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 Simplified Luminal Water
Imaging for the Detection of Prostate Cancer From Multiecho T2 MR
Images Microstructure
characterization of Bone Metastases from Prostate cancer with
Diffusion MRI: Preliminary Findings.
Microstructural characterisation of normal and malignant human prostate tissue with
VERDICT MRI.
Noninvasive quantification of solid tumor microstructure using VERDICT
MRI. Placenta microstructure
and microcirculation imaging with diffusion MRI. Uncovering the
heterogeneity and temporal complexity of neurodegenerative diseases
with Subtype and Stage Inference Data-driven models of
dominantly-inherited Alzheimer's disease progression Progression of
regional grey matter atrophy in multiple sclerosis An image-based model of
brain volume biomarker changes in Huntington's
disease A
data driven model of biomarker changes in sporadic Alzheimer's disease.
An
event-based disease progression model and its application to familial
Alzheimer's Disease and Huntington's disease
Bayesian Image Quality
Transfer with CNNs: Exploring Uncertainty in dMRI
Super-Resolution Image
quality transfer and applications in diffusion MRI. Model-based estimation
of microscopic anisotropy using diffusion MRI: a simulation study
PGSE, OGSE, and
sensitivity to axon diameter in diffusion MRI: Insight from a
simulation study. High angular resolution
diffusion imaging with stimulated echoes: compensation and correction
in experiment design and analysis Viable and fixed white
matter: diffusion magnetic resonance comparisons and contrasts at
physiological temperature. The matrix formalism
for generalised gradients with time-varying orientation in diffusion
NMR
Convergence and parameter choice for Monte-Carlo simulations of
diffusion MRI. Double oscillating
diffusion encoding and sensitivity to microscopic anisotropy
Optimizing gradient waveforms for microstructure sensitivity in
diffusion-weighted MR
A general framework for experiment design in diffusion MRI and its
application in measuring direct tissue-microstructure
features.
Optimal acquisition schemes for in-vivo quantitative magnetization
transfer MRI. Optimal
imaging parameters for fibre-orientation estimation in diffusion
MRI
Multiple fibres: beyond the diffusion tensor Probabilistic
anatomic connectivity derived from the microscopic persistent
angular structure of cerebral tissue.
Maximum entropy spherical deconvolution for diffusion MRI
Multiple-fibre reconstruction algorithms for diffusion MRI
Persistent angular structure: new insights from diffusion magnetic
resonance imaging data Probabilistic
Monte Carlo Based Mapping of Cerebral Connections Utilising
Whole-Brain Crossing Fibre Information
Detection and modeling of non-Gaussian apparent diffusion
coefficient profiles in human brain data
An introduction to computational diffusion MRI: the diffusion tensor
and beyond Camino:
Open-Source Diffusion-MRI Reconstruction and
Processing Interactive
Lesion Segmentation with Shape Priors from Off-line and On-line
Learning. Deformable
registration of diffusion tensor MR images with explicit orientation
optimization.
Spatial Transformations of Diffusion Tensor Magnetic Resonance Images Statistical
Modeling of Colour Data
Advances in Daylight Statistical
Colour Modelling
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
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.
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.
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.
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
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
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.
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.
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.
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.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.
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
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.
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
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.
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.
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).
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.
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.
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
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
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
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.
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
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
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.
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
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.
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.