kersbergen, K. J.; Makropoulos, A.; Melbourne, A.; Moeskops, P.; Mol, C. P.; Kuklisova-Murgasova, M.; Rueckert, D.; Schnabel, J. A.; Srhoj-Egekher, V.; Wu, J.; Wang, S.; de Vries, L. S. & Viergever, M. A. Evaluation of automatic neonatal brain segmentation algorithms: the NeoBrainS12 challenge. Med Image Anal, 2015, 20, 135-151

2014

[1] E. Orasanu, A. Melbourne, H. Lombaert, M. J. Cardoso, S. Johnsen, G. S. Kendall, N. J. Robertson, N. Marlow, and S. Ourselin. Pre-frontal cortical folding of the preterm brain: a longitudinal analysis of preterm-born neonates. In Workshop on Spatiotemporal Image Analysis for Longitudinal and Time-Series Image Data, MICCAI, 2014.
[2] A. Melbourne, Z. Eaton-Rosen, E. De Vita, A. Bainbridge, M. J. Cardoso, D. Price, E. Cady, G. S. Kendall, N. J. Robertson, N. Marlow, and S. Ourselin. Multi-modal measurement of the myelin-to-axon diameter g-ratio in preterm-born neonates and adult controls. In MICCAI. Lecture Notes in Computer Science 8677, pp 268-275, 2014.
[3] Z. Eaton-Rosen, A. Melbourne, E. Orasanu, M. Modat, A. Bainbridge, M. J. Cardoso, G. S. Kendall, N. J. Robertson, N. Marlow, and S. Ourselin. Longitudinal measurement of the developing thalamus in the very-preterm brain using multi-modal MRI. In MICCAI. Lecture Notes in Computer Science 8674, pp 276-283, 2014.
[4] A. Melbourne, M Lehmann, M. Modat, M.J. Cardoso, R. Ahmed, D. Thomas, E. De Vita, J. Dickson, J. Warren, C. Mahoney, J. Bomanji, B. Hutton, N. Fox, X. Golay, S. Ourselin, and J. Schott. Stratification of dementia sub-types using arterial spin labeled mri. In Alzheimer’s Association International Conference, 2014.
[5] A. Melbourne, Z. Eaton-Rosen, A. Bainbridge, G. S. Kendall, N. Robertson, N. Marlow, S. Ourselin. Measurement of the myelin-to-axon diameter g-ratio in very-preterm infants using multi-modal MRI. In ISMRM 2014, 4670.
[6] A. Melbourne, Z. Eaton-Rosen, D. Price, E. Cady, A. Bainbridge, G. S. Kendall, N. Robertson, N. Marlow, S. Ourselin. Longitudinal imaging of the preterm brain: white matter multi-component T2 relaxometry and MR spectroscopy. In ISMRM 2014, 460 (Oral Presentation).
[7] Zach Eaton-Rosen, Andrew Melbourne, Eliza Orasanu, Alan Bainbridge, Giles S. Kendall, Nicola J. Robertson, Neil Marlow and Sebastien Ourselin. Cortical maturation in the preterm period revealed using a multi-component diffusion-weighted MR model. In ISMRM 2014, 80 (Oral Presentation).
[8] Zach Eaton-Rosen, Andrew Melbourne, Eliza Orasanu, Alan Bainbridge, Giles S. Kendall, Nicola J. Robertson, Neil Marlow and Sebastien Ourselin. Measurement of white matter maturation in the preterm brain using NODDI. In ISMRM 2014, 3512.
[9] Eliza Orasanu, Andrew Melbourne, M. Jorge Cardoso, Marc Modat, Andrew M. Taylor, Sudhin Thayyil, Sebastien Ourselin. Fully automated estimation of brain volumes in post-mortem newborns and fetuses.  In ISMRM 2014, 1517.
[10] Eliza Orasanu, Andrew Melbourne, M. Jorge Cardoso, Marc Modat, Andrew M. Taylor, Sudhin Thayyil, Sebastien Ourselin. Average probabilistic brain atlases for post-mortem newborn and fetal populations and application to tissue segmentation.  In ISMRM 2014, 2001.
[11] Andrew Melbourne, Giles S Kendall, Manuel J Cardoso, Roxanna Gunny, Nicola J Robertson, Neil Marlow, and Sebastien Ourselin. Preterm birth affects the developmental synergy between cortical folding and cortical connectivity observed on multimodal MRI. NeuroImage 2014 Apr 1;89:23-34.
[12] Kendall GS, Melbourne A, Johnson S, Price D, Bainbridge A, Gunny R, Huertas-Ceballos A, Cady EB, Ourselin S, Marlow N, Robertson NJ. White matter NAA/Cho and Cho/Cr on MRS Predict Motor Outcome in Preterm Infants. Radiology, Apr;271(1):230-8.
[13] Valentin Hamy, Nikolaos Dikaios, Shonit Punwani, Andrew Melbourne, Arash Latifoltojar, Jesica Makanyang, Manil Chouhan, Emma Helbren, Alex Menys, Stuart Taylor, and David Atkinson. Respiratory motion correction in dynamic MRI using robust data decomposition registration - application to DCE-MRI. Medical Image Analysis, 18(2):301–313, 2014.
[14] Valentin Hamy, Marc Modat, Nikos Dikaios, Jon Cleary, Shonit Punwani, Rebecca Shipley, Sebastien Ourselin, David Atkinson and Andrew Melbourne. Multi-modal pharmacokinetic modelling for DCE-MRI: using diffusion weighted imaging to constrain the local arterial input function. SPIE Medical Imaging 2014 (Oral Presentation).

2013

[1] Andrew Melbourne, Zach Eaton-Rosen, Alan Bainbridge, Giles S. Kendall, M. Jorge Cardoso, Nicola J. Robertson, Neil Marlow, and Sebastien Ourselin. Measurement of myelin in the preterm brain: multi-compartment diffusion imaging and multi-component T2 relaxometry. MICCAI, Volume 8150, 2013, pp 336-344.
[2] Clarissa Garvey, Nathan Cahill, Andrew Melbourne, Christine Tanner, Sebastien Ourselin, and David Hawkes. Nonrigid image registration with two-sided space-fractional partial differential equations. In ICIP, 2013, pp 747-751
[3] A. Melbourne, G. Kendall, A. Bainbridge, M. J. Cardoso, N. Robertson, N. Marlow, and S. Ourselin. A quantitative analysis of the very preterm brain at 30 and 40 weeks gestational age; correlation of multi- component t2 relaxation and diffusion tensor anisotropy. In ISMRM, number 3591, 2013.
[4] Man Wong, Andrew Melbourne, M. Jorge Cardoso, Gemma B Northam, Sebastien Ourselin, and Torsten Baldeweg. Variegation in the adolescent cortical folding pattern in preterm and control populations. In ISMRM, number 3605, 2013. (Summa Cum Laude Merit Award).
[6] Andrew Melbourne, M. Jorge Cardoso, Giles S Kendall, Marc Modat, Nicola J Robertson, Neil Marlow, and Sebastien Ourselin. AdaPT: An adaptive preterm segmentation algorithm for neonatal brain MRI. Neuroimage, 65:97–108, Jan 2013 (Joint first authorship).

2012

[1] A. Melbourne. Comment on: Automated registration of sequential breath-hold dynamic contrast-enhanced MR images: a comparison of three techniques. MRI, In Press, 2012.
[2] A. Melbourne, M. J. Cardoso, G. Kendall, N. Robertson, N. Marlow, and S. Ourselin. Adaptive neonatal MRI brain segmentation with myelinated white matter class and automated extraction of ventricles I-IV. In MICCAI Challenge on Neonatal Brain Segmentation (NeoBrainS12 Challenge), 2012.
[3] A. Melbourne, M. J. Cardoso, G. Kendall, N. Robertson, N. Marlow, and S. Ourselin. A cortical surface analysis of very preterm infants on term-equivalent age MRI. In MICCAI Workshop on Perinatal and Paediatric Imaging: PaPI 2012, 2012.
[4] A. Melbourne, G. S. Kendall, M. J. Cardoso, R. Gunney, N. J. Robertson, N. Marlow, and S. Ourselin. Radial structure in the preterm cortex; persistence of the preterm phenotype at term equivalent age? In MICCAI. Lecture Notes in Computer Science, 2012.
[5] D. Cash, A. Melbourne, M. Modat, M. J. Cardoso, M. Clarkson, N. Fox, and S. Ourselin. Cortical folding analysis on patients with Alzheimer's disease and mild cognitive impairment. In MICCAI. Lecture Notes in Computer Science, 2012.
[6] A. Melbourne, G. Kendall, M. J. Cardoso, N. Robertson, N. Marlow, and S. Ourselin. Analysing the cortical folding pattern of very preterm neonates scanned at term-equivalent age: correlations with di ffusion tensor tractography. In ISMRM, number 95, 2012.
[7] A. Melbourne, G. Kendall, M. J. Cardoso, N. Robertson, N. Marlow, and S. Ourselin. Investigating the spatial folding pattern of very preterm neonatal cortex scanned at term-equivalent age. In ISMRM, number 3158, 2012.
[8] A. Melbourne. Correcting patient movement in dynamic contrast enhanced MRI. In ISMRM Educational Session, number 4287, 2012.
[9] A. Melbourne, M. J. Cardoso, G. Kendall, N. Robertson, N. Marlow, and S. Ourselin. Outlier rejection for adaptive neonatal segmentation. In ISMRM, number 3173, 2012.
[10] V. Hamy, A. Melbourne, B.Tremoulheac, S. Punwani, and D. Atkinson. Registration of DCE-MRI using robust data decomposition. In ISMRM, number 749, 2012.
[11] A. Melbourne, N. Cahill, C. Tanner, S. Ourselin, and D. J. Hawkes. Using fractional gradient information in nonrigid image registration: application to breast MRI. In SPIE Medical Imaging, number 8314 70, 2012.

2011

[1] A. Melbourne, J. Hipwell, M. Modat, T. Mertzanidou, H. Huisman, S. Ourselin, and D. J. Hawkes. The e ffect of motion correction on pharmacokinetic parameter estimation in dynamic-contrast-enhanced MRI. Phys Med Biol, 56(24):7693-7708, Nov 2011.
[2] A. Melbourne, G. S. Kendall, M. J. Cardoso, C. F. Hagmann, A. Bainbridge, N. Marlow, N.J. Robertson, and S. Ourselin. Automated analysis of the preterm neonatal cortex at term equivalent age and correlation with cognitive outcome at 1 year corrected age. In ESPR, 2011.
[3] M. J. Cardoso, A. Melbourne, G. S. Kendall, C. F. Hagmann, A. Bainbridge, N. Marlow, N.J. Robertson, and S. Ourselin. Adaptive neonatal brain segmentation: application to ventriculomegaly and excess extra-axial cerebral-spinal fluid. In ESPR, 2011.
[4] M. J. Cardoso, Andrew Melbourne, Giles S. Kendall, Marc Modat, Cornelia F. Hagmann, Nicola J. Robertson, Neil Marlow and Sebastien Ourselin. Adaptive neonate brain segmentation. Med Image Comput Comput Assist Interv, 14(Pt 3):378-386, 2011.
[5] A. Melbourne, J. Hipwell, M. Modat, T. Mertzanidou, H. Huisman, S. Ourselin, and D. J. Hawkes. Image registration and pharmacokinetic parameter estimation for 3d DCE-MR mammography. In ISMRM, number 3097, 2011.
[6] Y. Jafar, J. Hipwell, C. Tanner, A. Melbourne, and D. J. Hawkes. Discretisation of 3d deformation fields: Implications for establishing correspondence between 2d x-ray mammographic projections. In Proc. IEEE Int Biomedical Imaging: From Nano to Macro Symp, pages 1998{2001, 2011.
[7] A. Melbourne, N. D. Cahill, C. Tanner, and D. J. Hawkes. Image registration using an extendable quadratic regulariser. In Proc. IEEE Int Biomedical Imaging: From Nano to Macro Symp, pages 557-560, 2011.

2010

[1] A. Melbourne, J. Hipwell, and D. J. Hawkes. The effect of motion correction on pharmacokinetic parameter estimation. In International Workshop on Digital Mammography, number 6136, pages 744{751. LNCS, Springer-Verlag, 2010.
[2] A. Melbourne, D. J. Hawkes, and D. Atkinson. Data driven groupwise registration of di ffusion weighted images. In Proc. IEEE Int Biomedical Imaging: From Nano to Macro Symp, pages 352-355, 2010.
[3] A. Melbourne, G. Ridgway, and D. J. Hawkes. Image similarity metrics in image registration. In SPIE Medical Imaging, number 7623 112, 2010.

2009

[1] A. Melbourne. Alignment of Contrast Enhanced Medical Images. PhD thesis, University College London, 2009.
[2] A. Melbourne, D. Hawkes, and D. Atkinson. Image registration using uncertainty coefficients. In Proc. IEEE Int. Symp. Biomedical Imaging: From Nano to Macro ISBI '09, pages 951-954, 2009.
[3] M. J. White, D. J. Hawkes, A. Melbourne, D. J. Collins, C. Coolens, M. Hawkins, M. O. Leach, and D. Atkinson. Motion artifact correction in free-breathing abdominal MRI using overlapping partial samples to recover image deformations. Magn Reson Med, 62(2):440-449, Aug 2009.
[4] A. Melbourne, D. J. Collins, M. O. Leach, D. M. Koh, D. J. Hawkes, and D. Atkinson. Contrast enhanced image registration using kullbach-leibler assisted image matching and patching (KLAMP). In ISMRM, number 4223, 2009.
[5] A. Melbourne, M. Orton, D. J. Collins, D. M. Koh, M. O. Leach, D. J. Hawkes, and D. Atkinson. The eff ect of image registration on pharmacokinetic parameter extraction using 3d DCE-MRI. In ISMRM, number 4216, 2009.

2008

[1] A. Melbourne, D. Atkinson, and D. Hawkes. Influence of organ motion and contrast enhancement on image registration. Med Image Comput Comput Assist Interv, 11(Pt 2):948-955, 2008.
[2] A. Melbourne, D. J. Hawkes, and D. Atkinson. Non-rigid registration of di ffusion weighted MRI using progressive principal component registration (PPCR). In ISMRM, number 3097, 2008.

2007

[1] A. Melbourne, D. Atkinson, M. J. White, D. Collins, M. Leach, and D. Hawkes. Registration of dynamic contrast-enhanced mri using a progressive principal component registration (PPCR). Phys Med Biol, 52(17):5147{5156, Sep 2007.
[2] A. Melbourne, D. Atkinson, M. J. White, D. J. Collins, M. O. Leach, and D. J. Hawkes. Registration of dynamic contrast enhanced MRI using a progressive principal component registration (ppcr). In ISMRM, number 522, 2007.
[3] A. Melbourne, D. Atkinson, M. J. White, D. J. Collins, M. O. Leach, and D. J. Hawkes. Using registration to quantify the consistency of whole liver position during patient breath-hold in dynamic contrast-enhanced MRI. In ISMRM, number 3709, 2007.