Benjamin Ades-Aron¹, Jelle Veraart^1,2, Elias Kellner³, Yvonne W. Lui¹, Dmitry S. Novikov¹, and Els Fieremans^1
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. 

Qiuyun Fan¹, Aapo Nummenmaa¹, Jonathan R. Polimeni¹, Thomas Witzel¹, Susie Y. Huang¹, Van J. Wedeen¹, Bruce R. Rosen^1,2, and Lawrence L. Wald^1,2
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. 

Hengameh Mirzaalian¹, Lipeng Ning¹, Peter Savadjiev¹, Ofer Pasternak¹, Sylvain Bouix¹, Oleg Michailovich², Marek Kubicki¹, Carl Fredrik Westin¹, Martha E. Shenton¹, and Yogesh Rathi^1
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.

Angela Albi¹, Ofer Pasternak², Ludovico Minati^1,3, Moira Marizzoni⁴, Giovanni Frisoni^4,5, David Bartrés-Faz⁶, Núria Bargalló⁷, Beatriz Bosch⁸, Paolo Maria Rossini^9,10, Camillo Marra¹¹, Bernhard Müller¹², Ute Fiedler¹², Jens Wiltfang^12,13, Luca Roccatagliata^14,15, Agnese Picco¹⁶, Flavio Mariano Nobili¹⁶, Oliver Blin¹⁷, Julien Sein¹⁸, Jean-Philippe Ranjeva¹⁸, Mira Didic^19,20, Stephanie Bombois²¹, Renaud Lopes²¹, Régis Bordet²¹, Hélène Gros-Dagnac^22,23, Pierre Payoux^22,23, Giada Zoccatelli²⁴, Franco Alessandrini²⁴, Alberto Beltramello²⁴, Antonio Ferretti^25,26, Massimo Caulo^25,26, Marco Aiello²⁷, Carlo Cavaliere²⁷, Andrea Soricelli^27,28, Lucilla Parnetti²⁹, Roberto Tarducci³⁰, Piero Floridi³¹, Magda Tsolaki³², Manos Constantinidis³³, Antonios Drevelegas³⁴, and Jorge Jovicich^1
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.

Quinten Collier¹, Arnold Jan den Dekker^1,2, Ben Jeurissen¹, and Jan Sijbers^1
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.

Steven H. Baete^1,2, Jingyun Chen^1,2,3, Ricardo Otazo^1,2, and Fernando E. Boada^1,2
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.

Jose M Guerrero¹, Nagesh Adluru², Steven R Kecskemeti², Richard J Davidson³, and Andrew L Alexander^1
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.

Jelle Veraart^1,2, Dmitry S. Novikov², Jan Sijbers¹, and Els Fieremans^2
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.

Elizabeth B Hutchinson¹, Alexandru Avram¹, Michal Komlosh¹, M Okan Irfanoglu¹, Alan Barnett¹, Evren Ozarslan², Susan Schwerin³, Kryslaine Radomski³, Sharon Juliano³, and Carlo Pierpaoli^1
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.

Peter T. While¹, Igor Vidic², and Pål E. Goa^2
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.