“MASSIVE” brain dataset: Multiple acquisitions for standardization of structural imaging validation and evaluation

In this work, we present the MASSIVE (Multiple Acquisitions for Standardization of Structural Imaging Validation and Evaluation) brain dataset of a single healthy subject, which is intended to facilitate diffusion MRI (dMRI) modeling and methodology development.

[1]  D. Ortendahl,et al.  Measuring signal-to-noise ratios in MR imaging. , 1989, Radiology.

[2]  P. Basser,et al.  Estimation of the effective self-diffusion tensor from the NMR spin echo. , 1994, Journal of magnetic resonance. Series B.

[3]  P. Basser,et al.  Analytical Expressions for the b Matrix in NMR Diffusion Imaging and Spectroscopy , 1994 .

[4]  P. Jezzard,et al.  Correction for geometric distortion in echo planar images from B0 field variations , 1995, Magnetic resonance in medicine.

[5]  P. Basser,et al.  The b matrix in diffusion tensor echo‐planar imaging , 1997, Magnetic resonance in medicine.

[6]  P. Batchelor,et al.  International Society for Magnetic Resonance in Medicine , 1997 .

[7]  M. Horsfield,et al.  Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging , 1999, Magnetic resonance in medicine.

[8]  R M Harrison,et al.  A comparison of two methods for measuring the signal to noise ratio on MR images , 1999, Physics in medicine and biology.

[9]  A. Anderson.  Theoretical analysis of the effects of noise on diffusion tensor imaging , 2001, Magnetic resonance in medicine.

[10]  Derek K. Jones,et al.  Diffusion‐tensor MRI: theory, experimental design and data analysis – a technical review , 2002 .

[11]  Stefan Skare,et al.  How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging , 2003, NeuroImage.

[12]  P. Sundgren,et al.  Diffusion tensor imaging of the brain: review of clinical applications , 2004, Neuroradiology.

[13]  Derek K. Jones,et al.  Determining and visualizing uncertainty in estimates of fiber orientation from diffusion tensor MRI , 2003, Magnetic resonance in medicine.

[14]  Derek K. Jones,et al.  The effect of gradient sampling schemes on measures derived from diffusion tensor MRI: A Monte Carlo study † , 2004, Magnetic resonance in medicine.

[15]  P. Basser,et al.  Comprehensive approach for correction of motion and distortion in diffusion‐weighted MRI , 2004, Magnetic resonance in medicine.

[16]  D. Tuch.  Q‐ball imaging , 2004, Magnetic resonance in medicine.

[17]  W. Heindel,et al.  Dehydration confounds the assessment of brain atrophy , 2005, Neurology.

[18]  J. Helpern,et al.  Diffusional kurtosis imaging: The quantification of non‐gaussian water diffusion by means of magnetic resonance imaging , 2005, Magnetic resonance in medicine.

[19]  S. Schoenberg,et al.  Practical approaches to the evaluation of signal‐to‐noise ratio performance with parallel imaging: Application with cardiac imaging and a 32‐channel cardiac coil , 2005, Magnetic resonance in medicine.

[20]  Yaniv Assaf,et al.  Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain , 2005, NeuroImage.

[21]  P. Hagmann,et al.  Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging , 2005, Magnetic resonance in medicine.

[22]  J Sijbers,et al.  Mathematical framework for simulating diffusion tensor MR neural fiber bundles , 2005, Magnetic resonance in medicine.

[23]  L. Concha,et al.  Diffusion tensor tractography of the limbic system. , 2005, AJNR. American journal of neuroradiology.

[24]  S. Wakana,et al.  Three-Dimensional Atlas of Brain White Matter Tracts , 2005 .

[25]  P. Szeszko,et al.  MRI atlas of human white matter , 2006 .

[26]  J. Helpern,et al.  Three‐dimensional characterization of non‐gaussian water diffusion in humans using diffusion kurtosis imaging , 2006, NMR in biomedicine.

[27]  Alan C. Evans,et al.  The NIH MRI study of normal brain development , 2006, NeuroImage.

[28]  Y. Assaf,et al.  Diffusion Tensor Imaging (DTI)-based White Matter Mapping in Brain Research: A Review , 2007, Journal of Molecular Neuroscience.

[29]  Alan Connelly,et al.  Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution , 2007, NeuroImage.

[30]  Giuseppe Scotti,et al.  A Model-Based Deconvolution Approach to Solve Fiber Crossing in Diffusion-Weighted MR Imaging , 2007, IEEE Transactions on Biomedical Engineering.

[31]  Daniel C Alexander,et al.  Optimal acquisition orders of diffusion‐weighted MRI measurements , 2007, Journal of magnetic resonance imaging : JMRI.

[32]  Jerry L Prince,et al.  Effects of signal‐to‐noise ratio on the accuracy and reproducibility of diffusion tensor imaging–derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5T , 2007, Journal of magnetic resonance imaging : JMRI.

[33]  Jerry L. Prince,et al.  Effects of diffusion weighting schemes on the reproducibility of DTI-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5T , 2007, NeuroImage.

[34]  S. Schoenberg,et al.  Measurement of signal‐to‐noise ratios in MR images: Influence of multichannel coils, parallel imaging, and reconstruction filters , 2007, Journal of magnetic resonance imaging : JMRI.

[35]  Li-Wei Kuo,et al.  Optimization of diffusion spectrum imaging and q-ball imaging on clinical MRI system , 2008, NeuroImage.

[36]  J. Mangin,et al.  New diffusion phantoms dedicated to the study and validation of high‐angular‐resolution diffusion imaging (HARDI) models , 2008, Magnetic resonance in medicine.

[37]  M. Miller,et al.  Correction of B0 susceptibility induced distortion in diffusion-weighted images using large-deformation diffeomorphic metric mapping. , 2008, Magnetic resonance imaging.

[38]  Carlo Pierpaoli,et al.  Comparison of EPI Distortion Correction Methods in Diffusion Tensor MRI Using a Novel Framework , 2008, MICCAI.

[39]  Alexander Leemans,et al.  The B‐matrix must be rotated when correcting for subject motion in DTI data , 2009, Magnetic resonance in medicine.

[40]  Alan Connelly,et al.  A software tool to generate simulated white matter structures for the assessment of fibre-tracking algorithms , 2009, NeuroImage.

[41]  Jan Sijbers,et al.  ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data , 2009 .

[42]  Mara Cercignani,et al.  Twenty‐five pitfalls in the analysis of diffusion MRI data , 2010, NMR in biomedicine.

[43]  Giuseppe Scotti,et al.  A modified damped Richardson–Lucy algorithm to reduce isotropic background effects in spherical deconvolution , 2010, NeuroImage.

[44]  Maxime Descoteaux,et al.  Quantitative evaluation of 10 tractography algorithms on a realistic diffusion MR phantom , 2011, NeuroImage.

[45]  Konstantinos Arfanakis,et al.  Enhanced ICBM diffusion tensor template of the human brain , 2011, NeuroImage.

[46]  Jacques-Donald Tournier,et al.  Diffusion tensor imaging and beyond , 2011, Magnetic resonance in medicine.

[47]  Max A. Viergever,et al.  A comparison of MR based segmentation methods for measuring brain atrophy progression , 2011, NeuroImage.

[48]  Daniel L. Polders,et al.  Signal to noise ratio and uncertainty in diffusion tensor imaging at 1.5, 3.0, and 7.0 Tesla , 2011, Journal of magnetic resonance imaging : JMRI.

[49]  W. Baaré,et al.  An ex vivo imaging pipeline for producing high‐quality and high‐resolution diffusion‐weighted imaging datasets , 2011, Human brain mapping.

[50]  Raghu Machiraju,et al.  Susceptibility Distortion Correction for Echo Planar Images with Non-uniform B-Spline Grid Sampling: A Diffusion Tensor Image Study , 2011, MICCAI.

[51]  Derek K. Jones,et al.  How and how not to correct for CSF-contamination in diffusion MRI , 2012, NeuroImage.

[52]  J. Modersitzki,et al.  Diffeomorphic susceptibility artifact correction of diffusion-weighted magnetic resonance images , 2012, Physics in medicine and biology.

[53]  Daniel C. Alexander,et al.  NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain , 2012, NeuroImage.

[54]  Carlo Pierpaoli,et al.  Effects of image distortions originating from susceptibility variations and concomitant fields on diffusion MRI tractography results , 2012, NeuroImage.

[55]  Derek K. Jones,et al.  Motion correction and registration of high b‐value diffusion weighted images , 2012, Magnetic resonance in medicine.

[56]  Daniel C. Alexander,et al.  Structure Tensor Informed Fiber Tractography (STIFT) by combining gradient echo MRI and diffusion weighted imaging , 2012, NeuroImage.

[57]  A. Leemans,et al.  Comprehensive framework for accurate diffusion MRI parameter estimation , 2013, Magnetic resonance in medicine.

[58]  Jan Sijbers,et al.  Weighted linear least squares estimation of diffusion MRI parameters: Strengths, limitations, and pitfalls , 2013, NeuroImage.

[59]  Essa Yacoub,et al.  The WU-Minn Human Connectome Project: An overview , 2013, NeuroImage.

[60]  M. Viergever,et al.  Multi-Fiber Tractography Visualizations for Diffusion MRI Data , 2013, PloS one.

[61]  Alan Connelly,et al.  SIFT: Spherical-deconvolution informed filtering of tractograms , 2013, NeuroImage.

[62]  A. Leemans,et al.  4395 In vivo investigations of accuracy and precision of fiber orientations in crossing fibers in spherical deconvolution-based HARDI methods , 2013 .

[63]  A. Leemans,et al.  2505 MASSIVE : Multiple Acquisitions for Standardization of Structural Imaging Validation and Evaluation , 2013 .

[64]  R. Deriche,et al.  Diffusion MRI : Theory , Methods , and Applications , 2011 , 767 pages , 2013 .

[65]  Derek K. Jones,et al.  Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging , 2013, Human brain mapping.

[66]  I. Koerte,et al.  Diffusion Tensor Imaging , 2014 .

[67]  Y. Assaf,et al.  Improved precision in CHARMED assessment of white matter through sampling scheme optimization and model parsimony testing , 2014, Magnetic resonance in medicine.

[68]  Bram Stieltjes,et al.  Fiberfox: Facilitating the creation of realistic white matter software phantoms , 2014, Magnetic resonance in medicine.

[69]  Bram Stieltjes,et al.  Investigation of resolution effects using a specialized diffusion tensor phantom , 2014, Magnetic resonance in medicine.

[70]  Max A. Viergever,et al.  Recursive calibration of the fiber response function for spherical deconvolution of diffusion MRI data , 2014, NeuroImage.

[71]  S. Vos,et al.  Reliability of brain volume measurements: A test-retest dataset , 2014, Scientific Data.

[72]  Anna Varentsova,et al.  Development of a high angular resolution diffusion imaging human brain template , 2014, NeuroImage.

[73]  A. Alexander,et al.  Free water elimination diffusion tractography: A comparison with conventional and fluid-attenuated inversion recovery, diffusion tensor imaging acquisitions. , 2015, Journal of magnetic resonance imaging : JMRI.

[74]  Evan M. Gordon,et al.  Long-term neural and physiological phenotyping of a single human , 2015, Nature Communications.

[75]  Ali R. Khan,et al.  The DTI Challenge: Toward Standardized Evaluation of Diffusion Tensor Imaging Tractography for Neurosurgery , 2015, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

[76]  Yundi Shi,et al.  A diffusion tensor MRI atlas of the postmortem rhesus macaque brain , 2015, NeuroImage.

[77]  Aleksandra Pizurica,et al.  The effect of Gibbs ringing artifacts on measures derived from diffusion MRI , 2015, NeuroImage.

[78]  Patrick T. Hickey,et al.  Postmortem diffusion MRI of the human brainstem and thalamus for deep brain stimulator electrode localization , 2015, Human brain mapping.

[79]  Yu-Chun Lo,et al.  NTU‐DSI‐122: A diffusion spectrum imaging template with high anatomical matching to the ICBM‐152 space , 2015, Human brain mapping.

[80]  A. Alexander,et al.  Free water elimination diffusion tractography: A comparison with conventional and fluid‐attenuated inversion recovery, diffusion tensor imaging acquisitions , 2015, Journal of magnetic resonance imaging : JMRI.

[81]  Jessica A. Turner,et al.  Sharing the wealth: Neuroimaging data repositories , 2016, NeuroImage.

[82]  Sjoerd B. Vos,et al.  Checking and Correcting DTI Data , 2016 .

[83]  Max A. Viergever,et al.  Trade-off between angular and spatial resolutions in in vivo fiber tractography , 2016, NeuroImage.

[84]  Joseph A. Helpern,et al.  Fiber ball imaging , 2016, NeuroImage.

[85]  Wim Van Hecke,et al.  Diffusion tensor imaging : a practical handbook , 2016 .

[86]  Robert Oostenveld,et al.  ConnectomeDB—Sharing human brain connectivity data , 2016, NeuroImage.

[87]  Jelle Veraart,et al.  In vivo quantification of demyelination and recovery using compartment-specific diffusion MRI metrics validated by electron microscopy , 2016, NeuroImage.

[88]  Max C. Keuken,et al.  The Age-ility Project (Phase 1): Structural and functional imaging and electrophysiological data repository , 2016, NeuroImage.

[89]  Stamatios N. Sotiropoulos,et al.  An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging , 2016, NeuroImage.

[90]  Stefan Klein,et al.  PCA-based groupwise image registration for quantitative MRI , 2016, Medical Image Anal..

[91]  Bruce R. Rosen,et al.  MGH–USC Human Connectome Project datasets with ultra-high b-value diffusion MRI , 2016, NeuroImage.

[92]  Carlo Pierpaoli,et al.  The diffusion tensor imaging (DTI) component of the NIH MRI study of normal brain development (PedsDTI) , 2016, NeuroImage.

[93]  Sebastien Ourselin,et al.  The importance of correcting for signal drift in diffusion MRI , 2017, Magnetic resonance in medicine.