ISMRM 24th Annual Meeting & Exhibition • 07-13 May 2016 • Singapore

Scientific Session: Diffusion Weighted Image Analysis

Thursday, May 12, 2016
Summit 1
16:00 - 18:00
Moderators: Mara Cercignani, Jelle Veraart

Diffusion parameter EStImation with Gibbs and NoisE Removal (DESIGNER)
Benjamin Ades-Aron1, Jelle Veraart1,2, Elias Kellner3, Yvonne W. Lui1, Dmitry S. Novikov1, and Els Fieremans1
1Center for Biomedical Imaging, New York University School of Medicine, New York, NY, United States, 2iMinds Vision Lab, University of Anterp, Antwerp, Belgium, 3Department of Radiology, University Medical Center Freiburg, Freiburg, Germany
We propose a new pipeline (DESIGNER) for diffusion image processing that includes Marchenko Pastur denoising and Gibbs artifact removal, and thereby improves the precision and accuracy of the diffusion tensor and kurtosis tensor parameter estimation. In particular, our results show no notorious black voxels on kurtosis maps, while the original resolution is maintained in contrast to state-of-the-art processing methods that apply smoothing. 

HIgh B-value and high Resolution Integrated Diffusion (HIBRID) Imaging
Qiuyun Fan1, Aapo Nummenmaa1, Jonathan R. Polimeni1, Thomas Witzel1, Susie Y. Huang1, Van J. Wedeen1, Bruce R. Rosen1,2, and Lawrence L. Wald1,2
1Massachusetts General Hospital, Boston, MA, United States, 2Harvard-MIT Division of Health Sciences and Technology, MIT, Cambridge, MA, United States
The cerebral cortex is rich in gyral folding. Axonal fibers take sharp turns when bending into the cortex.  High resolution diffusion MRI is needed to characterize cortical structures in finer scale, while high b-value is desired to resolve complex white matter structures. We examined the impact of imaging resolution on characterizing the radial diffusion pattern in cortex, and proposed to improve the HIgh B-value and high Resolution Integrated Diffusion (HIBRID) imaging by incorporating information about each voxel’s proximity to the cortex. The combined data demonstrated the desired features from both high resolution and high b-value diffusion imaging. 

Harmonizing diffusion MRI data from multiple scanners
Hengameh Mirzaalian1, Lipeng Ning1, Peter Savadjiev1, Ofer Pasternak1, Sylvain Bouix1, Oleg Michailovich2, Marek Kubicki1, Carl Fredrik Westin1, Martha E. Shenton1, and Yogesh Rathi1
1Harvard Medical School and Brigham and Women’s Hospital, Boston, USA., Boston, MA, United States, 2University of Waterloo, Toronto, ON, Canada
Diffusion MRI (dMRI) is increasing being used to study neuropsychiatric brain disorders. To increase sample size and statistical power of neuroscience studies, we need to aggregate data from multiple sites1. However this is a challenging problem due to the presence of inter-site variability in the signal originating from several sources, e.g. number of head coils and their sensitivity, non-linearity in the imaging gradient, and other scanner related parameters2.  Prior works have addressed this issue either using meta analysis3, or by adding a statistical covariate4, which are not model free and may produce erroneous results.

Free water elimination using a bi-tensor model improves test-retest reproducibility of diffusion tensor imaging indices in the brain:  a longitudinal multisite reliability study of healthy elderly subjects
Angela Albi1, Ofer Pasternak2, Ludovico Minati1,3, Moira Marizzoni4, Giovanni Frisoni4,5, David Bartrés-Faz6, Núria Bargalló7, Beatriz Bosch8, Paolo Maria Rossini9,10, Camillo Marra11, Bernhard Müller12, Ute Fiedler12, Jens Wiltfang12,13, Luca Roccatagliata14,15, Agnese Picco16, Flavio Mariano Nobili16, Oliver Blin17, Julien Sein18, Jean-Philippe Ranjeva18, Mira Didic19,20, Stephanie Bombois21, Renaud Lopes21, Régis Bordet21, Hélène Gros-Dagnac22,23, Pierre Payoux22,23, Giada Zoccatelli24, Franco Alessandrini24, Alberto Beltramello24, Antonio Ferretti25,26, Massimo Caulo25,26, Marco Aiello27, Carlo Cavaliere27, Andrea Soricelli27,28, Lucilla Parnetti29, Roberto Tarducci30, Piero Floridi31, Magda Tsolaki32, Manos Constantinidis33, Antonios Drevelegas34, and Jorge Jovicich1
1Center for Mind/Brain Sciences (CIMEC), University of Trento, Rovereto, Trento, Rovereto (Trento), Italy, 2Departments of Psychiatry and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, Boston, MA, United States, 3Scientific Department, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy, Milan, Italy, 4LENITEM Laboratory of Epidemiology, Neuroimaging, & Telemedicine — IRCCS San Giovanni di Dio-FBF, Brescia, Italy, Brescia, Italy, 5Memory Clinic and LANVIE, Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland, Geneva, Switzerland, 6Department of Psychiatry and Clinical Psychobiology, Universitat de Barcelona and IDIBAPS, Barcelona, Spain, Barcelona, Spain, 7Department of Neuroradiology and Magnetic Resonance Image core Facility, Hospital Clínic de Barcelona, IDIBAPS, Barcelona, Spain, Barcelona, Spain, 8Alzheimer's Disease and Other Cognitive Disorders Unit, Department of Neurology, Hospital Clínic, and IDIBAPS, Barcelona, Spain, Barcelona, Spain, 9Deptartment Geriatrics, Neuroscience & Orthopaedics, Catholic University, Policlinic Gemelli, Rome, Italy, Rome, Italy, 10IRCSS S.Raffaele Pisana, Rome, Italy, Rome, Italy,11Center for Neuropsychological Research, Catholic University, Rome, Italy, Rome, Italy, 12LVR-Clinic for Psychiatry and Psychotherapy, Institutes and Clinics of the University Duisburg-Essen, Essen, Germany, Essen, Germany, 13Department of Psychiatry and Psychotherapy, University Medical Center (UMG), Georg August University, Göttingen, Germany, Göttingen, Germany, 14Department of Neuroradiology, IRCSS San Martino University Hospital and IST, Genoa, Italy, Genoa, Italy, 15Department of Health Sciences, University of Genoa, Genoa, Italy, Genoa, Italy, 16Department of Neuroscience, Ophthalmology, Genetics and Mother–Child Health (DINOGMI), University of Genoa, Genoa, Italy, Genoa, Italy, 17Pharmacology, Assistance Publique — Hôpitaux de Marseille, Aix-Marseille University — CNRS, UMR 7289, Marseille, France, Marseille, France, 18CRMBM–CEMEREM, UMR 7339, Aix Marseille Université — CNRS, Marseille, France, Marseille, France, 19APHM, CHU Timone, Service de Neurologie et Neuropsychologie, Marseille, France, Marseille, France, 20Aix Marseille Université, Inserm, INS UMR_S 1106, 13005, Marseille, France, Marseille, Italy, 21Université de Lille, Inserm, CHU Lille, U1171 - Degenerative and vascular cognitive disorders, F-59000 Lille, France, Lille, France, 22INSERM, Imagerie cérébrale et handicaps neurologiques, UMR 825, Toulouse, France, Toulouse, France, 23Université de Toulouse, UPS, Imagerie cérébrale et handicaps neurologiques, UMR 825, CHU Purpan, Place du Dr Baylac, Toulouse Cedex 9, France, Toulouse, France, 24Department of Neuroradiology, General Hospital, Verona, Italy, Verona, Italy, 25Department of Neuroscience Imaging and Clinical Sciences, University “G. d'Annunzio” of Chieti, Italy, Chieti, Italy, 26Institute for Advanced Biomedical Technologies (ITAB), University “G. d'Annunzio” of Chieti, Italy, Chieti, Italy,27IRCCS SDN, Naples, Italy, Naples, Italy, 28University of Naples Parthenope, Naples, Italy, Naples, Italy, 29Section of Neurology, Centre for Memory Disturbances, University of Perugia, Perugia, Italy, Perugia, Italy,30Medical Physics Unit, Perugia General Hospital, Perugia, Italy, Perugia, Italy, 31Neuroradiology Unit, Perugia General Hospital, Perugia, Italy, Perugia, Italy, 323rd Department of Neurology, Aristotle University of Thessaloniki, Thessaloniki, Greece, Thessaloniki, Greece, 33Interbalkan Medical Center of Thessaloniki, Thessaloniki, Greece, Thessaloniki, Greece, 34Interbalkan Medical Center of Thessaloniki, Thessaloniki, Greece Department of Radiology, Aristotle University of Thessaloniki, Thessaloniki, Greece, Thessaloniki, Greece
Brain diffusion tensor imaging (DTI) provides in-vivo characterization of white matter tissue microstructure. In this study we demonstrate that free water elimination in brain diffusion MRI significantly improves the test-retest reproducibility of DTI metrics (fractional anisotropy, axial, radial and mean diffusivity) in a multsite 3T setting. This work has important clinical applications since the improved reliability may provide increased sensitivity in longitudinal studies quantifying white matter neurophysiological processes related to disease stage/progression and treatment responses.

Robust DKI parameter estimation in case of CSF partial volume effects
Quinten Collier1, Arnold Jan den Dekker1,2, Ben Jeurissen1, and Jan Sijbers1
1iMinds Vision Lab, University of Antwerp, Antwerp, Belgium, 2Delft Center for Systems and Control, Delft University of Technology, Delft, Netherlands
Diffusion kurtosis imaging (DKI) suffers from partial volume effects caused by cerebrospinal fluid (CSF). We propose a DKI+CSF model combined with a framework to robustly estimate the DKI parameters. Since the estimation problem is ill-conditioned, a Bayesian estimation approach with a shrinkage prior is incorporated. Both simulation and real data experiments suggest that the use of this prior leads to a more accurate, precise and robust estimation of the DKI+CSF model parameters. Finally, we show that not correcting for the CSF compartment can lead to severe biases in the parameter estimations.

Low Rank plus Sparse Decomposition of ODF Distributions for Improved Detection of Group Differences in Diffusion Spectrum Imaging
Steven H. Baete1,2, Jingyun Chen1,2,3, Ricardo Otazo1,2, and Fernando E. Boada1,2
1Center for Advanced Imaging Innovation and Research (CAI2R), NYU School of Medicine, New York, NY, United States, 2Center for Biomedical Imaging, Dept of Radiology, NYU School of Medicine, New York, NY, United States, 3Steven and Alexandra Cohen Veterans Center for Posttraumatic Stress and Traumatic Brain Injury, Dept of Psychiatry, NYU School of Medicine, New York, NY, United States
Recent advances in data acquisition make it possible to use Diffusion Spectrum Imaging (DSI) as a clinical tool for in vivo study of white matter architecture. The dimensionality of DSI data sets requires a more robust methodology for their statistical analyses than currently available. Here we propose a combination of Low-Rank plus Sparse (L+S) matrix decomposition and Principal Component Analysis to reliably detect voxelwise group differences in the Orientation Distribution Function that are robust against the effects of noise and outliers. We demonstrate the performance of this approach using simulations to assess group differences between known ODF distributions.

Investigating the effects of intrinsic diffusivity on neurite orientation dispersion and density imaging (NODDI)
Jose M Guerrero1, Nagesh Adluru2, Steven R Kecskemeti2, Richard J Davidson3, and Andrew L Alexander1
1Medical Physics, University of Wisconsin - Madison, Madison, WI, United States, 2Waisman Center, University of Wisconsin - Madison, Madison, WI, United States, 3Psychology and Psychiatry, University of Wisconsin - Madison, Madison, WI, United States
NODDI model and its widely used estimation toolbox assume the intracellular (or intrinsic) diffusivity (ID) to a fixed value suitable for healthy adult brains. For broader applicability of the model in neurological diseases it is important to understand the effects of ID. Using multi-shell diffusion data we investigated the variability of estimated NODDI indices as well as the model residuals with respect to variations in ID. Our results suggest that the value for ID cannot simply be set to that offering the least residual since there are appreciable effects on the indices even in a small range of ID values.

Denoising  of diffusion MRI data using Random Matrix Theory
Jelle Veraart1,2, Dmitry S. Novikov2, Jan Sijbers1, and Els Fieremans2
1iMinds Vision Lab, University of Antwerp, Antwerp, Belgium, 2Center for Biomedical Imaging, New York University School of Medicine, New York, NY, United States
We here adopt the idea of noise removal by means of transforming redundant data into the Principal Component Analysis (PCA) domain and preserving only the components that contribute to the signal  to denoise diffusion MRI (dMRI) data. We objectify the threshold on the PCA eigenvalues for denoising by exploiting the fact that the noise-only eigenvalues are expected to obey the universal Marchenko-Pastur (MP) distribution. By doing so, we design a selective denoising technique that reduces signal fluctuations solely rooting in thermal noise, not in fine anatomical details.

A systematic comparative study of DTI and higher order diffusion models in brain fixed tissue
Elizabeth B Hutchinson1, Alexandru Avram1, Michal Komlosh1, M Okan Irfanoglu1, Alan Barnett1, Evren Ozarslan2, Susan Schwerin3, Kryslaine Radomski3, Sharon Juliano3, and Carlo Pierpaoli1
1SQITS, NICHD/NIH, Bethesda, MD, United States, 2Bogazici University, Istanbul, Turkey, 3APG, USUHS, Bethesda, MD, United States
We have systematically compared four diffusion MRI models – DTI, DKI, MAP-MRI and NODDI – in the same DWI data sets for fixed brain tissue to identify the relative strengths of these approaches and characterize the effects of experimental design and image quality on the generated metrics.  Metric-specific advantages in sensitivity and specificity were shown as well as differential vulnerability across the metrics to DWI sampling scheme and noise.  The intention of this work is to provide an integrative view of diffusion metrics that contributes to their utility in brain research.

A caveat to Bayesian estimation in intravoxel incoherent motion modelling
Peter T. While1, Igor Vidic2, and Pål E. Goa2
1Department of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway, 2Department of Physics, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
Intravoxel incoherent motion (IVIM) modelling has the potential to provide pixel-wise maps of pseudo-diffusion parameters that offer insight into tissue microvasculature. However, standard approaches using least-squares fitting yield parameter maps that are typically heavily corrupted by noise. Bayesian modelling has been shown recently to be a promising alternative. In this work we test the robustness of one such Bayesian approach by applying it to simulated noisy data, and obtain clearer parameter maps with much lower estimation uncertainty than least-squares fitting. However, certain features are found to disappear completely, indicating that a level of caution is required when implementing such techniques.

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