I am Director of the Centre for Medical Image Computing (CMIC) and one of the deputy heads of the Computer Science (CS) Department at UCL. I lead the MIG (Microstructure Imaging Group) and the POND (Progression of Neurological Diseases) group. Here is a one-page biography.

My background is in mathematics and computer science. My expertise is in computational modelling, pattern recognition, and machine learning for biomedical imaging and data analysis. My research aims to add understanding and capability in biomedicine by drawing on ideas from medical imaging, computer vision, data science, and machine learning. Much of my work focusses on imaging and the fusion of information from imaging with other data types (imaging + X).

The list below contains a selection of popular and recent publications. You can find a more complete list in UCL's research publications system or in my Google publication profile.

Microstructure imaging for the brain

A generative model of realistic brain cells with application to numerical simulation of the diffusion-weighted MR signal.
Palombo, M., Alexander, D. C., & Zhang, H. NeuroImage, 188, 391-402, 2019.
Construction of realistic virtual brain-tissue environments supporting computational models for microstructure imaging.

Microstructural imaging of the human brain with a super-scanner: 10 key advantages of ultra-strong gradients for diffusion MRI
Jones DK, Alexander DC, Bowtell R, Cercignani M, Dell'Acqua F, McHugh DJ, Miller KL, Palombo M, Parker GJM, Rudrapatna US, Tax CMW
Neuroimage, Vol. 182, pp. 8-28, 2018
Review of the potential of the high-gradient Connectom scanner.

Imaging brain microstructure with diffusion MRI: Practicality and applications
Alexander DC, Dyrby TB, Nilsson M, Zhang H
NMR in Biomedicine, e3841, 2017.
Review of microstructure imaging of the brain.

Multi-compartment microscopic diffusion imaging.
E. Kaden, N.D. Kelm, R.P. Carson, M.D. Does, and D.C. Alexander.
Neuroimage, Vol 139, pp. 346-359, 2016.
Uses the spherical mean technique (SMT) to construct a NODDI-like model providing estimates of neurite density and orientation dispersion, but without the need to fix diffusivity parameters and avoiding a simplistic extra-cellular diffusion model.

Bingham-NODDI: Mapping anisotropic orientation dispersion of neurites using diffusion MRI.
M. Tariq, T. Schneider, D.C. Alexander, C. A. M. Wheeler-Kingshott and H. Zhang.
Neuroimage (in press 2016).
Extension of NODDI to include dispersion anisotropy but retaining clinical viability.

White matter compartment models for in vivo diffusion MRI at 300mT/m. data.
U. Ferizi, T. Schneider, T. Witzel, L.L Wald, H. Zhang, C. A. M. Wheeler-Kingshott and D.C. Alexander.
Neuroimage, Vol 118 pp. 468-483, 2015.
Comparison of white matter diffusion MR models, c.f. Panagiotaki et al Neuroimage 2012 below, but using in-vivo human multi-shell diffusion MRI data uniquely at 300mT/m using the specialised Connectom scanner.

Contrast and stability of the axon diameter index from microstructure imaging with diffusion MRI
T.B. Dyrby, L.V. Sogaard, M.G. Hall, M. Ptito, and D.C. Alexander.
Magnetic Resonance in Medicine, Vol. 70, pp. 711-721, 2013.
Highlights experimentally the strong influence of gradient strength on axon diameter estimation.

NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain.
H. Zhang, T. Schneider, C. A. M. Wheeler-Kingshott and D.C. Alexander.
NeuroImage, Vol. 61, pp. 1000-1016, 2012.
Clinically viable brain microstructure imaging technique that separates FA into its three key contributing features: dispersion of fibre orientation; axon density; partial volume with CSF. Individual maps of each feature come from a two-shell HARDI acquisition.

Compartment models of the diffusion MR signal in brain white matter: A taxonomy and comparison
E. Panagiotaki, T. Schneider, B. Siow, M. G. Hall, M. F. Lythgoe, and D.C. Alexander.
NeuroImage, 59(3), 2241-2254, 2012.
Presents a taxonomy of diffusion models for white matter and uses extensive sampling of the pulsed-gradient spin-echo measurement space to compare the models computationally.

Orientationally invariant indices of axon diameter and density from diffusion MRI
D.C. Alexander, P.L. Hubbard, M.G. Hall, E.A. Moore, M. Ptito, G.J.M. Parker and T.D. Dyrby
NeuroImage, 52 (4), 1374-1389, 2010.
Introduces the ActiveAx technique and demonstrates, for the first time, mapping an index of axon diameter over coherent white matter regions in fixed and live brain.

Cancer imaging

Simplified Luminal Water Imaging for the Detection of Prostate Cancer From Multiecho T2 MR Images
W. Devine, F. Giganti, E.W. Johnston, H.S. Sidhu, E. Panagiotaki, S. Punwani, D.C. Alexander, D. Atkinson
Journal of Magnetic Resonance Imaging, 2018.
Clinically feasible protocol for estimating T2 components sensitive to prostate cancer.

Microstructure characterization of Bone Metastases from Prostate cancer with Diffusion MRI: Preliminary Findings.
Bailey C, Collins DJ, Tunariu N, Orton MR, Morgan VA, Feiweier T, Hawkes DJ, Leach MO, Alexander DC, Panagiotaki E
Frontiers in Oncology, 8, ARTN 26.
VERDICT in bone metastases.

Microstructural characterisation of normal and malignant human prostate tissue with VERDICT MRI.
E. Panagiotaki, R.W. Chan, N. Dikaios, J. O'Callaghan, A. Freeman, D. Atkinson, S. Punwani, D.J. Hawkes, and D.C Alexander
Investigative Radiology, Vol 50, pp 218-227, 2015
Preliminary translation of VERDICT to human prostate cancer imaging.

Noninvasive quantification of solid tumor microstructure using VERDICT MRI.
E. Panagiotaki, S. Walker-Samuel, B. Siow, S. P. Johnson, V. Rajkumar, R. B. Pedley, M. F. Lythgoe, D. C. Alexander
Cancer Research, Vol. 74, pp. 1902-1912, 2014.
Introduces VERDICT, which is a framework for diffusion MRI microstructure imaging in solid cancer tumours. Results show strong agreement with histology in estimating microscopic feature of xenograft models in mice, such as cell size and density and vascular volume fraction.

Placenta imaging

Placenta microstructure and microcirculation imaging with diffusion MRI.
Slator, P., Hutter, J., McCabe, L., Dos Santos Gomes, A., Price, A. N., Panagiotaki, E., Rutherford, M.A., Hajnal, J.V., Alexander, D. C.
Magnetic Resonance in Medicine, vol. 80, pp. 756-766, 2018.
Models for microstructure imaging of the placenta using diffusion MRI.

Disease progression modelling

Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference
Young AL, Marinescu RV, Oxtoby NP, Bocchetta M, Yong K, Firth NC, Cash DM, Thomas DL, Dick KM, Cardoso J, van Swieten J, Borroni B, Galimberti D, Masellis M, Tartaglia MC, Rowe JB, Graff C, Tagliavini F, Frisoni GB, Laforce R, Finger E, de Mendonca A, Sorbi S, Warren JD, Crutch S, Fox NC, Ourselin S, Schott JM, Rohrer JD, Alexander DC
Nature Communications, Vol. 9, 4273, 2018.
Presents the SuStaIn algorithm for data-driven discovery of disease subtypes and demonstrates using cross-sectional imaging data sets from Alzheimer's and fronto-temporal dementia cohorts. See press coverage in AlzForum and the i-Weekend.

Data-driven models of dominantly-inherited Alzheimer's disease progression
NP Oxtoby, AL Young, DM Cash, TLS Benzinger, AM Fagan, JC Morris, RJ Bateman, NC Fox, JM Schott, DC Alexander
Brain, Vol. 141, pp. 1529-1544.
Progression models of dominantly inhereted Alzheimer's disease, which highlight similarities and differences with sporadic AD by comparison with Young et al Brain 2014 below. See also the editorial by Li and Donohue.

Progression of regional grey matter atrophy in multiple sclerosis
Eshaghi A, Marinescu RV, Young AL, Firth NC, Prados F, Jorge Cardoso M, Tur C, De Angelis F, Cawley N, Brownlee WJ, De Stefano N, Laura Stromillo M, Battaglini M, Ruggieri S, Gasperini C, Filippi M, Rocca MA, Rovira A, Sastre-Garriga J, Geurts JJG, Vrenken H, Wottschel V, Leurs CE, Uitdehaag B, Pirpamer L, Enzinger C, Ourselin S, Gandini Wheeler-Kingshott CA, Chard D, Thompson AJ, Barkhof F, Alexander DC, Ciccarelli O.
Brain, Vol. 141, pp. 1665-1677, 2018.
Atrophy progression patterns in multiple sclerosis.

An image-based model of brain volume biomarker changes in Huntington's disease
P.A. Wijeratne, A.L. Young, N.P. Oxtoby, R.V. Marinescu, N.C. Firth, E.B. Johnson, A. Mohan, C. Sampaio, R.I. Scahill, S.J. Tabrizi, D.C. Alexander
Annals of Clinical and Translational Neurology, Vol. 5, pp. 570-582, 2018.
Disease progression models of Huntington's disease from the TRACK-HD imaging data set.

A data driven model of biomarker changes in sporadic Alzheimer's disease.
A.L. Young, N.P. Oxtoby, P. Daga, D.M. Cash, S. Ourselin, N.C. Fox, J.M. Schott and D.C. Alexander
Brain, Vol. 137, pp. 2564-2577, 2018.
Successful adaptation and application of the event-based model to sporadic disease (the ADNI data set) showing broad support for hypothetical models, but differences between the whole population and purer AD cohorts, such as APOE positives.

An event-based disease progression model and its application to familial Alzheimer's Disease and Huntington's disease
H.M. Fonteijn, M.J. Clarkson, M. Modat, J. Barnes, M. Lehmann, N.Z. Hobbs, R.I. Scahill, S.J. Tabrizi, S. Ourselin, N.C. Fox and D.C. Alexander
NeuroImage, Vol. 60, pp. 1880-1889, 2012.
Some improvements to the original event-based model formulation presented at IPMI 2011. Experimental results for Huntington's disease as well as familial Alzheimer's disease.

Image quality transfer

Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution
Ryutaro Tanno, Daniel E. Worrall, Aurobrata Ghosh, Enrico Kaden, Stamatios N. Sotiropoulos, Antonio Criminisi, Daniel C. Alexander
MICCAI 2017, pp. 611-619.
Image quality transfer using deep learning together with uncertainty estimation.

Image quality transfer and applications in diffusion MRI.
Alexander DC, Zikic D, Ghosh A, Tanno R, Wottschel V, Zhang J, Kaden E, Dyrby TB, Sotiropoulos SN, Zhang H, Criminisi A
Neuroimage, Vol.152, pp. 283-298, 2017.
Introduces the concept of image quality transfer and demonstrates in various ways using diffusion MRI data from the HCP.

Basic MR signal modelling

Model-based estimation of microscopic anisotropy using diffusion MRI: a simulation study
A. Ianus, I. Drobnjak, and D.C. Alexander.
NMR in Biomedicine, Vol. 29, pp. 672-685, 2016
Compares the ability of single and double diffusion encodings to recover the size and orientation distributions of anisotropic pores simultaneously. SDE can recover both parameters, but DDE provides greater sensitivity.

PGSE, OGSE, and sensitivity to axon diameter in diffusion MRI: Insight from a simulation study.
I. Drobnjak, H. Zhang, A. Ianus, E. Kaden, and D.C. Alexander.
Magnetic Resonance in Medicine, Vol. 75, pp. 688-700, 2016
Shows that the benefit of OGSE over PGSE in measuring compartment sizes comes only when there is uncertainty in pore orientation.

High angular resolution diffusion imaging with stimulated echoes: compensation and correction in experiment design and analysis
H. Lundell, D.C. Alexander and T.B. Dyrby.
NMR in Biomedicine, Vol. 27, pp. 918-925, 2014
Corrections to the acquisition protocol required for high angular resolution diffusion imaging with STEAM (stimulated echo).

Viable and fixed white matter: diffusion magnetic resonance comparisons and contrasts at physiological temperature.
S. Richardson, B. Siow, E. Panagiotaki, T. Schneider, M.F. Lythgoe, and D.C. Alexander.
Magnetic Resonance in Medicine, Vol. 72, pp. 1151-1161, 2014.
Comparison of diffusion MR signals and model parameters from fixed and viable (ie excised but kept in in-vivo state) tissue at corresponding temperatures. It shows that fixation does make a differences to key parameter estimates, although the models that best explain the data are consistent.

The matrix formalism for generalised gradients with time-varying orientation in diffusion NMR
I. Drobnjak, H. Zhang, M.G. Hall and D.C. Alexander.
Journal of Magnetic Resonance, Vol. 210, pp. 151-157, 2011.
Extends the matrix formalism to 3D to model diffusion NMR signals from arbitrary gradient waveforms. Now available open-source in the MISST software.

Convergence and parameter choice for Monte-Carlo simulations of diffusion MRI.
M.G. Hall and D.C. Alexander
IEEE Transactions on Medical Imaging, Vol. 28, pp. 1354-1364, 2009.
Describes the diffusion simulation system implemented in Camino.

Protocol and pulse-sequence design

Double oscillating diffusion encoding and sensitivity to microscopic anisotropy
A. Ianus, N. Shemesh, D.C. Alexander, and I. Drobnjak
Magnetic Resonance in Medicine, 2017.
Introduces the double oscillating diffusion encoding sequence and evaluates its advantages over standard double diffusion encoding in recovering microscopic pore anisotropy.

Optimizing gradient waveforms for microstructure sensitivity in diffusion-weighted MR
I. Drobnjak, B. Siow, D.C. Alexander
Journal of Magnetic Resonance, 206, 41-51, 2010.
Optimization algorithm to identify the best gradient waveforms for measuring pore sizes, such as axon diameter, using diffusion MRI. Square wave gradient blocks consistently emerge.

A general framework for experiment design in diffusion MRI and its application in measuring direct tissue-microstructure features.
D.C. Alexander
Magnetic Resonance in Medicine, Vol. 60, pp. 439-448, 2008.
Outlines the active-imaging optimization framework for tuning diffusion MRI protocols for specific microstructural parameters.

Optimal acquisition schemes for in-vivo quantitative magnetization transfer MRI.
M. Cercignani and D.C. Alexander
Magnetic Resonance in Medicine, Vol. 56, pp. 803--810, 2006.
Demonstrates significant improvement in quantitative MT parameter estimation after experiment design optimization.

Optimal imaging parameters for fibre-orientation estimation in diffusion MRI
D.C. Alexander and G.J. Barker
NeuroImage, Vol. 27, pp. 357--367, 2005.
Studies the dependence of accuracy and precision in diffusion tensor fractional anisotropy, mean diffusivity and fibre orientation estimate on the choice of acquisition parameters, such as b-value, number of gradient directions and number of b=0 images.

HARDI, Multiple fiber reconstruction, and tractography

Multiple fibres: beyond the diffusion tensor
K.K. Seunarine and D.C. Alexander
In H. Johansen-Berg and T.E.J. Behrens (Eds) Diffusion MRI: from quantitative measurement to in vivo neuroanatomy. pp 56-74. Academic Press. 2009.
Review of models and algorithms for recovering multiple fibre directions from high-angular resolution diffusion MRI data. Email me for a pdf.

Probabilistic anatomic connectivity derived from the microscopic persistent angular structure of cerebral tissue.
G.J.M. Parker and D.C. Alexander
Philosophical Transactions of the Royal Society B. Vol. 360, pp. 893--902, 2005.
Outlines a technique for connectivity mapping using PAS-MRI.

Maximum entropy spherical deconvolution for diffusion MRI
D.C. Alexander.
Proc. Information processing in medical imaging, 2005.
Demonstrates equivalence of the PAS-MRI algorithm with spherical deconvolution and exploits the relationship to develop a hybrid spherical deconvolution algorithm with natural constraints on the fibre orientation distribution.

Multiple-fibre reconstruction algorithms for diffusion MRI
D.C. Alexander.
Annals of the New York Academy of Sciences, Vol 1046, pp. 113--133, 2005.
Review of HARDI (high angular resolution diffusion imaging) or multiple-fibre reconstruction algorithms and compares some standard algorithms in simulation.

Persistent angular structure: new insights from diffusion magnetic resonance imaging data
K.M. Jansons and D.C. Alexander.
Inverse Problems, Vol. 19, pp. 1031-1046, 2003.
Introduces the PAS-MRI algorithm for reconstructing the distribution of fibre orientations without limitations on the number of fibre populations.

Probabilistic Monte Carlo Based Mapping of Cerebral Connections Utilising Whole-Brain Crossing Fibre Information
G.J.M. Parker and D.C. Alexander.
Proc. IPMI 2003.
Early tractography algorithm exploiting multiple-fibre reconstructions in each image voxel and demonstrating improvements.

Detection and modeling of non-Gaussian apparent diffusion coefficient profiles in human brain data
D.C. Alexander, G.J. Barker and S.R. Arridge.
Magnetic Resonance in Medicine, Vol. 48, pp. 331-340, 2002.
Early demonstration of the detectability of crossing fibres in standard diffusion MRI data sets from live humans.

General Diffusion MRI

An introduction to computational diffusion MRI: the diffusion tensor and beyond
D.C. Alexander.
Chapter in "Visualization and image processing of tensor fields" editted by J.Weickert and H.Hagen, Springer 2006.
Provides an overview of the diffusion MRI measurement and a variety of models and reconstruction algorithms.

Camino: Open-Source Diffusion-MRI Reconstruction and Processing
P.A. Cook, Y. Bai, S. Nedjati-Gilani, K.K. Seunarine, M.G. Hall, G.J.M. Parker and D.C. Alexander.
Proc. 14th ISMRM, Seattle, WA, USA, p. 2759, May 2006.
Abstract introducing and outlining the Camino toolkit.

Shape Modelling

Interactive Lesion Segmentation with Shape Priors from Off-line and On-line Learning.
T. Shepherd, S. J. D. Prince, and D.C. Alexander.
IEEE Trans. Medical Imaging. In press, 2012.
Presents shape models based on Gaussian Processes that do not require explicit landmarks and demonstrates their benefits for modelling and segmenting shapes in biomedical images, such as tumours and lesions.

Image registration and warping

Deformable registration of diffusion tensor MR images with explicit orientation optimization.
H. Zhang, P.A. Yushkevich, D.C. Alexander, J.C. Gee.
Medical image analysis 10(5), 764-785, 2006.
Image registration technique for diffusion tensor images that exploits the orientational information for matching.

Spatial Transformations of Diffusion Tensor Magnetic Resonance Images
D.C. Alexander, C. Pierpaoli, P.J. Basser and J.C. Gee.
IEEE Transactions on Medical Imaging, Vol. 20, No. 11, pp. 1131-1139, November 2001.
Describes how to warp diffusion tensor images and preserve the orientational information.

Colour modelling

Statistical Modeling of Colour Data
D.C. Alexander and B.F. Buxton.
International Journal of Computer Vision 44 (2):87-109, September 2001
Introduces a variety of candidate models for the distribution of measured colours from single objects in simple lighting conditions and compares them in laboratory and natural daylight scenes.

Advances in Daylight Statistical Colour Modelling
D.C. Alexander.
Proc. IEEE conf. Computer Vision and Pattern Recognition 1999
Improved colour models for daylight scenes. Inclusion of an ambient lighting component improves performance.