Compressed Sensing Diffusion Spectrum Imaging for Accelerated Diffusion Microstructure MRI in Long-Term Population Imaging

Mapping non-invasively the complex microstructural architecture of the living human brain, diffusion magnetic resonance imaging (dMRI) is one of the core imaging modalities in current population studies. For the application in longitudinal population imaging, the dMRI protocol should deliver reliable data with maximum potential for future analysis. With the recent introduction of novel MRI hardware, advanced dMRI acquisition strategies can be applied within reasonable scan time. In this work we conducted a pilot study based on the requirements for high resolution dMRI in a long-term and high throughput population study. The key question was: can diffusion spectrum imaging accelerated by compressed sensing theory (CS-DSI) be used as an advanced imaging protocol for microstructure dMRI in a long-term population imaging study? As a minimum requirement we expected a high level of agreement of several diffusion metrics derived from both CS-DSI and a 3-shell high angular resolution diffusion imaging (HARDI) acquisition, an established imaging strategy used in other population studies. A wide spectrum of state-of-the-art diffusion processing and analysis techniques was applied to the pilot study data including quantitative diffusion and microstructural parameter mapping, fiber orientation estimation and white matter fiber tracking. When considering diffusion weighted images up to the same maximum diffusion weighting for both protocols, group analysis across 20 subjects indicates that CS-DSI performs comparable to 3-shell HARDI in the estimation of diffusion and microstructural parameters. Further, both protocols provide similar results in the estimation of fiber orientations and for local fiber tracking. CS-DSI provides high radial resolution while maintaining high angular resolution and it is well-suited for analysis strategies that require high b-value acquisitions, such as CHARMED modeling and biomarkers from the diffusion propagator.

[1]  Fang-Cheng Yeh,et al.  Converting Multi-Shell and Diffusion Spectrum Imaging to High Angular Resolution Diffusion Imaging , 2016, Front. Neurosci..

[2]  Thomas Schultz,et al.  Versatile, robust, and efficient tractography with constrained higher-order tensor fODFs , 2017, International Journal of Computer Assisted Radiology and Surgery.

[3]  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.

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

[5]  Arthur W. Toga,et al.  Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template , 2008, NeuroImage.

[6]  Steen Moeller,et al.  Advances in diffusion MRI acquisition and processing in the Human Connectome Project , 2013, NeuroImage.

[7]  V. Vovk,et al.  Combining P-Values Via Averaging , 2012, Biometrika.

[8]  Rachid Deriche,et al.  MAPL: Tissue microstructure estimation using Laplacian-regularized MAP-MRI and its application to HCP data , 2016, NeuroImage.

[9]  D. Altman,et al.  Measurement error. , 1996, BMJ.

[10]  Timothy Edward John Behrens,et al.  Characterization and propagation of uncertainty in diffusion‐weighted MR imaging , 2003, Magnetic resonance in medicine.

[11]  Oliver Bieri,et al.  Assessing White Matter Microstructure in Brain Regions with Different Myelin Architecture Using MRI , 2016, PloS one.

[12]  Alan Connelly,et al.  Quantification of track-weighted imaging (TWI): Characterisation of within-subject reproducibility and between-subject variability , 2014, NeuroImage.

[13]  N. Toschi,et al.  Differences in Gaussian diffusion tensor imaging and non-Gaussian diffusion kurtosis imaging model-based estimates of diffusion tensor invariants in the human brain. , 2016, Medical physics.

[14]  Jieping Ye,et al.  A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems , 2013, ICML.

[15]  Derek K. Jones,et al.  Using the biophysical CHARMED model to elucidate the underpinnings of contrast in diffusional kurtosis analysis of diffusion-weighted MRI , 2011, Magnetic Resonance Materials in Physics, Biology and Medicine.

[16]  Rainer Goebel,et al.  Robust and fast nonlinear optimization of diffusion MRI microstructure models , 2017, NeuroImage.

[17]  Jean-Philippe Thiran,et al.  A Connectome-Based Comparison of Diffusion MRI Schemes , 2013, PloS one.

[18]  Julien Cohen-Adad,et al.  Pushing the limits of in vivo diffusion MRI for the Human Connectome Project , 2013, NeuroImage.

[19]  Wiro J. Niessen,et al.  Tract-specific white matter degeneration in aging: The Rotterdam Study , 2015, Alzheimer's & Dementia.

[20]  Christopher J Hardy,et al.  Bias and precision analysis of diffusional kurtosis imaging for different acquisition schemes , 2016, Magnetic resonance in medicine.

[21]  Rüdiger Stirnberg,et al.  Two-dimensional accelerated MP-RAGE imaging with flexible linear reordering , 2014, Magnetic Resonance Materials in Physics, Biology and Medicine.

[22]  Eric Achten,et al.  Optimal Experimental Design for Diffusion Kurtosis Imaging , 2010, IEEE Transactions on Medical Imaging.

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

[24]  Stefan Klein,et al.  Improving alignment in Tract-based spatial statistics: Evaluation and optimization of image registration , 2013, NeuroImage.

[25]  A. Connelly,et al.  Determination of the appropriate b value and number of gradient directions for high‐angular‐resolution diffusion‐weighted imaging , 2013, NMR in biomedicine.

[26]  Andrew L. Alexander,et al.  Computation of Diffusion Function Measures in $q$ -Space Using Magnetic Resonance Hybrid Diffusion Imaging , 2008, IEEE Transactions on Medical Imaging.

[27]  J. Veraart,et al.  Universal power-law scaling of water diffusion in human brain defines what we see with MRI , 2016, 1609.09145.

[28]  Stamatios N. Sotiropoulos,et al.  Non-parametric representation and prediction of single- and multi-shell diffusion-weighted MRI data using Gaussian processes , 2015, NeuroImage.

[29]  P. Matthews,et al.  Multimodal population brain imaging in the UK Biobank prospective epidemiological study , 2016, Nature Neuroscience.

[30]  Thomas Dierks,et al.  Microstructure and Cerebral Blood Flow within White Matter of the Human Brain: A TBSS Analysis , 2016, PloS one.

[31]  Steen Moeller,et al.  The Human Connectome Project's neuroimaging approach , 2016, Nature Neuroscience.

[32]  Steen Moeller,et al.  Evaluation of slice accelerations using multiband echo planar imaging at 3T , 2013, NeuroImage.

[33]  Mark W. Woolrich,et al.  Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? , 2007, NeuroImage.

[34]  P. Ellen Grant,et al.  Multi-shell diffusion signal recovery from sparse measurements , 2014, Medical Image Anal..

[35]  N. Makris,et al.  High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity , 2002, Magnetic resonance in medicine.

[36]  Julien Cohen-Adad,et al.  The Human Connectome Project and beyond: Initial applications of 300mT/m gradients , 2013, NeuroImage.

[37]  John S. Duncan,et al.  Identical, but not the same: Intra-site and inter-site reproducibility of fractional anisotropy measures on two 3.0 T scanners , 2010, NeuroImage.

[38]  A. Anderson.  Measurement of fiber orientation distributions using high angular resolution diffusion imaging , 2005, Magnetic resonance in medicine.

[39]  Essa Yacoub,et al.  Generalized Constant Solid Angle ODF and Optimal Acquisition Protocol for Fiber Orientation Mapping , 2012 .

[40]  P. Basser,et al.  MR diffusion tensor spectroscopy and imaging. , 1994, Biophysical journal.

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

[42]  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.

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

[44]  R. Deriche,et al.  Design of multishell sampling schemes with uniform coverage in diffusion MRI , 2013, Magnetic resonance in medicine.

[45]  Kawin Setsompop,et al.  Interslice leakage artifact reduction technique for simultaneous multislice acquisitions , 2014, Magnetic resonance in medicine.

[46]  A. Hofman,et al.  Cerebral white matter lesions and cognitive function: The Rotterdam scan study , 2000, Annals of neurology.

[47]  Derek K. Jones,et al.  Spatial and orientational heterogeneity in the statistical sensitivity of skeleton-based analyses of diffusion tensor MR imaging data , 2011, Journal of Neuroscience Methods.

[48]  Rachid Deriche,et al.  Sparse Reconstruction Challenge for diffusion MRI: Validation on a physical phantom to determine which acquisition scheme and analysis method to use? , 2015, Medical Image Anal..

[49]  J. Bartko.  Corrective Note to: “The Intraclass Correlation Coefficient as a Measure of Reliability” , 1974 .

[50]  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.

[51]  Andrew L. Alexander,et al.  Hybrid diffusion imaging , 2007, NeuroImage.

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

[53]  Daniel Rueckert,et al.  Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data , 2006, NeuroImage.

[54]  C. Hardy,et al.  Accelerated diffusion spectrum imaging in the human brain using compressed sensing , 2011, Magnetic resonance in medicine.

[55]  Rachid Deriche,et al.  Compressed Sensing for Accelerated EAP Recovery in Diffusion MRI , 2010, MICCAI 2010.

[56]  Chunshui Yu,et al.  Performances of diffusion kurtosis imaging and diffusion tensor imaging in detecting white matter abnormality in schizophrenia , 2014, NeuroImage: Clinical.

[57]  A. Hofman,et al.  Cerebral white matter lesions, vascular risk factors, and cognitive function in a population‐based study , 1994, Neurology.

[58]  Gareth J. Barker,et al.  Optimal imaging parameters for fiber-orientation estimation in diffusion MRI , 2005, NeuroImage.

[59]  Tobias Kober,et al.  Head motion detection using FID navigators , 2011, Magnetic resonance in medicine.

[60]  Julien Cohen-Adad,et al.  Accelerated diffusion spectrum imaging with compressed sensing using adaptive dictionaries , 2012, MICCAI.

[61]  J. Sijbers,et al.  More accurate estimation of diffusion tensor parameters using diffusion kurtosis imaging , 2011, Magnetic resonance in medicine.

[62]  Derek K. Jones,et al.  Why diffusion tensor MRI does well only some of the time: Variance and covariance of white matter tissue microstructure attributes in the living human brain☆ , 2014, NeuroImage.

[63]  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.

[64]  J. Polimeni,et al.  Blipped‐controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g‐factor penalty , 2012, Magnetic resonance in medicine.

[65]  P. Grenier,et al.  MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. , 1986, Radiology.

[66]  Carlo Pierpaoli,et al.  Mean apparent propagator (MAP) MRI: A novel diffusion imaging method for mapping tissue microstructure , 2013, NeuroImage.

[67]  A. Hofman,et al.  The Rotterdam Scan Study: design update 2016 and main findings , 2015, European Journal of Epidemiology.

[68]  Tobias Kober,et al.  Prospective and retrospective motion correction in diffusion magnetic resonance imaging of the human brain , 2012, NeuroImage.

[69]  Timothy E. J. Behrens,et al.  Human connectomics , 2012, Current Opinion in Neurobiology.

[70]  Jan Sijbers,et al.  Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data , 2014, NeuroImage.

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

[72]  Maxime Descoteaux,et al.  Optimal DSI reconstruction parameter recommendations: Better ODFs and better connectivity , 2016, NeuroImage.

[73]  Klaus H. Maier-Hein,et al.  Methodological considerations on tract-based spatial statistics (TBSS) , 2014, NeuroImage.

[74]  Steen Moeller,et al.  The Human Connectome Project: A data acquisition perspective , 2012, NeuroImage.

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

[76]  R. Deriche,et al.  Comparison of sampling strategies and sparsifying transforms to improve compressed sensing diffusion spectrum imaging , 2015, Magnetic resonance in medicine.

[77]  Michael W. Weiner,et al.  2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception , 2015, Alzheimer's & Dementia.

[78]  Stephen M. Smith,et al.  Permutation inference for the general linear model , 2014, NeuroImage.

[79]  Carlo Pierpaoli,et al.  Clinical feasibility of using mean apparent propagator (MAP) MRI to characterize brain tissue microstructure , 2016, NeuroImage.

[80]  Yogesh Rathi,et al.  Spatially Regularized Compressed Sensing for High Angular Resolution Diffusion Imaging , 2011, IEEE Transactions on Medical Imaging.

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

[82]  Jan Sijbers,et al.  Denoising of diffusion MRI using random matrix theory , 2016, NeuroImage.

[83]  P. Basser,et al.  Simple harmonic oscillator based reconstruction and estimation for three-dimensional q-space MRI , 2009 .

[84]  Maxime Descoteaux,et al.  Dipy, a library for the analysis of diffusion MRI data , 2014, Front. Neuroinform..

[85]  Thomas Schultz,et al.  Learning a Reliable Estimate of the Number of Fiber Directions in Diffusion MRI , 2012, MICCAI.

[86]  Timothy E. J. Behrens,et al.  Measuring macroscopic brain connections in vivo , 2015, Nature Neuroscience.