Computational Diffusion MRI: International MICCAI Workshop, Granada, Spain, September 2018

376 Pages·2019·14.08 MB·English

by  Elisenda Bonet-Carne, Francesco Grussu, Lipeng Ning, Farshid Sepehrband, Chantal M. W. Tax

Mathematics and Visualization
Elisenda Bonet-Carne · Francesco Grussu ·
Lipeng Ning · Farshid Sepehrband ·
Chantal M. W. Tax Editors
Computational
Diffusion MRI
International MICCAI Workshop,
Granada, Spain, September 2018
Mathematics and Visualization
Series Editors
Hans-Christian Hege, Konrad-Zuse-Zentrum für Informationstechnik Berlin (ZIB),
Berlin, Germany
David Hoffman, Department of Mathematics, Stanford University, Stanford, CA,
USA
Christopher R. Johnson, Scientific Computing and Imaging Institute, Salt Lake
City, UT, USA
KonradPolthier, AGMathematical GeometryProcessing, Freie UniversitätBerlin,
Berlin, Germany
Martin Rumpf, Bonn, Germany
The series Mathematics and Visualization is intended to further the fruitful
relationship between mathematics and visualization. It covers applications of
visualization techniques in mathematics, as well as mathematical theory and
methodsthatareusedforvisualization.Inparticular,itemphasizesvisualizationin
geometry, topology, and dynamical systems; geometric algorithms; visualization
algorithms; visualization environments; computer aided geometric design; compu-
tational geometry; image processing; information visualization; and scientific
visualization.Threetypesofbookswillappearintheseries:researchmonographs,
graduate textbooks, and conference proceedings.
More information about this series at http://www.springer.com/series/4562
Elisenda Bonet-Carne Francesco Grussu
(cid:129) (cid:129)
Lipeng Ning Farshid Sepehrband
(cid:129) (cid:129)
Chantal M. W. Tax
Editors
Computational Diffusion
MRI
International MICCAI Workshop, Granada,
Spain, September 2018
123
Editors
ElisendaBonet-Carne Francesco Grussu
Centrefor Medical Image Computing Queen SquareInstitute of Neurologyand
University CollegeLondon Centrefor Medical Image Computing
London,UK University CollegeLondon
London,UK
LipengNing
Psychiatry Neuroimaging Laboratory, Farshid Sepehrband
Brigham andWomen’sHospital Laboratory of NeuroImaging (LONI)
Harvard Medical School StevensNeuroimaging andInformatics
Boston, MA, USA Institute, Keck Schoolof Medicine
University of SouthernCalifornia
ChantalM. W.Tax LosAngeles, CA, USA
Cardiff University BrainResearch Imaging
Centre(CUBRIC)
Cardiff University
Cardiff, UK
ISSN 1612-3786 ISSN 2197-666X (electronic)
Mathematics andVisualization
ISBN978-3-030-05830-2 ISBN978-3-030-05831-9 (eBook)
https://doi.org/10.1007/978-3-030-05831-9
LibraryofCongressControlNumber:2018964925
Mathematics Subject Classification (2010): 00B25, 00A66, 00A72, 42B35, 60J60, 60J65, 62P10,
65CXX,65DXX,65Z05,68R10,68T99,92BXX
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Programme Committee
Nagesh Adluru University of Wisconsin-Madison, USA
Manisha Aggarwal Johns Hopkins University, USA
Dogu Baran Aydogan University of Southern California, USA
Dafnis Batalle King's College London, UK
Sylvain Bouix Harvard Medical School, USA
Ryan Cabeen University of Southern California, USA
Emmanuel Caruyer IRISA, France
Mara Cercignani University of Sussex, UK
Suheyla Cetin Karayumak Harvard Medical School, USA
Jian Cheng National Institutes of Health, USA
Daan Christiaens King's College London, UK
Alessandro Daducci University of Verona, Italy
Tom Dela Haije University of Copenhagen, Denmark
Matt Hall University College London, UK; National Physical
Laboratory, Teddington, UK
Enrico Kaden University College London, UK
Jan Klein Fraunhofer MEVIS, Germany
Christophe Lenglet University of Minnesota, USA
Damien McHugh University of Manchester, UK
Dorit Merhof RWTH Aachen University, Germany
Marco Palombo University College London, UK
Torben Schneider Philips Healthcare, UK
Thomas Schultz University of Bonn, Germany
Filip Szczepankiewicz Harvard Medical School, Brigham and Women´s
Hospital, USA
Pew-Thian Yap University of North Carolina at Chapel Hill, USA
Fan Zhang Harvard Medical School, USA
v
Preface
It is our great pleasure to present the proceedings of the 2018 International
Workshop on Computational Diffusion MRI (CDMRI’18) and the main results
of the Multi-shell Diffusion MRI Harmonization and Enhancement Challenge
(MUSHAC).BothwereheldundertheauspicesoftheInternationalConferenceon
Medical Image Computing and Computer Assisted Intervention (MICCAI), which
took place in Granada, Spain, on 20 September 2018. CDMRI’18 and MUSHAC
were sponsored by MICCAI, Siemens Healthineers, NVIDIA and Restaurante
OliverGranadaandendorsedbytheInternationalSocietyfor Magnetic Resonance
in Medicine (ISMRM).
This volume presents the latest developments in the highly active and rapidly
growing field of diffusion MRI. The reader will find numerous contributions cov-
ering a broad range of topics, from the mathematical foundations of the diffusion
process and signal generation, to new computational methods and estimation
techniquesfor theinvivo recoveryofmicrostructural andconnectivity features, as
well as harmonization and frontline applications in research and clinical practice.
Thiseditionincludesinvitedchaptersfromhigh-profileresearcherswiththespecific
focusonfournewimportanttopicsthataregainingmomentumwithinthediffusion
MRI community: (i) diffusion MRI signal acquisition and processing strategies;
(ii) machine learning for diffusion MRI; (iii) diffusion MRI signal harmonization;
and(iv)diffusionMRIoutsidethebrainandclinicalapplications.Additionally,the
volume also includes contributions in the field of tractography and connectivity
mapping, which continue being relevant and popular topics across all editions of
CDMRI.
Thisvolumeofferstheopportunitytosharenewperspectivesonthemostrecent
research challenges for those currently working in the field, but also offering a
valuablestartingpointforanyoneinterestedinlearningcomputationaltechniquesin
diffusion MRI. The book includes rigorous mathematical derivations, a large
numberofrich,full-colourvisualisationsandclinicallyrelevantresults.Assuch,it
willbeofinteresttoresearchersandpractitionersinthefieldsofcomputerscience,
MRI physics and applied mathematics.
vii
viii Preface
Each contribution in this volume has been peer-reviewed by multiple members
oftheInternationalProgramCommittee.Wewouldliketoexpressourgratitudeto
allCDMRI’18authorsandreviewersforensuringthequalityofthepresentedwork
andtoalltheteamswhoparticipatedinMUSHAC.WearegratefultotheMICCAI
2018 chairs for providing a platform to present and discuss the work collected in
this volume, to our sponsors and to ISMRM for the endorsement. We also would
liketothanktheeditorsoftheSpringerbookseriesMathematicsandVisualization
aswellasMartinPetersandRuthAllewelt(Springer,Heidelberg)fortheirsupport
to publish this collection as part of their series.
Finally, we express our sincere congratulations to the winners of the prizes that
were awarded during CDMRI’18 for the first time this year. The prizes were
awardedfollowingcarefulevaluationbyapanelofjudges,madebytheCDMRI’18
andMUSHACorganisersandbytheCDMRI’18andMUSHACkeynotespeakers.
(cid:129) Prizeforthebestpaper:SimonKoppersetal.with“Sphericalharmonicresidual
network for diffusion signal harmonization”.
(cid:129) Prize for the best MUSHAC method: Jaume Coll-Font et al. with the
“DIAMOND2” method.
(cid:129) Prize for the best oral presentation: Daniel Moyer et al. with “Measures of
tractography convergence”.
(cid:129) Prize for the best poster presentation: Santiago Aja-Fernandez et al. with
“Return-to-plane probability calculation from single-shell acquisitions”.
(cid:129) Prize for the best MUSHAC team presentation: Suheyla Cetin-Karayuma et al.
with the “LinearRISH” method.
Granada, Spain Elisenda Bonet-Carne, Ph.D.
September 2018 Research Associate
Francesco Grussu, Ph.D.
Research Associate
Lipeng Ning, Ph.D.
Instructor
Farshid Sepehrband, Ph.D.
Assistant Professor
Chantal M. W. Tax, Ph.D.
Research Fellow
Contents
Part I Diffusion MRI Signal Acquisition and Processing Strategies
Towards Optimal Sampling in Diffusion MRI . . . . . . . . . . . . . . . . . . . . 3
Hans Knutsson
Joint Image Reconstruction and Phase Corruption Maps Estimation
in Multi-shot Echo Planar Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Iñaki Rabanillo, Santiago Sanz-Estébanez, Santiago Aja-Fernández,
Joseph Hajnal, Carlos Alberola-López and Lucilio Cordero-Grande
Return-to-Axis Probability Calculation from Single-Shell
Acquisitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Santiago Aja-Fernández, Antonio Tristán-Vega, Malwina Molendowska,
Tomasz Pieciak and Rodrigo de Luis-García
A Novel Spatial-Angular Domain Regularisation Approach
for Restoration of Diffusion MRI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
Alessandro Mella, Alessandro Daducci, Giandomenico Orlandi,
Jean-Philippe Thiran, Maria Deprez and Merixtell Bach Cuadra
Dmipy, A Diffusion Microstructure Imaging Toolbox in Python
to Improve Research Reproducibility. . . . . . . . . . . . . . . . . . . . . . . . . . . 55
Abib Alimi, Rutger Fick, Demian Wassermann and Rachid Deriche
TissueSegmentationUsingSparseNon-negativeMatrixFactorization
of Spherical Mean Diffusion MRI Data . . . . . . . . . . . . . . . . . . . . . . . . . 69
Peng Sun, Ye Wu, Geng Chen, Jun Wu, Dinggang Shen
and Pew-Thian Yap
A Closed-Form Solution of Rotation Invariant Spherical Harmonic
Features in Diffusion MRI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
Mauro Zucchelli, Samuel Deslauriers-Gauthier and Rachid Deriche
ix
x Contents
Orientation-Dispersed Apparent Axon Diameter via Multi-Stage
Spherical Mean Optimization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
Marco Pizzolato, Demian Wassermann, Rachid Deriche,
Jean-Philippe Thiran and Rutger Fick
Part II Machine Learning for Diffusion MRI
Current Applications and Future Promises of Machine Learning
in Diffusion MRI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
Daniele Ravi, NooshinGhavami,DanielC. Alexander andAndradaIanus
q-Space Learning with Synthesized Training Data. . . . . . . . . . . . . . . . . 123
Chuyang Ye, Yue Cui and Xiuli Li
Graph-Based Deep Learning for Prediction of Longitudinal Infant
Diffusion MRI Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
Jaeil Kim, Yoonmi Hong, Geng Chen, Weili Lin, Pew-Thian Yap
and Dinggang Shen
Supervised Classification of White Matter Fibers
Based on Neighborhood Fiber Orientation Distributions
Using an Ensemble of Neural Networks. . . . . . . . . . . . . . . . . . . . . . . . . 143
Devran Ugurlu, Zeynep Firat, Ugur Ture and Gozde Unal
Part III Diffusion MRI Signal Harmonisation
Challenges and Opportunities in dMRI Data Harmonization. . . . . . . . . 157
Alyssa H. Zhu, Daniel C. Moyer, Talia M. Nir, Paul M. Thompson
and Neda Jahanshad
Spherical Harmonic Residual Network for Diffusion Signal
Harmonization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
Simon Koppers, Luke Bloy, Jeffrey I. Berman, Chantal M. W. Tax,
J. Christopher Edgar and Dorit Merhof
Longitudinal Harmonization for Improving Tractography
in Baby Diffusion MRI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183
Khoi Minh Huynh, Jaeil Kim, Geng Chen, Ye Wu, Dinggang Shen
and Pew-Thian Yap
Inter-Scanner Harmonization of High Angular Resolution DW-MRI
Using Null Space Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193
Vishwesh Nath, Prasanna Parvathaneni, Colin B. Hansen,
Allison E. Hainline, Camilo Bermudez, Samuel Remedios,
JustinA.Blaber,KurtG.Schilling,IlwooLyu,VaibhavJanve,YuruiGao,
Iwona Stepniewska, Baxter P. Rogers, Allen T. Newton, L. Taylor Davis,
Jeff Luci, Adam W. Anderson and Bennett A. Landman